Efficient prediction of impact for the proposed actions using common resources in an intent management function

Abstract

Methods and systems are described for intent management functions (IMF) that allow proposal agents to replace prediction agents to predict the effect of one or more actions on a limited set of resources. Common resources (CRs) are introduced as a layer of abstraction to the intent handling process, where CRs are defined as the resource abstraction and configuration parameters determining how the physical resources of a domain are shared among consumers. Actions from proposal agents might only be investigated if they change common resources. In some cases prediction of all KPIs is considered only based on the changes in common resources, and effect of actions on other KPIs, which does not affect common resources, are neglected.

Description

EFFICIENT PREDICTION OF IMPACT FOR THE PROPOSED ACTIONS USINGCOMMON RESOURCES IN AN INTENT MANAGEMENT FUNCTIONTECHNICAL FIELD

[0001] The present disclosure generally relates to systems and methods for predicting impacts on a system .BACKGROUND

[0002] A fundamental element of a Cognitive Network (CN) is the Intent Management Function (IMF) which should be able to manage one or multiple closed-loops, and a closed-loop consists of several modules with different responsibilities. Different closed loops can be allocated to different services, and each closed loop is expected to manage its associated service or Key Performance Indicator (KPI) autonomously.

[0003] The Intent Management Function (IMF) provides a zero-touch control for an environment. An IMF use case is shown in Figure 1. The intent manager 10 is controlled by one or more intents 5 from one or more operators 7 and controls one or more environments 15. Controlling an environment is done by observing the environment, reasoning around the combination of perceived situation and prior knowledge and taking actions on the environment. Note that these steps together form a closed loop. The overall purpose of the intent manager is to fulfill the intent. Figure 1 is based on: Stuart J. Russel, Peter Norvig 2003: “Artificial Intelligence, A Modern Approach” (2013).

[0004] Figure 2 illustrates an IMF 200 with different modules. One or more intents 205 are sent to the IMF framework 210. Each expectation in an intent 205 becomes a KPI that needs to be met. These KPIs are called target KPIs. Raw data that describes the state of the managed system is exposed from the environment and processed by data grounding agents 215. These data grounding agents 215 translate the raw data into measured KPIs. Target and measured KPIs can be compared, and the difference can become an issue or goal that the IMF 200 needs to meet. For example, the target KPI is “max 20 msec latency” but the measured KPI is “30 msec latency”. One or more proposal agents 220 are responsible for proposing actions that would solvean issue. The prediction agent 230 estimates the effect of proposals on the system state before the proposed actions are recommended and executed in the managed environment. The prediction agent 230 does this on all active expectations. The evaluation agent 240 receives the estimated effect of all proposed actions in all active expectations and selects the best action in terms of global network utility or cost. Finally, actuator agents 250 execute the action on the environment under control.

[0005] There currently exist certain challenges. In current IMFs, the prediction agents predict the effect of the action(s) on all KPIs the IMF is responsible for. For example, one core mechanism of conflict handling (prediction and evaluation) in an IMF is described in W02023 / 200412, titled “Intent Handling.” With IMFs, the impact of each proposed action on every other KPIs may need to be predicted. As there can be many KPIs, relationships between proposed actions and affected KPIs will build a large matrix. Hence, this is not always feasible to predict precisely what will be the effect of actions on all possible impacted KPIs in the network. This will lead to inaccurate prediction and inefficient and costly process.SUMMARY

[0006] One embodiment under the present disclosure comprises a method performed by an IMF for predicting one or more impacts of performing one or more proposed actions on a system. The method comprises receiving one or more intents, each of the one or more intents associated with one or more desired KPIs; detecting one or more actual KPIs in the system; comparing the one or more actual KPIs and the one or more desired KPIs; and proposing one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes in one or more configuration parameters, the one or more configuration parameters determining how one or more common resources are shared among one or more different services. The method further includes detecting that at least one action of the one or more actions impacts the one or more common resources; predicting one or more impacts of the at least one action for the one or more actual KPIs; and selecting at least one action of the one or more actions, based at least in part on the one or more impacts.

[0007] Another embodiment under the present disclosure comprises a system for evaluating and actuating one or more proposed actions in a system, wherein the system is operable to provide one or more services. The system comprises an IMF configured to receive one or moreintents, each of the one more intents associated with one or more desired KPIs; and compare one or more actual KPIs and the one or more desired KPIs. It further comprises a knowledge base configured to store one or more data related to the system and detect if one or more actions impact one or more common resources, and further configured to ignore any of the one or more actions that do not impact the one or more common resources; a reasoner configured to assist one or more evaluation agents; and one or more data grounding agents configured to detect the one or more actual KPIs in the system. The system also comprises one or more proposal agents configured to propose the one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes in one or more configuration parameters, the one or more configuration parameters determining how one or more common resources are shared among the one or more services; one or more prediction agents configured to predict one or more impacts on the one or more actual KPIs of the one or more actions if the one or more actions impact the one or more common resources; and the one or more evaluation agents configured to select at least one action of the one or more actions, based at least in part on the one or more impacts.

[0008] Another embodiment under the present disclosure is a method performed by a IMF for predicting impact of performing one or more proposed actions on a system, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources. The method comprises comparing one or more actual KPIs, in the system and one or more desired KPIs; proposing, by a first one or more proposal agents, one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources; predicting, by second one or more proposal agents, one or more expected KPIs based at least in part on the one or more changes; and selecting, by one or more evaluation agents, at least one action of the one or more actions, based at least in part on the one or more changes and the one or more expected KPIs.

[0009] Another embodiment under the present disclosure comprises a IMF for predicting impact of performing one or more proposed actions on a system. The IMF comprises the IMF configured to compare one or more actual KPIs in the system and one or more desired KPIs, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources. It further comprises one or more proposal agents configured to propose one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more commonresources; and update one or more expected KPIs based at least in part on the one or more changes. The IMF further comprises one or more evaluation agents configured to select at least one action of the one or more actions, based at least in part on the one or more changes and the one or more expected KPIs.

[0010] Another embodiment under the present disclosure comprises a method performed by an IMF for predicting impact of performing one or more proposed actions on a system, wherein a first subgroup of proposal agents are not aware of one or more common resources and a second subgroup of proposal agents are aware of the one or more common resources. The method comprises comparing one or more actual KPIs, in the system and one or more desired KPIs; proposing, by the one or more proposal agents, one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to one or more common resources, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising the one or more common resources; providing, by one or more prediction agents, one or more mappings for implementing the one or more changes to the first subgroup of proposal agents; and predicting, by the first subgroup of proposal agents, a first one or more expected KPIs based at least in part on the one or more mappings.

[0011] A further embodiment under the present disclosure comprises an IMF for predicting impact of performing one or more proposed actions on a system, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources. The IMF comprises one or more proposal agents configured to compare one or more actual key performance indicators, KPIs, in the system and one or more desired KPIs; and propose one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources. The IMF further includes a first subgroup of proposal agents that are not aware of the one or more common resources; and one or more prediction agents configured to provide one or more mappings for implementing the one or more changes to the first subgroup of proposal agents, wherein the first subgroup of proposal agents are configured to update a first one or more expected KPIs based at least in part on the one or more mappings. The IMF further comprises a second subgroup of proposal agents that are aware of the one or more common resources and are configured to update a second one or more expected KPIs based at least in part on the one or morechanges; and one or more evaluation agents configured to select at least one action of the one or more actions, based at least in part on the one or more changes and the first and second one or more expected KPIs.

[0012] Another embodiment under the present disclosure comprises a method performed by an IMF for predicting impact of performing one or more proposed actions on a system, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources, wherein a first one or more proposal agents are configured to use the one or more common resources and a second one or more proposal agents are not configured to use the one or more common resources, wherein a first subgroup of the first one or more proposal agents are not aware of the one or more common resources and a second subgroup of the first one or more proposal agents are aware of the one or more common resources. The method comprises comparing one or more actual KPIs in the system and one or more desired KPIs; proposing, by at least one proposal agent of the first one or more proposal agents, one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources; and triggering, by a reasoner, the others of the first one or more proposal agents to predict a first one or more expected KPIs in the light of the one or more changes. The method further comprises providing, by one or more prediction agents, one or more mappings for implementing the one or more changes to the first subgroup of proposal agents; predicting, by the first subgroup of proposal agents, a second one or more expected KPIs based at least in part on the one or more mappings; predicting, by the second subgroup of proposal agents, a third one or more expected KPIs based at least in part on the one or more changes; and selecting, by one or more evaluation agents, at least one action of the one or more actions, based at least in part on the one or more changes and the first and second one or more expected KPIs.

[0013] Another embodiment under the present disclosure comprises an IMF for predicting impact of performing one or more proposed actions on a system. The IMF is configured to compare one or more actual KPIs in the system and one or more desired KPIs. It further includes at least one proposal agent of a first one or more proposal agents, wherein the first one or more proposal agents are configured to use one or more common resources and a second one or more proposal agents are not configured to use the one or more common resources, wherein a first subgroup of the first one or more proposal agents are not aware of the one or more commonresources and a second subgroup of the first one or more proposal agents are aware of the one or more common resources, configured to; propose one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising the one or more common resources. It further comprises a reasoner configured to trigger the others of the first one or more proposal agents to predict their one or more expected KPIs in the light of the one or more changes. The IMF also comprises one or more prediction agents configured to provide one or more mappings for implementing the one or more changes to the first subgroup of proposal agents, wherein the first subgroup of proposal agents are configured to update a first one or more expected KPIs based at least in part on the one or more mappings, and wherein the second subgroup of proposal agents are configured to update a second one or more expected KPIs based at least in part on the one or more changes. The IMF also includes one or more evaluation agents configured to select at least one action of the one or more actions, based at least in part on the one or more changes and the first and second one or more expected KPIs.

[0014] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an indication of the scope of the claimed subject matter.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0016] Fig. 1 illustrates a use case for IMF and deployment in a zero-touch environment;

[0017] Fig. 2 illustrates an embodiment of an IMF with different modules;

[0018] Fig. 3 illustrates aspects of multiple actions and a limited set of common resources;

[0019] Fig. 4 illustrates an embodiment of an IMF;

[0020] Fig. 5 illustrates an embodiment of an IMF under the present disclosure;

[0021] Fig. 6 illustrates an embodiment of an IMF under the present disclosure;

[0022] Fig. 7 illustrates an embodiment of an IMF under the present disclosure;

[0023] Fig. 8 illustrates an embodiment of an IMF under the present disclosure;

[0024] Fig. 9 illustrates an embodiment of an IMF under the present disclosure;

[0025] Fig. 10 illustrates a flow-chart of a method embodiment under the present disclosure;

[0026] Fig. 11 illustrates a flow-chart of a method embodiment under the present disclosure;

[0027] Fig. 12 illustrates a flow-chart of a method embodiment under the present disclosure;

[0028] Fig. 13 illustrates a flow-chart of a method embodiment under the present disclosure;

[0029] Fig. 14 illustrates a flow-chart of a method embodiment under the present disclosure;

[0030] Fig. 15 shows a schematic of a communication system embodiment under the present disclosure;

[0031] Fig. 16 shows a schematic of a user equipment embodiment under the present disclosure;

[0032] Fig. 17 shows a schematic of a network node embodiment under the present disclosure; and

[0033] Fig. 18 shows a schematic of a virtualization environment embodiment under the present disclosure.DETAILED DESCRIPTION

[0034] Before describing various embodiments of the present disclosure in detail, it is to be understood that this disclosure is not limited to the parameters of the particularly exemplified systems, methods, apparatus, products, processes, and / or kits, which may, of course, vary. Thus, while certain embodiments of the present disclosure will be described in detail, with reference to specific configurations, parameters, components, elements, etc., the descriptions are illustrative and are not to be construed as limiting the scope of the claimed embodiments. Inaddition, the terminology used herein is for the purpose of describing the embodiments and is not necessarily intended to limit the scope of the claimed embodiments.

[0035] As described above, there currently exist certain challenges. For example, when implementing an IMF, it may be that the impact of each proposed action on every other KPIs should be predicted. As there can be many KPIs, relationships between proposed actions and affected KPIs will build a large matrix. Hence, this is not always feasible to predict precisely what will be the effect of actions on all possible impacted KPIs in the network, possibly leading to inaccurate prediction and inefficient and costly process.

[0036] The current disclosure aims to address these challenges, including:• With large numbers of combinations of actions-KPIs it is not always feasible to evaluate and perform an accurate prediction of the state of the impacted KPIs. Inaccurate / incorrect predictions can result in network degradation because of invalid data which are sent to evaluation agent that in turn may increase the probability of conflict among proposed actions to be actuated. Even if accurate predictions can be made, the amount of computation effort needed to perform the predictions may be high.• Another problem present in the state-of-the-art is that every time an intent is added, and removed or in some cases, when changed, the set of KPIs to be predicted changes, leading to cases where sometimes the prediction agents have to be updated.• Yet another problem is that when the capability of a new KPI is introduced (e.g., the IMF is prepared to accept an intent with a new, previously unknown KPI), or there are new actions introduced, which the IMF can take. In this case, new prediction agents need to be deployed or the existing ones updated.

[0037] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. The current disclosure introduces systems and methods that can allow the prediction agent to predict the effect of the action(s) on a limited set of resources.

[0038] A generic prediction mechanism that predicts the effect of an action on all KPIs (all expectations’ fulfillment) is very hard. To make the design of the prediction phase more manageable, prediction agents preferably predict the effect of an action based on a limited set of resources. What these limited set of resources are, depends on the environment under control forthe IMF. In this disclosure this limited set of resources is referred to as Common Resources (CRs) which are defined as physical resources made available for use by multiple services via sharing, virtualization, and slicing. These can be e.g., CPU, memory, storage, bandwidth, HW accelerator, transmit power, cost as a budget, etc.

[0039] CRs are introduced as a layer of abstraction to the intent handling process, where CRs are defined as the resource abstraction and configuration parameters determining how the physical resources of a domain (radio, core, transport, ... ) are shared among consumers (e.g., different services). CRs are like the narrow waist of the Internet Architecture.

[0040] Actions from proposal agents are only investigated if they change common resources and the effect of actions are only investigated indirectly via their changes in common resource allocations. Therefore, prediction of all KPIs is considered only based on the changes in common resources. Effect of actions on other KPIs, which does not affect common resources, are neglected.

[0041] Additionally, methods and systems are proposed for how proposal agents can substitute prediction agents. Consider if KPI prediction can be done based on changes in common resources, then proposal agents, who normally propose actions as common resource changes based on issues they can solve, typically their KPI target differences (KPI to common resource mapping), then those proposal agents can be (easily) updated to predict their anticipated KPI change by a given common resource change (common resource to KPI mapping). Such enrichments to the specific proposal agents would eliminate the need for dedicated prediction agents, who needs to map from actions to KPIs.

[0042] Methods and systems are also proposed for harmonizing among different level of common resource awareness across proposal agents by a simplified prediction agent, whose task would be e.g., i) to map actions on sub-common resources to common resource changes (sub-common resources to common resource) or ii) to map common resource changes to subcommon resource changes (common resource to sub-common resource) understood by a proposal agent, where sub-common resource is a subset of the domain’s common resource. For example, consider a priority scheduler and some per queue packet drop policy proposal agents only aware of packet drop policy (bind to a specific queue), does not necessarily understand changes in the priority queuing, for those proposal agents the per queue effect of changes must be derived. In this example the scheduling priority and the queue packet drop threshold are common resourcesdetermining transmission rate, imagine a proposal agent which is only aware of the queue packet drop threshold, hence understands only a subset of the common resources. For this agent the sum of priority adjustments may have to be translated to the queue which the agent oversees to adjust the drop priority.

[0043] Compound Actions are also described. Compound Action can involve e.g., when proposal agents respond with KPI change and counter action, then the evaluation logic can accept the genuine action and bundle it with other pro-active actions, i.e., actions on anticipated KPI degradations.

[0044] As set forth further below, this disclosure includes descriptions of the inner working of the cognitive loop and the different agents’ role in the IMF when the prediction is limited to a common set of resources.

[0045] Certain embodiments may provide one or more of the following technical advantages.• Common resources to IMF are like a narrow waist to the Internet. New actions from new proposal agent can be introduced without changes in the prediction and evaluation functions.• If a proposal agent by design is capable of quantifying (approximately) its common resource needs to achieve its KPI, then, it can also tell if given available resources would meet its KPI needs or not. Unlike random choice or directional only (increase / decrease) proposal agents, the more intelligent a proposal agent is with respect to quantifying its needs the easier to adapt a proposal agent to be able to predict its KPI based on available resources. Also, the domain of CRs, which is very limited, is mapped to the domain of actions, which is much larger. This mapping can reduce the complexity of predicting the impact of the proposed actions on the KPIs. It is like a dimension reduction. In Figure 3, there may be many actions impacting the same type of CR (i.e., bandwidth). If one can develop a model based on bandwidth, then the complexity can be reduced under the development of such prediction model.• The impact of an action is manifested via its change on common resources, i.e., prediction does not need to understand all possible actions but only a thinner set of actions affecting common resources.• Disclosed embodiments can be applicable whether the prediction agent is per KPI or per action.• Disclosed embodiments can provide simplification compared to existing prediction agent due to assumed existing similar functionality in proposal agents.• One promising practical use case for disclosed embodiments is in autonomous cellular networks• Generalization: as CR is same in every domain, it is easier to achieve generalization. For example, taking same amount of bandwidth for the same service can yield similar effect under different topologies.

[0046] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0047] Figure 4 describes existing system and methods (similar to Figure 2). IMF 500 has an intent manager framework 510 with a knowledge base 514 and a reasoner 512. IMF 500 further comprises data grounding agent(s) 520, proposal agent(s) 530, prediction agent(s) 540, evaluation agent(s) 550, and actuation agent(s) 560. The proposal agents 530 are responsible for delivering and assuring services with certain KPI requirements (expectations). Proposal agents 530 receive issues which can be identified as KPI deviances (i.e. the current value of a KPI / or a set of KPI is not meeting the target) and try to correct their service KPI(s) by proposing an action / actions. Prediction agents 540 then estimate the effect of the proposed actions on other KPIs the IMF 500 is responsible for. The prediction agent 540 does this on all active KPIs. The evaluation agent 550 receives the estimated effect of all proposed actions in all active KPIs and selects the best action in terms of intent manager utility or cost. The decided action(s) are passed to the actuation agents 560, that are responsible for implementing the action on the underlying network.

[0048] Figure 5 describes the notion of common resources, or CRs 770. IMF 700 has an intent manager framework 710 with a knowledge base 714 and a reasoner 712. IMF 700 further comprises data grounding agent(s) 720, proposal agent(s) 730, prediction agent(s) 740, evaluation agent(s) 750, and actuation agent(s) 760. The actions generated by the proposal agents 730 can be described as changes in configuration parameters in a technology domain. Some of these configuration parameters determine how the physical resources of a domain are sharedamong different services. Accordingly, changes in configuration parameters affect how CRs 770 are shared.

[0049] Figure 6 describes new aspects, including prediction agent functionality, under the present disclosure in a prediction agent system and the proposal agent system. IMF 800 has an intent manager framework 810 with a knowledge base 814 and a reasoner 812. IMF 800 further comprises data grounding agent(s) 820, proposal agent(s) 830, prediction agent(s) 840, evaluation agent(s) 850, and actuation agent(s) 860. A difference from IMF 500 of Figure 4 is that in Figure 6, as described further in the present disclosure, the actions generated by the proposal agents 830 are only investigated if they change the CR allocation. The effect of the actions on KPIs which do not impact CRs are neglected (the dashed line from proposal agent 830 to evaluation agent 850). With this, the need can be eliminated for dedicated prediction agents 830 which map from proposal actions to all KPIs in the managed system.

[0050] Figure 7 describes further systems and methods under the present disclosure in a proposal agent system, where the proposal agents 1030, 1040 can substitute the prediction agent. IMF 1000 has an intent manager framework 1010 with a knowledge base 1014 and a reasoner 1012. IMF 1000 further comprises data grounding agent(s) 1020, proposal agent(s) 1030, 1040, evaluation agent(s) 1050, and actuation agent(s) 1060. Consider a proposal agent 1030, 1040 that can propose actions as changes in CRs, based on an issue they receive. Then these proposal agents 1030, 1040 can be updated to predict their expected KPI change by a change in a CR. In the proposal agent 1040, a mapping is introduced from CRs change (which are subset of the actions) to KPI (prediction). When CR sharing affects the KPIs of other services, their responsive proposal agents 1030, 1040 performing the role of the prediction can be consulted. With this, the proposal agent 1030 can also be updated to predict its own KPI besides proposing actions as changes in CRs.

[0051] Figure 8 describes further systems and methods under the present disclosure in a prediction agent 1240 and the IMF 1200. IMF 1200 has an intent manager framework 1210 with a knowledge base 1214 and a reasoner 1212. IMF 1200 further comprises data grounding agent(s) 1220, proposal agent(s) 1230, 1235, 1238, prediction agent(s) 1240, evaluation agent(s) 1250, and actuation agent(s) 1260. In some cases, some proposal agents may not understand the same level of abstraction of CRs (e.g., proposal agent 1238 as shown in Figure 8). In this case, the prediction agent(s) 1240 perform harmonization of the different level of CRs awareness acrossproposal agents 1230, 1235, 1238. These prediction agent(s) 1240 can map CR changes proposed by proposal agents (e.g., 1230 in Figure 8) to sub-common resources changes. To do that, the IMF 1200 is preferably aware of which proposal agents understand the notion of CRs (e.g., proposal agent 1235 in Figure 8) and which proposal agents need harmonization (e.g., proposal agent 1238 in Figure 8).

[0052] Figure 9, in another embodiment under the present disclosure, illustrates recursion based on a number of look aheads. IMF 1400 has an intent manager framework 1410 with a knowledge base 1414 and a reasoner 1412. IMF 1400 further comprises data grounding agent(s) 1420, proposal agent(s) 1430, 1432, 1434, 1436, 1438, prediction agent(s) 1440, evaluation agent(s) 1450, and actuation agent(s) 1460. In Figure 9, when the proposal agent 1430 generates actions as changes in CRs, any other proposal agents (in this case proposal agents 1434, 1436) sharing the CRs are triggered to predict their expected KPIs in the light of changes of CRs. Some proposal agents 1432, 1438 do not share the same CRs and are not triggered. To enable this, the IMF 1400 should be aware of which proposal agents are sharing the same CRs as proposal agent 1430 and which ones are not sharing the same CRs. This can be achieved if the proposal agents 1430, 1432, 1434, 1436, 1438 upload rules to the knowledgebase at agent registration, indicating which common resource changes they need to be triggered. When proposal agents 1434, 1436 are triggered, beside predicting their KPIs, they can also propose actions (e.g., corrective actions, counteractions). The actions from proposal agents 1430, 1434, 1436 are given to the evaluation agent 1450. The evaluation agent 1450 can either select the best action among these actions or it can select a bundle of actions indicating which actions can be executed together. In addition, recursion is shown. After the evaluation agent 1450 selects action(s), corresponding proposal agents (1430, 1434, 1436 in this case) are triggered with the results of the evaluation agents 1450. This might trigger further recursion. Stopping criteria can be defined here based on the number of look-ahead iterations.

[0053] Figure 10 illustrates a method embodiment under the present disclosure. Figure 10 illustrates a method 1600 of making modifications to the proposal agents with traditional algorithms. Steps 1640, 1670, and 1692, among others, helps to illustrates certain benefits of the present disclosure, such as the newly introduced prediction API of the proposal agent. Method 1600 can involve both a proposal API 1605 and a prediction API 1645.

[0054] In the proposal API 1605, the proposal agent receives as input the target KPI defined in the intent expectation and the current KPI of its service at step 1610. It accordingly proposes action as a change in CRs, at 1620. At 1650, the resource allocation is performed. It then calculates its expected (predicted) value of KPI based on the change on common resources, at 1660. At 1680 it is determined if the expected KPI is greater than a target KPI and / or it is determined if resources are exhausted. If the calculated KPI does not meet the target KPI, then another iteration is triggered, returning to 1620. The output of the proposal API is the proposed action at 1690 (as change in CRs) along with its anticipated KPI 1692 if this action is implemented.

[0055] In the prediction API, the proposal agents receive as input the expected changes in their CRs along with their target KPI, at step 1640. As described above, at 1650, resource allocation is performed, based on both the proposed action from the proposal API 1605 and the target KPI and expected CR changes from the prediction API 1645. After calculating expected KPI at 1660, att 1670, the proposal agent(s) predict their KPI changes in the light of common resources change. This can be implemented in the first iteration of the same proposal API 1605. The proposal agent(s) can also generate response actions (e.g., corrective / counteractions) to improve their KPI, at step 1690, as part of developing a proposed action. Because this logic is already there in the proposal API 1605 (where the agent generates an action along with its anticipated KPI), implementing the prediction API 1645 can “reuse” these aspects of the proposal API 1605. Method 1600 can comprise multiple variations and embodiments and / or additional and / or alternative steps.

[0056] In the prediction API 1645, it can be seen that the functionalities of traditional proposal agents are used by the prediction API 1645. This indicates that complexity wise, implementing a prediction API 1645 with the proposal agent can bring great benefits with minimal increase in complexity: it reduces the complexity of developing dedicated prediction agents, and it reuses the functionalities of the proposal agent in existing proposal APIs 1605.

[0057] Various embodiments under the present disclosure discuss possible common resources. Common resources can comprise a variety of resources e.g., bandwidth, computational resources, memory, etc., which are utilized when different services e.g., mobile to mobile services (M2M); 5G services; 6G services; processing power; a variety of measurements within a network; video; sound; or a variety of other functionalities, are invoked.

[0058] There are multiple advances over the prior art included in the present disclosure, including but not limited to the following. One point of novelty includes specifying the concept of common resources and enhancement of the proposal agent to propose actions in means of changes in the common resources. Another is then measuring the changes in the KPIs based on the changes in the common resources. These aspects can eliminate the need for the prediction agent since a proposal agent will have all information with respect to the common resources that will be changed by the proposed actions that can be mapped to the respective KPIs.

[0059] Two new types of simplified prediction agents are also introduced. First, prediction agents to translate actions to changes in common resources, where actions are defined as common resource change actions without explicit indication of common resource changes. Second, prediction agents to predict common resource changes to a subset of common resources.

[0060] Other advances include methods and systems to enhance proposal agents to generate actions in the form of changes on the CRs. Such proposal agents can eliminate the need of prediction agents since the impact of the actions on CRs are already known by the proposal agent. This can be done by maintaining a data structure e.g., graph, hash table, etc., that maps CRs to related KPI values.

[0061] Other advances include methods and systems to introduce new types of prediction agents that map changes in CRs to a subset of CRs. Other advances include enhancement of knowledge base inside the intent management framework (formerly known as Cognitive Core) to support rule-based registration of proposal agent to maintain the information about what CRs are known by what proposal agent. Another advance is the concept of bundle actions where an evaluation agent might choose a set of actions to fulfil an intent. And another advance is a recursive scheme where an evaluation agent will recursively choose actions and examine the action inside an evaluation-prediction-proposal agents loop until a predefined criteria is met.

[0062] There are multiple technological benefits to embodiments under the present disclosure. These include, for example, precise and efficient prediction of the impact of an action on the network parameters / KPIs which will improve the performance of proposal and prediction agents and will increase accuracy in evaluation agent. Finally, more relevant actions will be actuated to fulfil requirements in an intent.Additional Embodiments

[0063] Another possible method embodiment under the present disclosure is shown in Figure 11. Method 1800 comprises a method performed by an IMF for predicting one or more impacts of performing one or more proposed actions on a system. Step 1810 is receiving one or more intents, each of the one more intents associated with one or more desired KPIs. Step 1820 is detecting one or more actual KPIs in the system. Step 1830 is comparing the one or more actual KPIs and the one or more desired KPIs. Step 1840 is proposing one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes in one or more configuration parameters, the one or more configuration parameters determining how one or more common resources are shared among one or more different services. Step 1850 detecting that at least one action of the one or more actions impacts the one or more common resources. Step 1860 is predicting one or more impacts of the at least one action for the one or more actual KPIs. Step 1870 is selecting at least one action of the one or more actions, based at least in part on the one or more impacts. Method 1800 can comprise multiple variations and embodiments and / or additional and / or alternative steps.

[0064] Another possible method embodiment under the present disclosure is shown in Figure 12. Method 2000 comprises a method performed by an IMF for predicting impact of performing one or more proposed actions on one or more common resources offered in a system, wherein the system is operable under one or more configuration parameters. Step 2010 is comparing one or more actual KPIs, in the system and one or more desired KPIs. Step 2020 is proposing, by a first one or more proposal agents, one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources. Step 2030 is predicting, by second one or more proposal agents, one or more expected KPIs based at least in part on the one or more changes. Step 2040 is selecting, by one or more evaluation agents, at least one action of the one or more actions, based at least in part on the one or more changes and the one or more expected KPIs. Method 2000 can comprise multiple variations and embodiments and / or additional and / or alternative steps.

[0065] Another possible method embodiment under the present disclosure is shown in Figure 13. Method 2200 comprises a method performed by an IMF for predicting impact of performing one or more proposed actions on a system, wherein a first subgroup of proposal agents are not aware of the one or more common resources and a second subgroup of proposal agents areaware of the one or more common resources. Step 2210 is comparing one or more actual KPIs, in the system and one or more desired KPIs. Step 2220 is proposing, by the one or more proposal agents, one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to one or more common resources, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising the one or more common resources. Step 2230 is providing, by one or more prediction agents, one or more mappings for implementing the one or more changes to the first subgroup of proposal agents. Step 2240 is predicting, by the first subgroup of proposal agents, a first one or more expected KPIs based at least in part on the one or more mappings. Method 2200 can comprise multiple variations and embodiments and / or additional and / or alternative steps.

[0066] Another embodiment possible method embodiment under the present disclosure is shown in Figure 14. Method 2400 comprises a method performed by an IMF for predicting impact of performing one or more proposed actions on a system, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources, wherein a first one or more proposal agents are configured to use the one or more common resources and a second one or more proposal agents are not configured to use the one or more common resources, wherein a first subgroup of the first one or more proposal agents are not aware of the one or more common resources and a second subgroup of the first one or more proposal agents are aware of the one or more common resources. Step 2410 is comparing one or more actual KPIs in the system and one or more desired KPIs. Step 2420 is proposing, by at least one proposal agent of the first one or more proposal agents, one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources. Step 2430 is triggering, by a reasoner, the others of the first one or more proposal agents to predict a first one or more expected KPIs in the light of the one or more changes. Step 2440 is providing, by one or more prediction agents, one or more mappings for implementing the one or more changes to the first subgroup of proposal agents. Step 2450 is predicting, by the first subgroup of proposal agents, a second one or more expected KPIs based at least in part on the one or more mappings. Step 2460 is predicting, by the second subgroup of proposal agents, a third one or more expected KPIs based at least in part on the one or more changes. Step 2470 is selecting, by one or more evaluation agents, at least one action of the one or more actions, based at least in part on the one or more changes and the first and secondone or more expected KPIs. Method 2400 can comprise multiple alternative embodiments with additional or alternative steps.

[0067] Figure 15 shows an example of a communication system 3100 in accordance with some embodiments. In the example, the communication system 3100 includes a telecommunication network 3102 that includes an access network 3104, such as a radio access network (RAN), and a core network 3106, which includes one or more core network nodes 3108. The access network 3104 includes one or more access network nodes, such as network nodes 3110a and 3110b (one or more of which may be generally referred to as network nodes 3110), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 3102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 3102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 3102, including one or more network nodes 3110 and / or core network nodes 3108.

[0068] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platformorchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the 0-RAN Alliance or comparable technologies. The network nodes 3110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 3112a, 3112b, 3112c, and 3112d (one or more of which may be generally referred to as UEs 3112) to the core network 3106 over one or more wireless connections.

[0069] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 3100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 3100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0070] The UEs 3112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 3110 and other communication devices. Similarly, the network nodes 3110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 3112 and / or with other network nodes or equipment in the telecommunication network 3102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 3102.

[0071] In the depicted example, the core network 3106 connects the network nodes 3110 to one or more host computing systems, such as host 3116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 3106 includes one more core network nodes (e.g., core network node 3108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 3108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF),Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0072] The host 3116 may be under the ownership or control of a service provider other than an operator or provider of the access network 3104 and / or the telecommunication network 3102. The host 3116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0073] As a whole, the communication system 3100 of Figure 15 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z- Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0074] In some examples, the telecommunication network 3102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 3102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 3102. For example, the telecommunications network 3102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0075] In some examples, the UEs 3112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 3104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 3104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0076] In the example, the hub 3114 communicates with the access network 3104 to facilitate indirect communication between one or more UEs (e.g., UE 3112c and / or 3112d) and network nodes (e.g., network node 3110b). In some examples, the hub 3114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 3114 may be a broadband router enabling access to the core network 3106 for the UEs. As another example, the hub 3114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 3110, or by executable code, script, process, or other instructions in the hub 3114. As another example, the hub 3114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 3114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub 3114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 3114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 3114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0077] The hub 3114 may have a constant / persistent or intermittent connection to the network node 3110b. The hub 3114 may also allow for a different communication scheme and / or schedule between the hub 3114 and UEs (e.g., UE 3112c and / or 3112d), and between the hub 3114 and the core network 3106. In other examples, the hub 3114 is connected to the core network 3106 and / or one or more UEs via a wired connection. Moreover, the hub 3114 may be configured to connect to an M2M service provider over the access network 3104 and / or to anotherUE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 3110 while still connected via the hub 3114 via a wired or wireless connection. In some embodiments, the hub 3114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 3110b. In other embodiments, the hub 3114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 3110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0078] Possible embodiments of the IMF described herein can reside in different parts of system 3100. For example, components of an IMF can be distributed across, e.g., core network 3108, nodes 3110A / B, hub 3114, host 3116, and / or other components. For example, in O-RAN scenarios, IMFs can reside in the SMO (Service Management and Orchestration), as an rAPP (RAN automation application), and various embodiments of methods and systems described herein can be implemented within an rApp. In other embodiments, if some parts of the IMF reside in the O-RAN SMO platform, while some other parts of the IMF in the rAPP (e.g., intent manager framework in the platform and the agents are rApps), there is likely an R1 interface impact. The preceding description should be understood to describe only certain embodiments, and various embodiments may differ. For example, the preceding description may not be practical in certain cases, such as when the IMF is spread across different domains such as a core network (CN) and RAN.

[0079] Figure 16 shows a UE 3200 in accordance with some embodiments. The UE 3200 presents additional details of some embodiments of the UE 3112 of Figure 15. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3 GPP), including a narrowband internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0080] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0081] The UE 3200 includes processing circuitry 3202 that is operatively coupled via a bus 3204 to an input / output interface 3206, a power source 3208, a memory 3210, a communication interface 3212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 16. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0082] The processing circuitry 3202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 3210. The processing circuitry 3202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 3202 may include multiple central processing units (CPUs).

[0083] In the example, the input / output interface 3206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or anycombination thereof. An input device may allow a user to capture information into the UE 3200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presencesensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0084] In some embodiments, the power source 3208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 3208 may further include power circuitry for delivering power from the power source 3208 itself, and / or an external power source, to the various parts of the UE 3200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 3208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 3208 to make the power suitable for the respective components of the UE 3200 to which power is supplied.

[0085] The memory 3210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 3210 includes one or more application programs 3214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 3216. The memory 3210 may store, for use by the UE 3200, any of a variety of various operating systems or combinations of operating systems.

[0086] The memory 3210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD- DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digitaldata storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 3210 may allow the UE 3200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 3210, which may be or comprise a device-readable storage medium.

[0087] The processing circuitry 3202 may be configured to communicate with an access network or other network using the communication interface 3212. The communication interface 3212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 3222. The communication interface 3212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 3218 and / or a receiver 3220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 3218 and receiver 3220 may be coupled to one or more antennas (e.g., antenna 3222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0088] In the illustrated embodiment, communication functions of the communication interface 3212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission controlprotocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0089] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 3212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0090] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0091] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loTdevice in addition to other components as described in relation to the UE 3200 shown in Figure 16.

[0092] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3 GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0093] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0094] Figure 17 shows a network node 3300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), 0-RAN nodes or components of an 0-RAN node (e.g., 0-RU, 0-DU, O-CU).

[0095] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an 0-RAN accessnode) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0096] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSRBSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0097] The network node 3300 includes a processing circuitry 3302, a memory 3304, a communication interface 3306, and a power source 3308. The network node 3300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 3300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 3300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 3304 for different RATs) and some components may be reused (e.g., a same antenna 3310 may be shared by different RATs). The network node 3300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 3300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 3300.

[0098] The processing circuitry 3302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitablecomputing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 3300 components, such as the memory 3304, to provide network node 3300 functionality.

[0099] In some embodiments, the processing circuitry 3302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 3302 includes one or more of radio frequency (RF) transceiver circuitry 3312 and baseband processing circuitry 3314. In some embodiments, the radio frequency (RF) transceiver circuitry 3312 and the baseband processing circuitry 3314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 3312 and baseband processing circuitry 3314 may be on the same chip or set of chips, boards, or units.[000100] The memory 3304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), readonly memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 3302. The memory 3304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 3302 and utilized by the network node 3300. The memory 3304 may be used to store any calculations made by the processing circuitry 3302 and / or any data received via the communication interface 3306. In some embodiments, the processing circuitry 3302 and memory 3304 is integrated.[000101] The communication interface 3306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 3306 comprises port(s) / terminal(s) 3316 to send and receive data, for example to and from a network over a wired connection. The communication interface 3306 also includes radio front-end circuitry 3318 that may be coupled to, or in certain embodiments a part of, the antenna 3310. Radio front-end circuitry 3318 comprises filters 3320 and amplifiers 3322. The radio front-end circuitry 3318 may be connected to an antenna 3310 and processing circuitry 3302. The radio front-end circuitry may be configured to condition signalscommunicated between antenna 3310 and processing circuitry 3302. The radio front-end circuitry 3318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 3318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 3320 and / or amplifiers 3322. The radio signal may then be transmitted via the antenna 3310. Similarly, when receiving data, the antenna 3310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 3318. The digital data may be passed to the processing circuitry 3302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.[000102] In certain alternative embodiments, the network node 3300 does not include separate radio front-end circuitry 3318, instead, the processing circuitry 3302 includes radio frontend circuitry and is connected to the antenna 3310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 3312 is part of the communication interface 3306. In still other embodiments, the communication interface 3306 includes one or more ports or terminals 3316, the radio front-end circuitry 3318, and the RF transceiver circuitry 3312, as part of a radio unit (not shown), and the communication interface 3306 communicates with the baseband processing circuitry 3314, which is part of a digital unit (not shown).[000103] The antenna 3310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 3310 may be coupled to the radio front-end circuitry 3318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 3310 is separate from the network node 3300 and connectable to the network node 3300 through an interface or port.[000104] The antenna 3310, communication interface 3306, and / or the processing circuitry 3302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 3310, the communication interface 3306, and / or the processing circuitry 3302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.[000105] The power source 3308 provides power to the various components of network node 3300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 3308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 3300 with power for performing the functionality described herein. For example, the network node 3300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 3308. As a further example, the power source 3308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.[000106] Embodiments of the network node 3300 may include additional components beyond those shown in Figure 17 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 3300 may include user interface equipment to allow input of information into the network node 3300 and to allow output of information from the network node 3300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 3300. In some embodiments providing a core network node, such as core network node 108 of FIG. 31, some components, such as the radio front-end circuitry 3318 and the RF transceiver circuitry 3312 may be omitted.[000107] Figure 18 is a block diagram illustrating a virtualization environment 3400 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 3400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodimentsin which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 3400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.[000108] Applications 3402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 3400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.[000109] Hardware 3404 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 3406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 3408a and 3408b (one or more of which may be generally referred to as VMs 3408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 3406 may present a virtual operating platform that appears like networking hardware to the VMs 3408.[000110] The VMs 3408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 3406. Different embodiments of the instance of a virtual appliance 3402 may be implemented on one or more of VMs 3408, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.[000111] In the context of NFV, a VM 3408 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 3408, and that part of hardware 3404 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, formsseparate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 3408 on top of the hardware 3404 and corresponds to the application 3402.[000112] Hardware 3404 may be implemented in a standalone network node with generic or specific components. Hardware 3404 may implement some functions via virtualization. Alternatively, hardware 3404 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 3410, which, among others, oversees lifecycle management of applications 3402. In some embodiments, hardware 3404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 3412 which may alternatively be used for communication between hardware nodes and radio units.[000113] Although the computing devices described herein (e.g., UEs, network nodes) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface.In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.[000114] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

Claims

CLAIMS1. A method (1800) performed by an intent management function, IMF (500) for predicting one or more impacts of performing one or more proposed actions on a system, the method comprising: receiving (1810) one or more intents, each of the one or more intents associated with one or more desired key performance indicators, KPIs; detecting (1820) one or more actual KPIs in the system; comparing (1830) the one or more actual KPIs and the one or more desired KPIs; proposing (1840) one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes in one or more configuration parameters, the one or more configuration parameters determining how one or more common resources are shared among one or more different services; detecting (1850) that at least one action of the one or more actions impacts the one or more common resources; predicting (1860) one or more impacts of the at least one action for the one or more actual KPIs; and selecting (1870) at least one action of the one or more actions, based at least in part on the one or more impacts.

2. The method of claim 1 , further comprising actuating the at least one action.

3. A system (800) for evaluating and actuating one or more proposed actions in a system, wherein the system is operable to provide one or more services, the system comprising: an intent management function (810) configured to; receive one or more intents, each of the one more intents associated with one or more desired key performance indicators, KPIs; and compare one or more actual KPIs and the one or more desired KPIs; the intent management function further comprising; a knowledge base (514) configured to store one or more data related to the system;a reasoner (512) configured to assist one or more evaluation agents and detect if one or more actions impact one or more common resources, and further configured to ignore any of the one or more actions that do not impact the one or more common resources; one or more data grounding agents (820) configured to detect the one or more actual KPIs in the system; one or more proposal agents (830) configured to propose the one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes in one or more configuration parameters, the one or more configuration parameters determining how one or more common resources are shared among the one or more services; one or more prediction agents (840) configured to predict one or more impacts on the one or more actual KPIs of the one or more actions if the one or more actions impact the one or more common resources; and the one or more evaluation agents (850) configured to select at least one action of the one or more actions, based at least in part on the one or more impacts.

4. The system of claim 3, further comprising one or more actuation agents (860) configured to actuate the at least one action.

5. A method (2000) performed by a intent management function (1000) for predicting impact of performing one or more proposed actions on one or more common resources offered in a system, wherein the system is operable under one or more configuration parameters, the method comprising: comparing (2010) one or more actual key performance indicators, KPIs, in the system and one or more desired KPIs; proposing (2020), by a first one or more proposal agents (1030), one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources; predicting (2030), by a second one or more proposal agents (1040), one or more expected KPIs based at least in part on the one or more changes; andselecting (2040), by one or more evaluation agents (1050), at least one action of the one or more actions, based at least in part on the one or more changes and the one or more expected KPIs.

6. The method of claim 5, further comprising actuating the at least one action.

7. An intent management function (1000) for predicting impact of performing one or more proposed actions on a system, the system comprising: the intent management function configured to compare one or more actual key performance indicators, KPIs, in the system and one or more desired KPIs, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources; one or more proposal agents (1030, 1040) configured to; propose one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources; predict one or more expected KPIs based at least in part on the one or more changes; and one or more evaluation agents (1050) configured to select at least one action of the one or more actions, based at least in part on the one or more changes and the one or more expected KPIs.

8. The system of claim 7, further comprising one or more actuation agents configured to actuate the at least one action.

9. A method (2200) performed by an intent management function (1200) for predicting impact of performing one or more proposed actions on a system, wherein a first subgroup of proposal agents (1238) are not aware of the one or more common resources and a second subgroup of proposal agents (1235) are aware of the one or more common resources, the method comprising: comparing (2210) one or more actual key performance indicators, KPIs, in the system and one or more desired KPIs; proposing (2220), by the one or more proposal agents (1230), one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to one or more common resources, wherein the system is operable under one or more configurationparameters, the one or more configuration parameters comprising the one or more common resources; providing (2230), by one or more prediction agents (1240), one or more mappings for implementing the one or more changes to the first subgroup of proposal agents; and predicting (2240), by the first subgroup of proposal agents, a first one or more expected KPIs based at least in part on the one or more mappings.

10. The method of claim 9, further comprising: updating, by the second subgroup of proposal agents, a second one or more expected KPIs based at least in part on the one or more changes; and selecting, by one or more evaluation agents (1250), at least one action of the one or more actions, based at least in part on the one or more changes and the first and second one or more expected KPIs.

11. The method of claim 10, further comprising actuating the at least one action.

12. An intent management function (1200) for predicting impact of performing one or more proposed actions on a system, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources, the system comprising: one or more proposal agents (1230), configured to; compare one or more actual key performance indicators, KPIs, in the system and one or more desired KPIs; and propose one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources; a first subgroup of proposal agents (1238) that are not aware of the one or more common resources; one or more prediction agents (1240) configured to provide one or more mappings for implementing the one or more changes to the first subgroup of proposal agents, wherein the first subgroup of proposal agents are configured to update a first one or more expected KPIs based at least in part on the one or more mappingsa second subgroup of proposal agents (1235) that are aware of the one or more common resources and are configured to update a second one or more expected KPIs based at least in part on the one or more changes; and one or more evaluation agents (1250) configured to select at least one action of the one or more actions, based at least in part on the one or more changes and the first and second one or more expected KPIs.

13. The system of claim 12, further comprising one or more actuation agents configured to actuate the at least one action.

14. A method (2400) performed by an intent management function (1400) for predicting impact of performing one or more proposed actions on a system, wherein the system is operable under one or more configuration parameters, the one or more configuration parameters comprising one or more common resources, wherein a first one or more proposal agents (1430, 1434, 1436) are configured to use the one or more common resources and a second one or more proposal agents (1432, 1438) are not configured to use the one or more common resources, wherein a first subgroup (1436) of the first one or more proposal agents are not aware of the one or more common resources and a second subgroup (1434) of the first one or more proposal agents are aware of the one or more common resources, the method comprising: comparing (2410) one or more actual key performance indicators, KPIs, in the system and one or more desired KPIs; proposing (2420), by at least one proposal agent of the first one or more proposal agents, one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources; triggering (2430), by a reasoner (520), the others of the first one or more proposal agents to predict a first one or more expected KPIs in the light of the one or more changes; providing (2440), by one or more prediction agents (1440), one or more mappings for implementing the one or more changes to the first subgroup of proposal agents; predicting (2450), by the first subgroup of proposal agents, a second one or more expected KPIs based at least in part on the one or more mappings;predicting (2460), by the second subgroup of proposal agents, a third one or more expected KPIs based at least in part on the one or more changes; and selecting (2470), by one or more evaluation agents (1450), at least one action of the one or more actions, based at least in part on the one or more changes and the first and second one or more expected KPIs.

15. The method of claim 14, further comprising actuating the at least one action.

16. The method of claim 14 or 15, further comprising proposing, by any of the first or second one or more proposal agents, an additional one or more actions to achieve the one or more desired KPIs.

17. An intent management function (1400) for predicting impact of performing one or more proposed actions on one or more common resources offered in a system, the system operable under one or more configuration parameters of the one or more common resources, the intent management function configured to compare one or more actual key performance indicators, KPIs, in the system and one or more desired KPIs, the intent management function comprising: at least one proposal agent (1430) of a first one or more proposal agents configured to propose one or more actions for attaining the one or more desired KPIs, the one or more actions comprising one or more changes to the one or more common resources, wherein the first one or more proposal agents (1430, 1434, 1436) are configured to use one or more common resources and a second one or more proposal agents (1432, 1438) are not configured to use the one or more common resources, wherein a first subgroup (1436) of the first one or more proposal agents are not aware of the one or more common resources and a second subgroup (1434) of the first one or more proposal agents are aware of the one or more common resources, configured to; a reasoner (520) configured to trigger the others of the first one or more proposal agents (1434, 1436) to predict their one or more expected KPIs in the light of the one or more changes; one or more prediction agents (1440) configured to provide one or more mappings for implementing the one or more changes to the first subgroup (1436) of proposal agents, wherein the first subgroup of proposal agents are configured to predict a first one or more expected KPIs based at least in part on the one or more mappings, and wherein the second subgroup (1434) ofproposal agents are configured to predict a second one or more expected KPIs based at least in part on the one or more changes; and one or more evaluation agents (1450) configured to select at least one action of the one or more actions, based at least in part on the one or more changes and the first and second one or more expected KPIs.

18. The system of claim 17, further comprising one or more actuation agents configured to actuate the at least one action.

19. The system of any of claims 3, 7, 12, or 17, wherein the system comprises at least one of: a user equipment; a network node; a host; a virtualization environment.

20. A computer program (3214, 3402) comprising instructions which, when executed on at least one processor (3202, 3404), cause the at least one processor to carry out a method according to any of claims 1, 5, 10, or 14.

21. A carrier containing a computer program according to claim 20, wherein the carrier comprises one of an electronic signal, optical signal, radio signal or computer readable storage medium.

22. A computer program product (3214, 3402) comprising non transitory computer readable media having stored thereon a computer program according to claim 20.

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