Resource management node deployment method based on artificial intelligence

Abstract

The invention discloses a resource management node deployment method based on artificial intelligence, and relates to the technical field of network resource management, and the method comprises the following steps: S1, constructing a resource node parameter vector set and a state initialization data set; s2, performing function vector clustering by using the resource parameter vector set; s3, performing node deployment mapping logic construction by using the functional region data set; s4, performing node role determination by using the deployment mapping matrix; s5, generating a deployment instruction set by using the deployment mapping matrix and the role label set; and S6, performing multi-scene response verification by using the deployment execution data stream. The method comprises the following steps of: screening and aggregating functional dimensions in a resource parameter vector set according to a set similarity threshold value by setting a measurement mode which takes resource feature structured data as a basis, adopts a functional similarity clustering analysis mechanism and combines Euclidean distance as similarity between feature vectors, so as to generate a functional region data set;

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network resource management, and particularly to a resource management node deployment method based on artificial intelligence. BACKGROUND

[0002] In a distributed computing environment, a cloud-edge collaborative system, and a large-scale intelligent network platform, the deployment of resource management nodes has a decisive influence on task scheduling, data transmission efficiency, and service response capability. Traditional deployment strategies rely on static configuration or scheduling mechanisms based on artificial rules, which are difficult to adapt to the dynamic changes in resource requirements in various application scenarios. With the development of artificial intelligence technology, more and more systems have begun to use methods based on vector representation, similarity calculation, and scenario modeling to optimize node deployment strategies and improve resource utilization efficiency and system adaptability.

[0003] In the prior art, the matching strategy between functional requirements and node capabilities in the resource deployment process often relies on static parameter configuration or fixed resource template mapping methods, and lacks dynamic feature analysis capability from the perspective of functional requirements. Especially when facing multi-source heterogeneous resource parameter inputs, it is difficult to effectively extract and aggregate the core expression at the functional level. SUMMARY

[0004] To overcome the deficiencies of the prior art, the present application provides a resource management node deployment method based on artificial intelligence to solve the problems raised in the background.

[0005] To achieve the above-mentioned purposes, the present application provides the following technical solutions: In a first aspect, the present application provides a resource management node deployment method based on artificial intelligence, comprising the following steps: S1, constructing a resource node parameter vector set and a state initialization data set; S2, using the resource parameter vector set to perform function vector clustering to obtain a function area data set; S3, using the function area data set to construct node deployment mapping logic to obtain a deployment mapping matrix; S4, using the deployment mapping matrix to determine node roles to obtain a node role label set; S5, using the deployment mapping matrix and the role label set to generate a deployment instruction set to obtain a deployment execution data stream; S6, using the deployment execution data stream to perform multi-scenario response verification to obtain a scenario adaptive deployment model.

[0006] Further optimization of the present technical solution is that in step S1, the running parameters of each resource node in the system are collected, and the collected data is normalized to form a complete vector set , wherein the number of currently active nodes; In addition, based on the total resource overview of the full node, the inter-node connectivity topology relationship, the timestamp identifier, and the online and offline state of the node, a state initialization data set is independently established , which is used to describe the overall running state of the system and includes the running state of each node.

[0007] Further optimization of the technical solution, the step S2 first constructs a similarity space, then applies a clustering algorithm, then filters the state data and the clustering result, and finally constructs a functional region data set; In the process of constructing the similarity space, the model formula of the distance function between nodes is: ; wherein, : are all normalized vectors; represents the Euclidean distance, i.e., the L² norm; This function is used to measure the similarity degree of any two nodes in the resource feature dimension.

[0008] Further optimization of the technical solution, in the process of applying the clustering algorithm in step S2, a density-based clustering method is selected to perform functional clustering, and the clustering mechanism constructs a clustering cluster based on the following conditions: ; wherein, : distance threshold, used to control the clustering neighborhood; : minimum sample number, used to control the minimum neighborhood number required to form a core point; Each clustering cluster represents a group of nodes that are density-connected in the resource feature space.

[0009] Further optimization of the technical solution, in the process of state data screening and clustering result filtering in step S2, the state initialization data set output by step S1 is used to perform logical consistency filtering on the preliminary clustering result, and a state mask function is introduced: ; According to the corresponding state of the node in the data set , it is judged whether it currently meets the running stability requirements of scheduling adaptation, and for each clustering cluster , only its logically valid subset is retained: .

[0010] Further optimization of the technical solution, the functional area data set construction process in step S2, all through the state screening cluster set The whole is called functional area data set: ; Among them The final output represents the grouping structure composed of the resource node set corresponding to each functional area, which meets the dual constraints of resource feature similarity and operation logic rationality.

[0011] Further optimization of the technical solution, the step S3 first carries out the calculation of the functional center vector of the region, then carries out the analysis of the functional similarity between regions, then carries out the construction of the mapping reachability judgment function, and finally carries out the construction of the deployment mapping matrix, wherein the calculation of the functional center vector of the region includes: The center representation vector of each functional area The mean value of all functional vectors in the cluster, and its calculation formula is: ; Among them, The number of resource nodes in the th cluster area.

[0012] Further optimization of the technical solution, the functional similarity analysis between regions in step S3 includes: By calculating the cosine similarity of two region center vectors And The matching degree between the functional center vectors is calculated, and the similarity calculation formula is: ; Among them, Indicates the inner product of the vector, Indicates the Euclidean norm.

[0013] Further optimization of the technical solution, the construction of the mapping reachability judgment function in step S3 includes: By setting the judgment function , it is judged whether the region Can accept the deployment mapping request from the region , and its function formula is: ; Among them, The similarity threshold value is used to control the deployment mapping sensitivity, and the value of the similarity threshold value Is set according to the experience or obtained by historical data tuning according to the system's requirement for deployment matching accuracy. ​

[0014] Further optimization of the technical solution, the deployment mapping matrix construction in step S3 includes: Constructing a deployment mapping matrix The expression of the matrix element is: ; A mapping function for inter-regional deployment feasibility, which is used to determine whether a region can be deployed to a region .

[0015] In a second aspect, an embodiment of the present application provides a computer device, including a memory and a processor, and the memory stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the resource management node deployment method based on artificial intelligence according to the first aspect of the present application.

[0016] In a third aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the resource management node deployment method based on artificial intelligence according to the first aspect of the present application.

[0017] Compared with the prior art, the present application provides a resource management node deployment method based on artificial intelligence, which has the following beneficial effects: The resource management node deployment method based on artificial intelligence, by setting a resource feature structured data-based, using a functional similarity clustering analysis mechanism, combining Euclidean distance as a similarity measure between feature vectors, filtering and aggregating the functional dimensions in the resource parameter vector set according to the set similarity threshold, thereby generating a functional region data set, realizing the structured clustering and regional division of resource function attributes before deployment, improving the consistency of functional expression and the accuracy of resource selection in the deployment strategy formulation process, solving the problem of lack of structured modeling path based on functional clustering in the prior art, resulting in weak generalization ability and insufficient granularity resolution of resource deployment strategy. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Fig. 1 A flowchart of a resource management node deployment method based on artificial intelligence according to the present application is shown in the figure; Fig. 2 A function vector clustering flowchart of a resource management node deployment method based on artificial intelligence proposed in the present application is shown in the figure. Fig. 3 A node deployment mapping logic construction flowchart of a resource management node deployment method based on artificial intelligence proposed in the present application is shown in the figure. DETAILED DESCRIPTION

[0020] In order to make the above objectives, features and advantages of the present application more apparent and comprehensible, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0021] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0022] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is the embodiment independent or selectively excluded from other embodiments.

[0023] Embodiment one: Reference Figs. 1-3 For the first embodiment of the present application, the embodiment provides a resource management node deployment method based on artificial intelligence, including the following steps: S1, construct a resource node parameter vector set and a state initialization data set; In step S1, through a mature node monitoring and collecting system, the running parameters of each resource node in the system are collected; after the collection is completed, the collected data is normalized to unify the parameter dimensions, and the normalized indicators are combined into a vector using a feature splicing mechanism All The vectors are numbered according to the node index to form a complete vector set Among them is the number of currently active nodes.

[0024] In addition, based on the overall node resource overview (total occupancy rate, average delay, etc.), the inter-node connectivity topology relationship (detected through the inter-node heartbeat protocol), the timestamp identification and the node online and offline state, a state initialization data set is independently established , which is used to describe the overall running state of the system, including the running state of each node.

[0025] S2, function vector clustering using the resource parameter vector set, obtaining a function area data set; Step S2 is based on the resource parameter vector set obtained in step S1 The node parameter vector , the similarity clustering mechanism is used for function area division, thereby generating a function area data set .

[0026] Step S2 first constructs a similarity space, then applies a clustering algorithm, then filters state data and clustering results, and finally constructs a function area data set; In the similarity space construction process, the model formula of the node distance function is: ; Wherein, : all are normalized vectors; represents the Euclidean distance, that is, the L² norm; This function is used to measure the similarity degree of any two nodes in the resource feature dimension.

[0027] In the clustering algorithm application process in step S2, a density-based clustering method is selected to perform function clustering, and the clustering mechanism constructs a clustering cluster based on the following conditions: ; Wherein, : distance threshold, used to control the clustering neighborhood; : minimum sample number, used to control the minimum neighborhood number required to form a core point; Each clustering cluster represents a group of nodes that are density connected in the resource feature space.

[0028] In the state data screening and clustering result filtering process in step S2, the state initialization data set output by step S1 is used to filter the preliminary clustering results for logical consistency, and a state mask function is introduced: ; According to the corresponding state of the node in the data set , it is judged whether it currently meets the running stability requirements of the scheduling adaptation, and for each clustering cluster , only its logically valid subset is retained: .

[0029] The function area data set construction process in step S2 will collect all the cluster sets that pass the state screening The whole is denoted as a function area data set: ; Among them is the final output, indicating the grouping structure composed of the resource node set corresponding to each function area, satisfying the dual constraints of resource feature similarity and operation logic rationality.

[0030] Unlike most existing resource management methods that rely only on physical topology or static rule division (such as physical location-based, subnet division, or administrator preset logic), step S2 introduces a unified resource parameter vector space model for function similarity analysis; it combines operation state logic judgment to form a function area construction mechanism under dual constraints; the resulting function area data set not only has parameter space clustering consistency, but also has runtime rationality, and has the structural advantage of being directly mapped to the subsequent scheduling system.

[0031] S3, using the function area data set to construct node deployment mapping logic, obtaining a deployment mapping matrix; Step S3 is based on the function areas obtained in step S2 to determine which areas in the system can be used as the mapping logic path for resource deployment. To achieve this purpose, step S3 first performs region function center vector calculation, then performs inter-regional function similarity analysis, then constructs a mapping reachability judgment function, and finally constructs a deployment mapping matrix.

[0032] Step S3 first performs region function center vector calculation, then performs inter-regional function similarity analysis, then constructs a mapping reachability judgment function, and finally constructs a deployment mapping matrix.

[0033] Region function center vector calculation: The center representation vector of each function area is the mean of all function vectors within the cluster, and its calculation formula is: ; Among them, represents the number of resource nodes in the th cluster area.

[0034] Inter-regional function similarity analysis: By calculating the cosine similarity of two region center vectors and , the matching degree between function center vectors is calculated, and the similarity calculation formula is: ; wherein, denotes the vector inner product, denotes the Euclidean norm.

[0035] Mapping reachability judgment function construction: By setting the judgment function , judge whether the region is acceptable to the deployment mapping request from the region , and its function formula is: ; wherein, is a similarity threshold value, used to control the deployment mapping sensitivity, and the value of the similarity threshold is set according to the experience of the system's requirement for deployment matching accuracy or obtained through historical data tuning.

[0036] Deployment mapping matrix construction: Construct a deployment mapping matrix , and the expression of the matrix element is: ; is a mapping function of the deployment feasibility between regions, and the formula is used to judge whether the region can be deployed to the region .

[0037] Traditional mapping usually relies on preset node capability model or static policy table. In this step, the deployment mapping matrix is constructed based on the dynamic similarity between the functional region vectors, without additional node set, forming an adaptive mapping mechanism based on functional expression, and improving the matching accuracy and adaptability in dynamic resource environment.

[0038] S4, using the deployment mapping matrix to determine the node role, obtaining a node role label set; Step S4 obtains the participation degree of each node in functional deployment by counting the number of functional regions mapped by each node in the deployment mapping matrix . This process uses mature sparse matrix non-zero element counting method to traverse and calculate each column (or each row); Using the feature label in the functional region data set in step S2, combining the matching item position in , applying mature label co-occurrence statistical method to calculate the feature overlap degree of the node and its mapped functional region, obtaining the "deployment strength" index; at the same time, through the feature coverage graph modeling method, the relationship density between node functions is evaluated, obtaining the "function coupling degree" index; taking the above three indexes of "participation degree", "deployment strength" and "function coupling degree" as input features, using mature rule-based hierarchical label classification system, the node is divided into different role types, forming a role label; Finally, all nodes and their role labels are combined to form a node role label set, each record identifying a node ID and its role classification, as the basis for the next step of network structure mapping.

[0039] S5, using the deployment mapping matrix and the role label set to generate a deployment instruction set, obtaining a deployment execution data flow; Step S5 is based on the deployment mapping matrix For each resource management node, extract its corresponding functional area set to form a node-function correspondence table; Using the node role label set generated in step S4, the role attribute of each node is parsed, combined with the node function execution table, and using a mature conditional mapping rule model to determine the corresponding relationship between its deployment method and communication logic.

[0040] Using a mature process task descriptor generation method, the deployment method of each node is combined with the function task to build a standardized deployment instruction structure, using a mature event-driven process model (Event-Driven Process Chain, EPC) to model the deployment instruction structure, through a directed graph structure to organize each task node, and using the node role label set to determine the execution node and data input source of each task, finally building a deployment execution data flow representing the task scheduling process between nodes.

[0041] The expression of the deployment execution data flow table finally output in step S5 is: ; For the node role label set, ; For the deployment instruction structure set generated by the task trigger path, ; For the binding mapping function of the role label and the task instruction, .

[0042] S6, using the deployment execution data flow to verify multiple scene responses, obtaining a scene adaptive deployment model; Step S6 is based on the deployment execution data flow The response performance under multiple running scenarios is verified, and a scene adaptive deployment model with environmental adaptability is constructed.

[0043] Multi-scene environment set construction: ; ; Among them, : Load state vector; Network state vector; : Service request vector.

[0044] In-scenario deployment of data flow execution simulation: For each scene The deployment execution data stream obtained in step S5 is used. Simulation scheduling and execution are performed, during which the execution structure is executed for each deployment instruction. Simulate its performance in the scene Based on key performance indicators such as execution latency, communication cost, and resource consumption, a deployment performance indicator vector for this scenario is obtained: ,in Its dimensions include, but are not limited to, response time, throughput, and stability score.

[0045] Adaptive model building and regression optimization: Deployment performance metrics results across multiple scenarios: Combined with scene attribute vector set: We use the mature Support Vector Regression (SVR) model to build an adaptive deployment model: ; In this AI-based resource management node deployment method, the adaptive deployment model generated in step S6 is used to achieve automatic deployment decisions for resource management nodes under various dynamic scenarios. This model, by inputting new scenario attribute vectors (such as node performance status, network topology changes, service request density, etc.), matches the optimal deployment execution data flow from the trained deployment strategies, thereby outputting a set of deployment task instructions that can be directly issued. The use of this model no longer relies on manual rule formulation, but rather on the learning results based on historical multi-scenario deployment data, achieving real-time, intelligent, and differentiated node deployment responses, improving the system's adaptive scheduling capabilities and resource utilization efficiency in complex business scenarios.

[0046] Step S6 differs from the traditional approach of deploying and optimizing only in a single scenario. It introduces a multi-scenario joint simulation mechanism and a cross-scenario adaptive regression modeling process to achieve generalized learning capabilities for deployment logic. Traditional methods often formulate deployment strategies based on fixed scenario rules, lacking robust modeling and predictive feedback capabilities under environmental changes. This step, however, establishes a multi-dimensional mapping model between deployment behavior and scenario attributes, serving as a crucial transitional step towards a self-adjusting deployment system.

[0047] Example 2: The embodiment also provides a computer device suitable for the case of the resource management node deployment method based on artificial intelligence, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the resource management node deployment method based on artificial intelligence provided in the above embodiment.

[0048] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to realize the resource management node deployment method based on artificial intelligence provided in the above embodiment.

[0049] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0050] If the functions are realized in the form of software function units and sold or used as independent products, the functions can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0051] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

[0052] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In some embodiments, the computer-readable medium can be non-transitory.

[0053] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques, which are well known in the art of making integrated circuits, can be used to implement the various techniques and technologies described herein: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0054] It should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not limit the present application, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced, without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of claims of the present application.

Claims

1. An artificial intelligence-based resource management node deployment method, characterized by, Comprising the following steps: S1, constructing a resource node parameter vector set and state initialization data set; S2, using the resource parameter vector set to perform function vector clustering to obtain a function area data set; S3, using the function area data set to perform node deployment mapping logic construction to obtain a deployment mapping matrix; S4, using the deployment mapping matrix to determine node roles to obtain a node role label set; S5, using the deployment mapping matrix and the role label set to generate a deployment instruction set to obtain a deployment execution data stream; S6, using the deployment execution data stream to perform multi-scenario response verification to obtain a scenario adaptive deployment model.

2. The method of claim 1, wherein, In the step S1, the running parameter of each resource node in the system is collected, and the collected data is normalized to form a complete vector set wherein is the number of currently active nodes In addition, based on the total resource overview of the full node, the inter-node connectivity topology relationship, the timestamp identification, and the on-and-off-line state of the node, a state initialization dataset is independently established , which is used to depict the overall running state of the system and contains the running state of each node.

3. The method of claim 1, wherein, The step S2 first performs similarity space construction, then performs clustering algorithm application, then performs state data screening and clustering result filtering, and finally performs function area data set construction; In the similarity space construction process, the distance function between nodes The model formula is: ； wherein : are both normalized vectors; denotes the Euclidean distance, i.e. the L2 norm; This function is used to measure the similarity degree of any two nodes in the resource characteristic dimension.

4. The method of claim 3, wherein, In the clustering algorithm application process of step S2, a density-based clustering method is selected. Functional clustering is performed, and the clustering mechanism constructs clusters based on the following conditions: ； Wherein, : distance threshold for controlling the clustering neighborhood; : minimum number of samples to control the minimum number of neighborhoods needed to form a core point; Each cluster of nodes represents a set of nodes that are densely connected in the resource feature space.

5. The method of claim 3, wherein, In the state data screening and clustering result filtering process in step S2, the state initialization data set output in step S1 is used Logical consistency filtering is performed on the preliminary clustering result, and a state mask function is introduced: ； According to the node In the data set The corresponding state, judge whether it meets the running stability requirements of scheduling adaptation at present, keep its logically valid subset for each cluster ​ 。 6. The method of claim 3, wherein the method further comprises: In the functional region dataset construction process in the step S2, all the cluster sets passing the state screening The whole is denoted as a functional region dataset: ； wherein is the final output, representing the grouping structure of the resource node sets corresponding to each functional area, satisfying the dual constraints of resource feature similarity and operation logic rationality.

7. The method of claim 1, wherein, The step S3 first performs region function center vector calculation, then performs inter-region function similarity analysis, then performs mapping reachability judgment function construction, and finally performs deployment mapping matrix construction; The region function center vector calculation includes: Each functional region The center of the cluster represents the vector is the mean of all functional vectors within the cluster, which is calculated as: ； wherein, represents the number of resource nodes in the th cluster region.

8. The method of claim 7, wherein, The inter-region function similarity analysis in the step S3 includes: The matching degree between the functional center vectors is calculated by calculating the cosine similarity of the two region center vectors and The similarity calculation formula is: ； wherein denotes the vector inner product, denotes the Euclidean norm.

9. The method of claim 7, wherein, The mapping reachability judgment function construction in the step S3 includes: By setting a decision function , judge whether the area is acceptable from the area of the deployment mapping request, its function formula is: ； wherein, is a similarity threshold value for controlling the deployment mapping sensitivity, the similarity threshold value The value of is set according to the experience or obtained by tuning the historical data according to the system requirement for deployment matching accuracy.

10. The method of claim 1, wherein, The deployment mapping matrix construction in the step S3 includes: Constructing a deployment mapping matrix The expression for the matrix elements is ； Mapping function for inter-zone deployment feasibility, the formula is used to determine whether a zone is deployable to a zone .

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