Cloud node shutdown management

Abstract

A method, system, and device for shutting down an identified Open Radio Access Network (O-RAN) cloud (O-Cloud) node is provided, the method including receiving, by a Federated O-Cloud Orchestration and Management (FOCOM), a recommendation from an rApp to shut down the identified O-Cloud node, sending, by FOCOM, an instruction to a Network Function Orchestrator (NFO) to move workloads from the identified O-Cloud node to an alternative O-Cloud node based on the recommendation, and sending, by FOCOM, an instruction to Infrastructure Management Services (IMS) to shut down the identified O-Cloud node.

Description

[Technical Field]

[0001] Systems and methods consistent with example embodiments of the present disclosure relate to managing open radio access network (O-RAN) cloud (O-Cloud) node shutdown. [Background technology]

[0002] The radio access network (RAN) is a critical component in telecommunications systems because it connects end-user devices (or user equipment) to the rest of the network. The RAN includes a combination of various network elements (NEs) that connect end-user devices to the core network. Traditionally, the hardware and / or software of a particular RAN is vendor-specific.

[0003] Open RAN (O-RAN) technology has emerged to enable multiple vendors to provide hardware and / or software for telecommunication systems. To this end, O-RAN decomposes RAN functions into a centralized unit (CU), a distributed unit (DU), and a radio unit (RU). The CU is a logical node that hosts the RAN's Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP), and / or Packet Data Convergence Protocol (PDCP) sublayers. The DU is a logical node that hosts the RAN's Radio Link Control (RLC), Media Access Control (MAC), and Physical (PHY) sublayers. The RU is a physical node that converts radio signals from the antenna into digital signals that can be transmitted to the DU via the fronthaul. These entities can be developed by different vendors because they have open protocols and interfaces between them.

[0004] Figure 1 shows a conventional O-RAN architecture. Referring to Figure 1, RAN functions in the O-RAN architecture are controlled and optimized by RICs. RICs are software-defined components that implement modular applications to facilitate the multi-vendor interoperability required in O-RAN systems and to automate and optimize RAN operations. RICs are divided into two types: non-real-time RICs (Non-RT RICs) and near-real-time RICs (Near-RT RICs).

[0005] The Non-RT RIC is the control point for non-real-time control loops and operates on sub-second timescales within a Service Management and Orchestration (SMO) framework. Its functionality is implemented through modular applications called rApps (rApp1, ..., rAppN) and includes providing policy-based guidance and enrichment over the A1 interface, which is the interface that enables the Non-RT RIC to communicate with the Near-RT RIC; performing data analytics; artificial intelligence / machine learning (AI / ML) training and inference for RAN optimization; and / or recommending configuration management actions over the O1 interface, which is the interface that connects the SMO to RAN managed elements (e.g., Near-RT RIC, O-RAN centralized unit (O-CU), O-RAN distributed unit (O-DU), etc.).

[0006] The Near-RT RIC operates on a timescale between 10 milliseconds and 1 second and connects to the O-DU, O-CU (decomposed into the O-CU control plane (O-CU-CP) and O-CU user plane (O-CU-UP)), and open evolved NodeB (O-eNB) via the E2 interface. The Near-RT RIC uses the E2 interface to control the underlying RAN elements (E2 nodes / network functions (NFs)) in a near-real-time control loop. The Near-RT RIC monitors, pauses / stops, overrides, and controls the E2 nodes (O-CU, O-DU, and O-eNB) through policies. For example, the Near-RT sets policy parameters for activated functions in the E2 nodes. Additionally, the Near-RT RIC hosts xApps to implement functions such as quality of service (QoS) optimization, mobility optimization, slicing optimization, interference mitigation, load balancing, and security. The two types of RICs work together to optimize O-RAN. For example, the Non-RT RIC provides policies, data, and AI / ML models over the A1 interface that are implemented and used by the Near-RT RIC for RAN optimization, and the Near-RT returns policy feedback (i.e., how the policies set by the Non-RT RIC are performing).

[0007] The SMO framework in which the Non-RT RIC is located manages and orchestrates RAN elements. Specifically, the SMO includes the Federated O-Cloud Orchestration and Management (FOCOM), the Network Function Orchestrator (NFO) that manages virtual machine (VM)-based virtual network functions (VNFs) and container (i.e., instance)-based VNFs, and the Operation and Administration Management (OAM) as part of the SMO that manages and orchestrates what is called the O-RAN Cloud (O-Cloud). The O-Cloud is a collection of physical RAN nodes hosting the RIC, O-CU, and O-DU, supporting software components (e.g., operating systems and runtime environments), and the SMO itself. In other words, the SMO manages the O-Cloud from within. The O2 interface is the interface between the SMO and the O-Cloud on which it resides. Through the O2 interface, the SMO provides Infrastructure Management Services (IMS) and Deployment Management Services (DMS). The O2 interface can also transmit O2 telemetry data, such as O-cloud configuration or any logical function data, energy consumption, node health status, etc. to the SMO. Summary of the Invention [Means for solving the problem]

[0008] In related technologies, a VNF may be implemented as a single or set of VMs or containers deployed in a data center on commodity hardware. To achieve cost efficiency, it is typically desirable to minimize the power consumption of the Network Function Virtualization (NFV) infrastructure. In related technologies, this can be achieved by using the minimum set of physical servers required by the VNF in terms of compute, memory, and disk-related parameters. Thus, network energy consumption can be optimized according to the network traffic load by shutting down unused resources and maintaining optimal network capacity. When the O-Cloud is operating under low load, deployed NFs or their microservices can be migrated to shut down idle O-Cloud nodes.

[0009] In related technologies of the O-RAN architecture, the management of O-Cloud resources (either physical or logical resources), such as the shutdown of O-Cloud nodes, is orchestrated by FOCOM with the help of IMS. For this purpose, FOCOM can communicate with IMS via the O2 interface. Thus, it may be desirable to manage the shutdown of O-Cloud nodes using recommendations (e.g., from rApps) in order to optimize network capacity based on energy consumption and other factors.

[0010] However, situations may arise where workloads currently running on an O-Cloud node need to be moved to an alternative node before the O-Cloud node can be shut down. Therefore, coordination between FOCOM and NFO is required to facilitate the shutdown of the O-Cloud node, which may include workload migration.

[0011] Example embodiments of the present disclosure provide a method and system for shutting down an identified Open Radio Access Network (O-RAN) cloud (O-Cloud) node. In particular, this may include receiving, by a Federated O-Cloud Orchestration and Management (FOCOM), a recommendation from an rApp to shut down the identified O-Cloud node; sending, by FOCOM, an instruction to a Network Function Orchestrator (NFO) to move workloads from the identified O-Cloud node to an alternative O-Cloud node based on the recommendation; and sending, by FOCOM, an instruction to an Infrastructure Management Services (IMS) to shut down the identified O-Cloud node. Thus, FOCOM can facilitate communication with the NFO based on the recommendation from the rApp to shut down the identified O-Cloud node, thereby optimizing network energy and efficiency and reducing operational costs.

[0012] According to an embodiment, a Federated O-Cloud Orchestration and Management (FOCOM) may be provided and configured to receive a recommendation from an rApp to shut down an identified O-Cloud node, send an instruction to a Network Function Orchestrator (NFO) to move workloads from the identified O-Cloud node to an alternative O-Cloud node based on the recommendation, and send an instruction to Infrastructure Management Services (IMS) to shut down the identified O-Cloud node.

[0013] According to an embodiment, a non-transitory computer-readable storage medium may be provided, having recorded thereon instructions for performing a method including receiving, by a Federated O-Cloud Orchestration and Management (FOCOM), a recommendation from an rApp to shut down an identified O-Cloud node; sending, by FOCOM, an instruction to a Network Function Orchestrator (NFO) to move workload from the identified O-Cloud node to an alternative O-Cloud node based on the recommendation; and sending, by FOCOM, an instruction to an Infrastructure Management Service (IMS) to shut down the identified O-Cloud node.

[0014] Additional aspects will be set forth in part in the description that follows, and in part will be apparent from the description, or may be learned by practice of the illustrated embodiments of the present disclosure.

[0015] Features, aspects, and advantages of certain exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which like reference numerals refer to like elements. [Brief explanation of the drawings]

[0016] [Figure 1] 1 shows the O-RAN architecture according to related technologies. [Figure 2] 1 illustrates a system architecture for an SMO service according to one embodiment. [Figure 3] 1 illustrates a flowchart of a method for shutting down a cloud node and migrating workloads, according to one embodiment. [Figure 4] 1 illustrates a diagram of an example environment in which the systems and / or methods described herein may be implemented. [Figure 5]1 illustrates a diagram of exemplary components of a device according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0017] The following detailed description of the exemplary embodiments refers to the accompanying drawings, in which the same reference numbers in different drawings may identify the same or similar elements.

[0018] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations. Moreover, one or more features or components of one embodiment may be incorporated into or combined with other embodiments (or one or more features of other embodiments). Additionally, in the flowcharts and descriptions of operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be performed (at least partially) concurrently, and the order of one or more operations may be interchanged.

[0019] It will be apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not limiting of the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code. It will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0020] Although particular combinations of features are recited in the claims and / or disclosed herein, these combinations are not intended to limit the disclosure of possible implementations. Indeed, many of these features can be combined in ways not specifically recited in the claims and / or disclosed herein. Although each dependent claim listed below may depend directly on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.

[0021] No element, act, or instruction used herein should be construed as critical or required unless expressly stated as such. Also, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more." Where only one item is intended, the term "one" or similar language is used. Also, as used herein, terms such as "has," "have," "having," "include," and "including" are intended to be open-ended terms. Furthermore, the phrase "based on" is intended to mean "based at least in part on," unless specifically stated otherwise. Furthermore, phrases such as "at least one of [A] and [B]" or "at least one of [A] or [B]" should be understood to include A only, B only, or both A and B.

[0022] Exemplary embodiments relate to O-Cloud resource optimization, which is a process of efficiently utilizing O-Cloud resources and eliminating O-Cloud resource waste by selecting, provisioning, and right-sizing resources within the O-Cloud. According to exemplary embodiments, Network Functions (NFs) in the O-Cloud are orchestrated as VNFs / CNFs. SMO (NFO, FOCOM) handles the management and orchestration of the VNFs / CNFs and the underlying O-Cloud infrastructure. The management, orchestration, and optimization functions of SMO can be enhanced by intelligent observability analytics from the VNFs / CNFs and the O-Cloud, according to exemplary embodiments.

[0023] The Non-RT RIC hosts third-party applications, such as rApps within the SMO, which can collect and read various O1- and O2-related observability data and metrics through O1- and O2-related services. These third-party rApps can be leveraged in exemplary embodiments to provide guidance and / or recommendations to the NFO and FOCOM for the management, orchestration, and optimization of the VNFs / CNFs and the underlying O-Cloud infrastructure.

[0024]

[0006] Exemplary embodiments of the present disclosure provide a method and system for a FOCOM to facilitate communication with an NFO to shut down an identified O cloud node based on a recommendation from an rApp. In particular, this may include: FOCOM receiving a recommendation from the rApp to shut down the identified O cloud node; FOCOM sending an instruction to the NFO to move workload from the identified O cloud node to an alternative O cloud node based on the recommendation; and FOCOM sending an instruction to Infrastructure Management Services (IMS) to shut down the identified O cloud node. Thus, FOCOM can facilitate communication with the NFO based on the recommendation from the rApp to shut down the identified O cloud node, thereby optimizing network energy and efficiency and reducing operational costs.

[0025] 2 illustrates a block diagram of an SMO services (SMOS) system architecture according to one embodiment. In particular, SMO 200 may include services (SMOS) that can communicate with each other via a communication interface (labeled SMOS communication in FIG. 2).

[0026] NFO 210 and FOCOM 220 can communicate with O cloud services (not shown) via an O2 interface.

[0027] The RAN NF 230 may be configured with one or more RAN NF OAM services capable of performing functions responsible for operations, administration, and maintenance (OAM).

[0028] Service Management and Exposure (SME) 240, R1-related services 250, rApp 260, Data Management and Exposure (DME) 270, AI / ML workflow 280, and A1-related services 290 may be services running on a Non-RT RIC.

[0029] In some embodiments, rApp 260 can communicate with SME 240 via the R1 interface to obtain information about FOCOM / DMS / O1 services and endpoints. In some embodiments, rApp 260 can also communicate with DME 270 to obtain information about data types supported by FOCOM / DMS / O1 services.

[0030] According to some embodiments, FOCOM 220 can communicate with SME 240 to discover NFO services and endpoints. According to some embodiments, FOCOM 220 can communicate with DME 270 to discover data types supported by NFO.

[0031] According to some embodiments, the NFO 210 can communicate with the SME 240 to discover RAN OAM services. According to some embodiments, the NFO 210 can communicate with the DME 270 to discover data types supported by OAM.

[0032] The rApp 260 can also subscribe to O1- and O2-related data. In particular, the rApp 260 can subscribe to O1-related data from the OAM service (e.g., the RAN NF 230) and O2-related data from the FOCOM 220 and the DMS. This data can be used to collect information about traffic patterns and resource usage of O-Cloud nodes. This can be done, for example, by using or monitoring O-Cloud system metrics or VM metrics. However, it should be understood by those skilled in the art that other data collection methods can be used depending on the specific implementation. Such information can be collected from the O-Cloud by the IMS and the DMS and transmitted northbound using at least one of the O2ims and O2dms interfaces. That is, the FOCOM 220 can be implemented using the O2ims API, and the DMS can be implemented using the O2dms API.

[0033] Thus, rApp 260 may collect and store data regarding traffic patterns and resource usage (e.g., CPU, memory, network, etc.) based on the O1- and O2-related data of the O cloud nodes. According to some embodiments, machine learning (ML) and / or artificial intelligence (AI) techniques may be used to analyze the stored data to analyze the collected data and correspondingly identify traffic patterns and resource usage of the O cloud nodes. Based on the analysis, an ML model may be trained to predict future traffic patterns and resource usage based on the O1- and O2-related data. The trained ML model may then be used to identify idle time and underutilized resources. While ML / AI techniques may be implemented using AI / ML workflow 280 according to some implementations, the specific manner in which ML model training is implemented is not particularly limited thereto.

[0034] Based on rApp 260's use of the trained ML model, rApp 260 can be configured to recommend to FOCOM 220 that the O-Cloud node be shut down. It should also be appreciated that after recommending a node shutdown, rApp 260 can continuously monitor and update the ML model to ensure that future recommendations are also accurate.

[0035] FIG. 3 illustrates a flowchart of a method 300 for shutting down a cloud node and migrating workloads, according to one embodiment.

[0036] In operation 301, a FOCOM (e.g., FOCOM 220) may receive a recommendation from an rApp (e.g., rApp 260) to shut down an identified O-cloud node. This may be an O-cloud node that has been determined to be idle by the rApp based on a trained ML model, or an O-cloud node for which shutting down the identified O-cloud node would improve network efficiency.

[0037] According to some embodiments, after receiving the recommendation, the FOCOM can communicate with the SME (e.g., SME 240) and the DME (e.g., SME 270) to discover the NFO service endpoints and supported data types.

[0038] According to some embodiments, after receiving the recommendation, the NFO (e.g., NFO 210) can communicate with the topology exposure and inventory management SMOS to obtain topology and inventory information to determine workload placement. Similarly, according to some embodiments, the rApp can also communicate with the topology exposure and inventory management SMOS to recommend workload placement to the NFO.

[0039] In operation 302, the FOCOM may send an instruction to the NFO to move the workload to an alternative O cloud node. According to embodiments, this may be performed only if there is a workload running on the identified O cloud node. Upon receiving the instruction, the NFO may check the availability of an alternative O cloud node, thereby orchestrating the workload movement using the DMS. However, according to some embodiments, if the NFO cannot find a suitable node, the NFO may return an error message to the FOCOM, which may indicate that no suitable workload is available.

[0040] According to some embodiments, upon receiving instructions from the FOCOM, the NFO may communicate with the SME to discover RAN OAM services and with the DME to discover data types supported by OAM.

[0041] According to some embodiments, after receiving instructions from the FOCOM, the NFO can send instructions to the OAM (e.g., RAN NF 230) to drain all traffic from the identified O-cloud node and move the traffic to an alternate O-cloud node. After performing the move, the OAM can send a confirmation to the NFO that the traffic was successfully moved.

[0042] In operation 303, FOCOM can instruct the IMS to shut down the node. This may be performed via the O2ims interface according to an embodiment. Operation 303 may be performed after it receives confirmation of successful traffic movement from the NFO. Specifically, after the DMS moves the workload, it can send a confirmation (e.g., a confirmation message) to the NFO, which can then send a confirmation to FOCOM. As mentioned above, according to some embodiments, the rApp can continue to monitor and update the ML model even after the identified O-Cloud node is shut down.

[0043] Based on the above embodiments, it can be seen that FOCOM can facilitate communication with NFO based on the recommendation from rApp to shut down the identified O-cloud nodes, thereby optimizing the energy and efficiency of the network and reducing the operational costs.

[0044] 4 is a diagram of an example environment 400 in which the systems and / or methods described herein may be implemented. As shown in FIG. 4, environment 400 may include a user device 410, a platform 420, and a network 430. The devices in environment 400 may be interconnected by wired connections, wireless connections, or a combination of wired and wireless connections. In an embodiment, any of the functions and operations described with reference to FIGS. 2 through 4 above may be performed by any combination of elements shown in FIG. 4.

[0045] The user device 410 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information related to the platform 420. For example, the user device 410 may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smartphone, a wireless telephone, etc.), a wearable device (e.g., smart glasses or a smart watch), or a similar device. In some implementations, the user device 410 can receive information from the platform 420 and / or transmit information to the platform 420.

[0046] Platform 420 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information. In some implementations, platform 420 may include a cloud server or a collection of cloud servers. In some implementations, platform 420 may be designed to be modular, such that certain software components can be swapped in or out depending on particular needs. As such, platform 420 may be easily and / or quickly reconfigured for different uses.

[0047] In some implementations, as shown, platform 420 may be hosted in a cloud computing environment 422. Notably, although the implementations described herein describe platform 420 as being hosted within cloud computing environment 422, in some implementations platform 420 may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

[0048] Cloud computing environment 422 includes an environment that hosts platform 420. Cloud computing environment 422 may provide services such as computation, software, data access, storage, etc. that do not require end-user (e.g., user device 410) knowledge of the physical location and configuration of the systems and / or devices that host platform 420. As shown, cloud computing environment 422 may include a collection of computing resources 424 (collectively referred to as “computing resources 424” and individually referred to as “computing resource 424”).

[0049] Computing resources 424 include one or more personal computers, clusters of computing devices, workstation computers, server devices, or other types of computing and / or communication devices. In some implementations, computing resources 424 may host platform 420. Cloud resources may include compute instances running within computing resources 424, storage devices provided within computing resources 424, data transfer devices provided by computing resources 424, etc. In some implementations, computing resources 424 may communicate with other computing resources 424 via wired connections, wireless connections, or a combination of wired and wireless connections.

[0050] As further shown in FIG. 4 , the computing resources 424 include a collection of cloud resources, such as one or more applications (“Applications” (APPs) 424-1), one or more virtual machines (“Virtual Machines” (VMs) 424-2), virtualized storage (“Virtualized Storage” (VSs) 424-3, and one or more hypervisors (“Hypervisors” (HYPs) 424-4). While the present exemplary embodiment refers to virtualized network functions, it is understood that one or more other exemplary embodiments may be implemented in at least one of, but not limited to, containers, cloud-native services, one or more container platforms, and the like. For example, in one or more other exemplary embodiments, any of the above-described components (e.g., nodes, E2 nodes, SMO functions, RICs, systems, devices, etc.) may be software-based components deployed or hosted on a server cluster, such as, for example, a hybrid cloud server or a data center server. The software-based components may be containerized and operate or execute containerized network elements, deployed and controlled by one or more addressable machines referred to as “nodes.” In this regard, a server cluster may include at least one master node and multiple worker nodes, where the master node(s) control and manage a set of associated worker nodes.

[0051] Applications 424-1 include one or more software applications that may be provided to or accessed by user device 410. Applications 424-1 can eliminate the need to install and run software applications on user device 410. For example, applications 424-1 may include software associated with platform 420 and / or any other software that may be provided via cloud computing environment 422. In some implementations, one application 424-1 may send and receive information to one or more other applications 424-1 via virtual machine 424-2.

[0052] Virtual machine 424-2 includes a software implementation of a machine (e.g., a computer) that executes programs like a physical machine. Virtual machine 424-2 can be either a system virtual machine or a process virtual machine, depending on the use and the degree to which virtual machine 424-2 matches any actual machine. A system virtual machine may provide a complete system platform that supports the execution of a complete operating system ("OS"). A process virtual machine may execute a single program and support a single process. In some implementations, virtual machine 424-2 may run on behalf of a user (e.g., user device 410) and manage the infrastructure of cloud computing environment 422, such as data management, synchronization, or long-term data transfer.

[0053] Virtualized storage 424-3 includes one or more storage systems and / or one or more devices that use virtualization technology within the storage systems or devices of computing resources 424. In some implementations, types of virtualization in the context of storage systems may include block virtualization and file virtualization. Block virtualization may refer to the abstraction (or separation) of logical storage from physical storage so that the storage system can be accessed regardless of the physical storage or heterogeneous structure. The separation may provide storage system administrators with flexibility in how they manage storage for end users. File virtualization may eliminate the dependency between data accessed at the file level and where the file is physically stored. This may enable performance optimization of storage usage, server consolidation, and / or nondisruptive file movement.

[0054] Hypervisor 424-4 may provide hardware virtualization technology that allows multiple operating systems (e.g., "guest operating systems") to run simultaneously on a host computer, such as computing resource 424. Hypervisor 424-4 may provide a virtual operating platform for the guest operating systems and may manage the execution of the guest operating systems. Multiple instances of different operating systems may share virtualized hardware resources.

[0055] Network 430 may include one or more wired and / or wireless networks. For example, network 430 may include a cellular network (e.g., a Fifth Generation (5G) network, a Long-Term Evolution (LTE) network, a Third Generation (3G) network, a Code Division Multiple Access (CDMA) network, etc.), a Public Land Mobile Network (PLMN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a telephone network (e.g., a Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, an optical fiber-based network, etc., and / or a combination of these or other types of networks.

[0056] The number and arrangement of devices and networks shown in Figure 4 are provided as an example. In practice, there may be more, fewer, different, or differently arranged devices and / or networks than those shown in Figure 4. Furthermore, two or more devices shown in Figure 4 may be implemented within a single device, or a single device shown in Figure 4 may be implemented as multiple distributed devices. Additionally, or instead, one set of devices (e.g., one or more devices) of environment 400 may perform one or more functions described as being performed by another set of devices in environment 400.

[0057] 5 is a diagram of example components of a device 500. The device 500 may correspond to a user device 410 and / or a platform 420. As shown in FIG. 5, the device 500 may include a bus 510, a processor 520, a memory 530, a storage component 540, an input component 550, an output component 560, and a communication interface 570.

[0058] The bus 510 includes components that enable communication between the components of the device 500. The processor 520 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 520 may be a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, the processor 520 includes one or more processors that can be programmed to perform functions. Memory 530 may include random access memory (RAM), read only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) that stores information and / or instructions for use by processor 520.

[0059] The storage component 540 stores information and / or software related to the operation and use of the device 500. For example, the storage component 540 may include a hard disk (e.g., a magnetic disk, an optical disk, an optical-magnetic disk, and / or a solid-state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium, along with a corresponding drive. The input component 550 includes components that enable the device 500 to receive information, such as by user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, buttons, switches, and / or a microphone). Additionally or alternatively, the input component 550 may include sensors that sense information (e.g., a Global Positioning System (GPS) component, an accelerometer, a gyroscope, and / or an actuator). Output components 560 include components that provide output information from device 500 (eg, a display, a speaker, and / or one or more light-emitting diodes (LEDs)).

[0060] The communication interface 570 includes transceiver-like components (e.g., a transceiver and / or a separate receiver and transmitter) that enable the device 500 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface 570 may enable the device 500 to receive information from another device and / or provide information to another device. For example, the communication interface 570 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a Universal Serial Bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

[0061] Device 500 may perform one or more processes described herein. Device 500 may perform these processes in response to processor 520 executing software instructions stored by a non-transitory computer-readable medium, such as memory 530 and / or storage component 540. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spread across multiple physical storage devices.

[0062] The software instructions may be loaded into memory 530 and / or storage component 540 from another computer-readable medium or from another device via communication interface 570. When executed, the software instructions stored in memory 530 and / or storage component 540 may cause processor 520 to perform one or more processes described herein.

[0063] Additionally, or instead, hardwired circuitry may be used in place of or in combination with software instructions to implement one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

[0064] The number and arrangement of components shown in Figure 5 is provided as an example. In practice, device 500 may include more, fewer, different, or differently arranged components than those shown in Figure 5. Additionally or alternatively, one set of components (e.g., one or more components) of device 500 may perform one or more functions that are described as being performed by another set of components of device 500.

[0065] In embodiments, any of the operations or processes of Figures 2-3 may be performed by or using any one of the elements shown in Figures 4 and 5. It is understood that other embodiments may be implemented in a variety of different architectures (e.g., without limitation, a bare metal architecture or any cloud-based or deployment architecture such as Kubernetes, Docker, OpenStack, etc.).

[0066] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations.

[0067] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible level of technical detail. Furthermore, one or more of the above components described above may be implemented as instructions stored on a computer-readable medium and executable by at least one processor (and / or may include at least one processor). The computer-readable medium may include a computer-readable non-transitory storage medium (or media) having computer-readable program instructions for causing a processor to perform operations.

[0068] A computer-readable storage medium may be any tangible device that can retain and store instructions for use by an instruction-execution device. A computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or ridge-in-groove structures on which instructions are recorded, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through wires.

[0069] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or storage device over a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage on a computer-readable storage medium within the respective computing / processing device.

[0070] The computer-readable program code / instructions for performing operations may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, Instruction-Set-Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or object-oriented programming languages ​​such as Smalltalk and C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) can execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry to perform an aspect or operation.

[0071] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, executed by the processor of the computer or other programmable data processing apparatus, form means for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may also be stored on a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that a computer-readable storage medium having instructions stored therein includes an article of manufacture containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0072] These computer-readable program instructions may also be loaded into a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable data processing apparatus, or other device to perform a series of operational steps to generate a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device perform the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0073] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each block in a flowchart or block diagram may represent a portion of a microservice, module, segment, or instruction set, which includes one or more executable instructions for implementing the specified logical function(s). The methods, computer systems, and computer-readable media may include more, fewer, different, or differently arranged blocks than those shown in the figures. In some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the figures. For example, two blocks shown in succession may actually be executed concurrently or substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or acts or perform a combination of dedicated hardware and computer instructions.

[0074] It will be apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0075] Various aspects of the embodiments

[0076] Various further respective aspects and features of embodiments of the present disclosure can be defined by the following clauses. Item [1]: A method including: receiving, by a Federated O-Cloud Orchestration and Management (FOCOM), a recommendation from an rApp to shut down an identified O-Cloud node; sending, by FOCOM, an instruction to a Network Function Orchestrator (NFO) to move workloads from the identified O-Cloud node to an alternative O-Cloud node based on the recommendation; and sending, by FOCOM, an instruction to an Infrastructure Management Services (IMS) to shut down the identified O-Cloud node. Item [2]: The method described in Item [1], in which the rApp is subscribed to O1-related data from Operations, Administration, and Maintenance (OAM) and O2-related data from FOCOM. Item [3]: The method described in Item [2], wherein the rApp generates recommendations using a machine learning model based on collected data from O1-related data and O2-related data, the O1-related data and O2-related data relating to traffic patterns and resource usage. Item [4]: ​​The method according to any one of items [1] to [3], wherein upon receiving an instruction to move a workload from FOCOM, the NFO is configured to check the availability of an alternative O cloud node, and if the NFO can find an alternative O cloud node, the NFO is configured to send an instruction to move the workload to a Deployment Management Service (DMS), and if the NFO cannot find an alternative O cloud node, the NFO is configured to instead send an error message to FOCOM. Item [5]: The method according to item [4], wherein upon receiving an instruction to move the workload from the FOCOM, the NFO is configured to send an instruction to Operations, Administration, and Maintenance (OAM) to drain traffic from the identified O-cloud node. Item [6]: The method described in Item [5], wherein the instruction to the IMS to shut down the identified cloud node is sent when the DMS receives confirmation from the NFO that the workload has been moved. Item [7]: A method according to any one of items [1] to [6], wherein the rApp, FOCOM, and NFO each communicate with a Service Management and Exposure (SME) and a Data Management and Exposure (DME) to receive information relating to at least one of services, endpoints, and supported data types. Item [8]: Federated O-Cloud Orchestration and Management (FOCOM) configured to receive a recommendation from the rApp to shut down the identified O-Cloud node, send an instruction to a Network Function Orchestrator (NFO) to move workloads from the identified O-Cloud node to an alternative O-Cloud node based on the recommendation, and send an instruction to Infrastructure Management Services (IMS) to shut down the identified O-Cloud node. Item [9]: The rApp subscribes to O1 related data from Operations, Administration, and Maintenance (OAM) and O2 related data from FOCOM, as described in item [8]. Item

[10] : The rApp generates recommendations using machine learning models based on collected data from O1-related data and O2-related data, where the O1-related data and O2-related data are related to traffic patterns and resource usage. FOCOM as described in Item [9]. Item

[11] : A FOCOM according to any one of items [8] to

[10] , wherein upon receiving an instruction to move a workload from the FOCOM, the NFO is configured to check the availability of an alternative cloud node, and if the NFO can find an alternative cloud node, the NFO is configured to send an instruction to move the workload to a Deployment Management Service (DMS), and if the NFO cannot find an alternative cloud node, the NFO is configured to instead send an error message to the FOCOM. Item

[12] : The FOCOM of item

[11] , wherein upon receiving an instruction to move the workload from the FOCOM, the NFO is configured to send an instruction to Operations, Administration, and Maintenance (OAM) to drain traffic from the identified O-cloud node. Item

[13] : The FOCOM described in Item

[12] , wherein the instruction to the IMS to shut down the identified cloud node is sent when the DMS receives confirmation from the NFO that the workload has been moved. Item

[14] : The rApp, FOCOM, and NFO each communicate with a Service Management and Exposure (SME) and a Data Management and Exposure (DME) to receive information related to at least one of services, endpoints, and supported data types. FOCOM according to any one of items [8] to

[13] . Item

[15] : A non-transitory computer-readable storage medium having recorded thereon instructions for performing a method including receiving, by a Federated O-Cloud Orchestration and Management (FOCOM), a recommendation from an rApp to shut down an identified O-Cloud node; sending, by FOCOM, an instruction to a Network Function Orchestrator (NFO) to move workloads from the identified O-Cloud node to an alternative O-Cloud node based on the recommendation; and sending, by FOCOM, an instruction to an Infrastructure Management Services (IMS) to shut down the identified O-Cloud node. Item

[16] : The non-transitory computer-readable record of Item

[15] , wherein the rApp is subscribed to O1-related data from Operations, Administration, and Maintenance (OAM) and O2-related data from FOCOM. Item

[17] : The non-transitory computer-readable record of Item

[16] , wherein the rApp generates recommendations using a machine learning model based on collected data from O1-related data and O2-related data, the O1-related data and O2-related data relating to traffic patterns and resource usage. Item

[18] : Upon receiving an instruction to move a workload from FOCOM, the NFO is configured to check the availability of an alternative O cloud node, and if the NFO can find an alternative O cloud node, the NFO is configured to send an instruction to move the workload to a Deployment Management Service (DMS), and if the NFO cannot find an alternative O cloud node, the NFO is configured to instead send an error message to FOCOM. A non-transitory computer-readable record according to any one of items

[15] to

[17] . Item

[19] : The non-transitory computer-readable record of Item

[18] , wherein upon receiving an instruction to move the workload from the FOCOM, the NFO is configured to send an instruction to Operations, Administration, and Maintenance (OAM) to drain traffic from the identified O-cloud node. Item

[20] : The non-transitory computer-readable record of Item

[19] , wherein the instruction to the IMS to shut down the identified O cloud node is sent when the DMS receives confirmation from the NFO that the workload has been moved.

[0077] It may be appreciated that many modifications and variations of the present disclosure are possible in light of the above teachings. It will be apparent that, within the scope of the appended claims, the present disclosure may be practiced otherwise than as specifically described herein.

Claims

1. receiving, via a Federated O-Cloud Orchestration and Management (FOCOM), a recommendation from the rApp to shut down the identified O-Cloud node; sending, by the FOCOM, a command to a network function orchestrator (NFO) to move workloads from the identified O cloud node to an alternative O cloud node based on the recommendation; and sending, by the FOCOM, an instruction to an Infrastructure Management Service (IMS) to shut down the identified O cloud node. method.

2. The method of claim 1 , wherein the rApp is subscribed to O1-related data from Operations, Administration, and Maintenance (OAM) and O2-related data from the FOCOM.

3. The method of claim 2 , wherein the rApp generates the recommendation using a machine learning model based on collected data from the O1-related data and the O2-related data, the O1-related data and the O2-related data relating to traffic patterns and resource usage.

4. Upon receiving the instruction to transfer the workload from the FOCOM, the NFO is configured to check the availability of the alternative cloud node; If the NFO can find an alternative cloud node, the NFO is configured to send an instruction to a deployment management service (DMS) to move the workload; The method of claim 1 , wherein if the NFO is unable to find an alternative O cloud node, the NFO is configured to instead send an error message to the FOCOM.

5. 5. The method of claim 4, wherein upon receiving the instruction to move the workload from the FOCOM, the NFO is configured to send an instruction to an Operations, Administration, and Maintenance (OAM) to drain traffic from the identified O cloud node.

6. The method of claim 5 , wherein the instruction to the IMS to shut down the identified O cloud node is sent upon receiving confirmation from the NFO that the DMS has moved the workload.

7. 2. The method of claim 1, wherein the rApp, the FOCOM, and the NFO communicate with a Service Management and Exposure (SME) and a Data Management and Exposure (DME), respectively, to receive information related to at least one of services, endpoints, and supported data types.

8. receiving a recommendation from rApp to shut down the identified O cloud node; Sending instructions to a network function orchestrator (NFO) to move workloads from the identified O cloud node to an alternative O cloud node based on the recommendation; and configured to send an instruction to an Infrastructure Management Service (IMS) to shut down the identified cloud node; Federated Cloud Orchestration and Management (FOCOM).

9. 9. The FOCOM of claim 8, wherein the rApp is subscribed to O1 related data from Operations, Administration and Maintenance (OAM) and O2 related data from the FOCOM.

10. 10. The FOCOM of claim 9, wherein the rApp generates the recommendation using a machine learning model based on collected data from the O1-related data and the O2-related data, the O1-related data and the O2-related data relating to traffic patterns and resource usage.

11. Upon receiving the instruction to transfer the workload from the FOCOM, the NFO is configured to check the availability of the alternative cloud node; If the NFO can find an alternative cloud node, the NFO is configured to send an instruction to a deployment management service (DMS) to move the workload; The FOCOM of claim 8 , wherein if the NFO is unable to find an alternative O cloud node, the NFO is configured to instead send an error message to the FOCOM.

12. 12. The FOCOM of claim 11, wherein upon receiving the instruction to move the workload from the FOCOM, the NFO is configured to send an instruction to an Operations, Administration, and Maintenance (OAM) to drain traffic from the identified O cloud node.

13. 13. The FOCOM of claim 12, wherein the instruction to the IMS to shut down the identified O cloud node is sent upon receiving confirmation from the NFO that the DMS has moved the workload.

14. 9. The FOCOM of claim 8, wherein the rApp, the FOCOM, and the NFO each communicate with a Service Management and Exposure (SME) and a Data Management and Exposure (DME) to receive information related to at least one of services, endpoints, and supported data types.

15. receiving, via a Federated O-Cloud Orchestration and Management (FOCOM), a recommendation from the rApp to shut down the identified O-Cloud node; sending, by the FOCOM, a command to a network function orchestrator (NFO) to move workloads from the identified O cloud node to an alternative O cloud node based on the recommendation; and sending, by the FOCOM, an instruction to an Infrastructure Management Service (IMS) to shut down the identified O cloud node.

16. 16. The non-transitory computer-readable storage medium of claim 15, wherein the rApp is subscribed to O1 related data from Operations, Administration and Maintenance (OAM) and O2 related data from the FOCOM.

17. 17. The non-transitory computer-readable storage medium of claim 16, wherein the rApp generates the recommendation using a machine learning model based on collected data from the O1-related data and the O2-related data, the O1-related data and the O2-related data relating to traffic patterns and resource usage.

18. Upon receiving the instruction to transfer the workload from the FOCOM, the NFO is configured to check the availability of the alternative cloud node; If the NFO can find an alternative cloud node, the NFO is configured to send an instruction to a deployment management service (DMS) to move the workload; 16. The non-transitory computer-readable storage medium of claim 15, wherein if the NFO is unable to find an alternative O cloud node, the NFO is configured to instead send an error message to the FOCOM.

19. 20. The non-transitory computer-readable storage medium of claim 18, wherein upon receiving the instruction to move the workload from the FOCOM, the NFO is configured to send an instruction to an Operations, Administration, and Maintenance (OAM) to drain traffic from the identified O cloud node.

20. 20. The non-transitory computer-readable storage medium of claim 19, wherein the instruction to the IMS to shut down the identified O cloud node is sent upon receiving confirmation from the NFO that the DMS has moved the workload.

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