Dear Fatima Aziz,

The data presented below provide some useful information:

Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) system with stochastic wireless channels and task arrivals is considered. In order to minimize long-term average computation cost in terms of power consumption and buffering delay at each user, a deep reinforcement learning (DRL)-based dynamic computation offloading strategy is investigated to build a scalable system with limited feedback. Specifically, a continuous action space based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn decentralized computation offloading policies at all users respectively, where local execution and task offloading powers will be adaptively allocated according to each user’s local observation. Numerical results demonstrate that the proposed DDPG-based strategy can help each user learn an efficient dynamic offloading policy and also verify the superiority of its continuous power allocation capability to policies learned by conventional discrete action space-based reinforcement learning approaches like deep Q-network (DQN) as well as some other greedy strategies with reduced computation cost. Besides, power-delay tradeoff for computation offloading is also analyzed for both the DDPG-based and DQN-based strategies.

https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-020-01801-6

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Decentralized SDN Control Plane for a Distributed Cloud-Edge Infrastructure: A Survey

Today’s emerging needs (Internet of Things applications, Network Function Virtualization services, Mobile Edge computing, etc.) are challenging the classic approach of deploying a few large data centers to provide cloud services. A massively distributed Cloud-Edge architecture could better fit these new trends’ requirements and constraints by deploying on-demand infrastructure services in Point-of-Presences within backbone networks. In this context, a key feature is establishing connectivity among several resource managers in charge of operating, each one a subset of the infrastructure. After explaining the networking management challenges related to distributed Cloud-Edge infrastructures, this article surveys and analyzes the characteristics and limitations of existing technologies in the Software Defined Network field that could be used to provide the inter-site connectivity feature. We also introduce Kubernetes, the new de facto container orchestrator platform, and analyze its use in the proposed context. This survey is concluded by providing a discussion about some research directions in the field of SDN applied to distributed Cloud-Edge infrastructures’ management.

https://hal.archives-ouvertes.fr/hal-03119901/document

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Thanks alot Dr.Len-Mizrah

for share this information with reference

Regards…

Dear Fatima Aziz,

the idea for "Recursive Hierarchical SDN Architecture" might help you.

See:

Figure 13 – Peers as symmetric requestors and providers

https://opennetworking.org/wp-content/uploads/2014/10/TR-521_SDN_Architecture_issue_1.1.pdf

Figure 9548: Recursive hierarchical SDN architecture: a) general idea, b) implementation of handover in SD-IoV

https://www.researchgate.net/publication/341574902_SDN_Software_Defined_Networking

Best regards and much success

Anatol Badach