Edge Computing
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2021 ◽  
Vol 191 ◽  
pp. 106495
Author(s):  
Weizheng Shen ◽  
Yalin Sun ◽  
Yu Zhang ◽  
Xiao Fu ◽  
Handan Hou ◽  
...  

2021 ◽  
Vol 20 (6) ◽  
pp. 1-33
Author(s):  
Kaustabha Ray ◽  
Ansuman Banerjee

Multi-Access Edge Computing (MEC) has emerged as a promising new paradigm allowing low latency access to services deployed on edge servers to avert network latencies often encountered in accessing cloud services. A key component of the MEC environment is an auto-scaling policy which is used to decide the overall management and scaling of container instances corresponding to individual services deployed on MEC servers to cater to traffic fluctuations. In this work, we propose a Safe Reinforcement Learning (RL)-based auto-scaling policy agent that can efficiently adapt to traffic variations to ensure adherence to service specific latency requirements. We model the MEC environment using a Markov Decision Process (MDP). We demonstrate how latency requirements can be formally expressed in Linear Temporal Logic (LTL). The LTL specification acts as a guide to the policy agent to automatically learn auto-scaling decisions that maximize the probability of satisfying the LTL formula. We introduce a quantitative reward mechanism based on the LTL formula to tailor service specific latency requirements. We prove that our reward mechanism ensures convergence of standard Safe-RL approaches. We present experimental results in practical scenarios on a test-bed setup with real-world benchmark applications to show the effectiveness of our approach in comparison to other state-of-the-art methods in literature. Furthermore, we perform extensive simulated experiments to demonstrate the effectiveness of our approach in large scale scenarios.


2022 ◽  
Vol 18 (2) ◽  
pp. 1
Author(s):  
Seifedine Kadry ◽  
Karrar Hameed Abdulkareem ◽  
Abdullah Lakhan ◽  
Mazin Abed Mohammed ◽  
Ahmed N. Rashid

2022 ◽  
Vol 22 (1) ◽  
pp. 1-27
Author(s):  
Zhenyu Fan ◽  
Wang Yang ◽  
Fan Wu ◽  
Jing Cao ◽  
Weisong Shi

Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.


2021 ◽  
Author(s):  
Chetan Sharma ◽  
Samuel R. Browd ◽  
Maya Sharma

The paper provides insights into the emerging field of VR Surgery which is a combination of emerging areas in Computer Science like VR, 5G, and Edge Computing and Medicine areas like Surgery and Education. The data mentioned in the paper relates to 5G growth in the US, Edge Computing demands on the network and the new architectures that will be needed to make such systems work end-to-end.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hui Luo ◽  
Quan Yin

Driven by the development of the Internet industry, mobile robots (MRs) technology has become increasingly mature and widely used in all walks of life. Since MRs are densely distributed in the network system, how to establish a reliable communication architecture to achieve good cooperation and resource sharing between MRs has become a research hotspot. In this respect, mobile edge computing (MEC) technology and millimeter wave (mmW) technology can provide powerful support. This paper proposes a mmW communication network architecture for distributed MRs in MEC environment. The mmW base station provides reliable communication services for MRs under the coverage of information cloud (IC). We design a joint resource and power allocation strategy aimed at minimizing network energy consumption. First, we use the Lyapunov optimization technique to transform the original infinite horizon Markov decision process (MDP) problem. Then, a semidistributed algorithm is introduced to solve the distributed optimization problem in the mmW network. By improving the autonomous decision-making ability of the mmW base station, the signaling overheads caused by information interaction are reduced, and information leakage is effectively avoided. Finally, the global optimal solution is obtained. Simulation results demonstrate the superiority of the proposed strategy.


2021 ◽  
Author(s):  
Chetan Sharma ◽  
Samuel R. Browd ◽  
Maya Sharma

The paper provides insights into the emerging field of VR Surgery which is a combination of emerging areas in Computer Science like VR, 5G, and Edge Computing and Medicine areas like Surgery and Education. The data mentioned in the paper relates to 5G growth in the US, Edge Computing demands on the network and the new architectures that will be needed to make such systems work end-to-end.


2022 ◽  
Vol 27 (2) ◽  
pp. 315-324
Author(s):  
Chao Yan ◽  
Yankun Zhang ◽  
Weiyi Zhong ◽  
Can Zhang ◽  
Baogui Xin

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