MVR: An Architecture for Computation Offloading in Mobile Edge Computing

Wei, Xiaolin; Wang, Shangguang; Zhou, Aoying; Xu, Jinliang; Sen, Sandip; Kumar, Sathish A.P.; Yang, Fangchun

doi:10.1109/ieee.edge.2017.42

Cited by 73 publications

(35 citation statements)

References 17 publications

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“…In [9], proposed system to leverage from the ability of computing and storage capacity available in the edge servers. In [10], a new computation offloading model in MEC was introduced. Its principle is to enable the use of virtual resources in the edge cloud to reduce resource and energy consumption and improve the performance of the application.…”

Section: Related Workmentioning

confidence: 99%

Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

et al. 2020

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In recent years, computation offloading has become an effective way to overcome the constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive mobile application tasks to remote cloud-based data centers. Smart cities can benefit from offloading to edge points in the framework of the so-called cyber-physical-social systems (CPSS), as for example in traffic violation tracking cameras. We assume that there are mobile edge computing networks (MECNs) in more than one region, and they consist of multiple access points, multi-edge servers, and N MDs, where each MD has M independent real-time massive tasks. The MDs can connect to a MECN through the access points or the mobile network. Each task be can processed locally by the MD itself or remotely. There are three offloading options: nearest edge server, adjacent edge server, and remote cloud. We propose a reinforcementlearning-based state-action-reward-state-action (RL-SARSA) algorithm to resolve the resource management problem in the edge server, and make the optimal offloading decision for minimizing system cost, including energy consumption and computing time delay. We call this method OD-SARSA (offloading decision-based SARSA). We compared our proposed method with reinforcement learning based Q learning (RL-QL), and it is concluded that the performance of the former is superior to that of the latter. INDEX TERMS Mobile devices, edge computing, mobile edge computing, edge cloud computing, virtual machines, access points.

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Section: Related Workmentioning

confidence: 99%

Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

et al. 2020

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“…Chun et al [9] proposed the CloneCloud framework, which enables more effective offloading through virtualization-based synchronization of program execution environment between the user terminal and server. More recent work focuses on establishing the application offloading process for augmented reality through mobile virtual reality [12].…”

Section: Previous Workmentioning

confidence: 99%

Comparative Analysis of 5G Mobile Communication Network Architectures

et al. 2020

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Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.

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“…Therefore, an advantage of Edge computing is to provide resources near end users/nodes so to reduce long execution time and end-node battery consumption. A solution that facilitates offloading of complex services from a mobile node to an edge computing server has introduced a model that provides the use of virtual resources in the edge servers [5]. It achieves this by shifting the service execution from a single mobile node to the edge servers automatically, by dividing a single task to 5 subtasks using 0-1 integer liner programming method.…”

Section: Recent Literature On Edge Computing Implementationsmentioning

confidence: 99%

SOSE: Smart Offloading Scheme Using Computing Resources of Nearby Wireless Devices for Edge Computing Services

Al-ameri

Lami

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Offloading of all or part of any cloud service computation, when running processing-intensive mobile cloud computing services (MCCS), to servers in the cloud introduces time delay and communication overhead. Edge computing has emerged to resolve these issues, by shifting part of the service computation from the cloud to Edge servers near the end-devices. An innovative Smart Cooperative Computation Offloading Framework (SCCOF) to leverage computation offloading to the cloud has been previously published by us [1]. This paper proposes SOSE; a solution to offload sub-tasks to nearby devices, on-the-go, that will form an "edge computing resource, we call SOSE_EDGE" so to enable the execution of the MCCS on any end-device. This is achieved by using short-range wireless connectivity to network between available cooperative end-devices. SOSE can partition the MCCS workload to execute among a pool of Offloadees (nearby end-devises such as Smartphones, tablets, and PC's) to achieve minimum latency and improve performance while reducing energy consumption of the Offloader (end-device that is running the MCCS). SOSE established the Edge computing resource by: (1) Profiling and partitioning the service workload to subtasks based on a complexity relationship we developed, (2) establishing peer2peer remote connection with the available cooperative nearby Offloadees based on SOSE assessment criteria, (3) migrating the subtasks to the target edge devices in parallel and retrieve results. Scenarios and experiments to evaluate SOSE show that a significant improvement in terms of execution time (>40%) and energy consumption (>28%) has been achieved when compared with cloud offloading solutions.

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MVR: An Architecture for Computation Offloading in Mobile Edge Computing

Cited by 73 publications

References 17 publications

Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

Comparative Analysis of 5G Mobile Communication Network Architectures

SOSE: Smart Offloading Scheme Using Computing Resources of Nearby Wireless Devices for Edge Computing Services

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