Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing

Li, Zhi; Zhu, Qi

doi:10.3390/info11020083

Cited by 61 publications

(17 citation statements)

References 18 publications

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“…Many algorithms exist under EA and are more or less adapted to certain problems with their own pros and cons. For example, genetic algorithms tend to not be trapped in local optima [99,100] while being hard tuning it to problems. Thus, Wan et al [100] propose a different use of EA for task-driven resource assignment, including hybridization of different EA algorithms.…”

Section: Learning Methodsmentioning

confidence: 99%

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

Santi¹,

Mitton²

2021

ITU-J FET

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Multiaccess Edge Computing (MEC) brings additional computing power in proximity of mobile users, reducing latency, saving energy and alleviating the network's bandwidth. This proximity is beneficial, especially for mission-critical applications where each second matters, such as disaster management or military operations. Moreover, it enables MEC resources embedded on mobile units like drones or robots that are flexible to be deployed for mission-critical applications. However, the MEC servers are capacity-limited and thus need an acute management of their resources. The mobile resources also need a smart deployment scheme to deliver their services efficiently. In this survey, we review mission-critical applications, resource allocation and deployment of mobile resources techniques in the context of the MEC. First, we introduce the technical specifics and uses of MEC in mission-critical applications to highlight their needs and requirements. Then, we discuss the resource allocation schemes for MEC and assess their fit depending on the application needs. In the same fashion, we finally review the deployment of MEC mobile resources. We believe this work could serve as a helping hand to design efficient MEC resource management schemes that respond to challenging environments such as mission-critical applications.

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Section: Learning Methodsmentioning

confidence: 99%

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

Santi¹,

Mitton²

2021

ITU-J FET

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“…Li et al. [ 9 ] proposed a joint optimization method of task allocation ratio, channel bandwidth, and computing resources of mobile edge servers based on genetic algorithm, aiming at the situation that part of computing tasks can be partially allocated to the mobile edge server. Under the constraints of wireless transmission resources and mobile edge server processing resources, a genetic algorithm is used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained.…”

Section: Related Workmentioning

confidence: 99%

Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning

et al. 2021

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Edge computing is a new paradigm, which provides storage, computing, and network resources between the traditional cloud data center and terminal devices. In this paper, we concentrate on the application-driven task offloading problem in edge computing by considering the strong dependencies of sub-tasks for multiple users. Our objective is to joint optimize the total delay and energy generated by applications, while guaranteeing the quality of services of users. First, we formulate the problem for the application-driven tasks in edge computing by jointly considering the delays and the energy consumption. Based on that, we propose a novel Application-driven Task Offloading Strategy (ATOS) based on deep reinforcement learning by adding a preliminary sorting mechanism to realize the joint optimization. Specifically, we analyze the characteristics of application-driven tasks and propose a heuristic algorithm by introducing a new factor to determine the processing order of parallelism sub-tasks. Finally, extensive experiments validate the effectiveness and reliability of the proposed algorithm. To be specific, compared with the baseline strategies, the total cost reduction by ATOS can be up to 64.5% on average.

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“…However, the optimization complexity will not be decreased through such a paradigm. Some researchers modeled task offloading in MEC as an NP-hard Knapsack Problem, calculating the optimum resolution by dynamic planning [15], the genetic algorithm [16], or other heuristic algorithms [17]. The calculations for these methods require a vast iteration or matrix manipulation, and it is hard to satisfy the real-time requirement.…”

Section: Motivationmentioning

confidence: 99%

Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing

Shi

et al. 2021

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Using reinforcement learning technologies to learn offloading strategies for multi-access edge computing systems has been developed by researchers. However, large-scale systems are unsuitable for reinforcement learning, due to their huge state spaces and offloading behaviors. For this reason, this work introduces the centralized training and decentralized execution mechanism, designing a decentralized reinforcement learning model for multi-access edge computing systems. Considering a cloud server and several edge servers, we separate the training and execution in the reinforcement learning model. The execution happens in edge devices of the system, and edge servers need no communication. Conversely, the training process occurs at the cloud device, which causes a lower transmission latency. The developed method uses a deep deterministic policy gradient algorithm to optimize offloading strategies. The simulated experiment shows that our method can learn the offloading strategy for each edge device efficiently.

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Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing

Cited by 61 publications

References 18 publications

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

A resource management survey for mission-critical and time-critical applications in multiaccess edge computing

Towards Application-Driven Task Offloading in Edge Computing Based on Deep Reinforcement Learning

Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing

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