Task offloading and resource allocation in mobile-edge computing system

Kan, Te-Yi; Chiang, Yao; Wei, Hung‐Yu

doi:10.1109/wocc.2018.8372737

Cited by 63 publications

(23 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Reference [12], the authors used Karush-Kuhn-Tucker (KKT) conditions to solve the problem of optimizing MD energy consumption. Moreover, there were similar studies in single MEC server such as References [30][31][32][33]. For multi-tier offloading of computational tasks [13][14][15][16], there can be one or more SBSs overlaid by an MSB, with the SBSs connected to the MBS by a wireless or wired network.…”

Section: Related Workmentioning

confidence: 92%

Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

Huynh

Pham

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

show abstract

Section: Related Workmentioning

confidence: 92%

Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

Huynh

Pham

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…For the loss function of the Actor network, refer to (15). The loss gradient defined in the article [39] is as follows:…”

Section: B Dynamic Resource Optimization Algorithm Based On Drlmentioning

confidence: 99%

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

As a mode of processing task request, edge computing paradigm can reduce task delay and effectively alleviate network congestion caused by the proliferation of Internet of things(IoT) devices compared with cloud computing. However, in the actual construction of the network, there are various edge autonomous subnets in the adjacent areas, which leads to the possibility of unbalance of server load among autonomous subnets during the peak period of task request. In this paper, a deep reinforcement learning algorithm is proposed to solve the complex computation offloading problem for the heterogeneous Edge Computing Server(ECS) collaborative computing. The problem is solved based on the real-time state of the network and the attributes of the task, which adopts Actor Critic and Policy Gradient's Deep Deterministic Policy Gradient(DDPG) to make optimized decisions of computation offloading. Considering multi-task, the heterogeneity of edge subnet and mobility of edge devices, the proposed algorithm can learn the network environment and generate the computation offloading decision to minimize the task delay.The simulation results show that the proposed DDPG-based algorithm is competitive compared with the Deep Q Network(DQN) algorithm and Asynchronous Advantage Actor-Critic(A3C) algorithm. Moreover, the optimal solutions are leveraged to analyze the influence of edge network parameters on task delay. INDEX TERMS Edge computing, computation offload, collaborative computing, reinforcement learning, DDPG.

show abstract

“…In terms of latency, the literature [22], [23] studied the joint communication and computing resource allocation in a cloud edge collaboration system to minimize the weighting and delay of all devices. Reference [24] improves QoS by offloading computation-intensive tasks to the MEC server, and considers allocating radio resources and computing resources of the MEC server at the same time to improve system efficiency. In addition, the requirements of various tasks are considered, that is, assuming that different tasks may have different delay requirements, the problem is expressed as a cost minimization problem, and a heuristic algorithm is designed to solve the problem.…”

Section: Related Workmentioning

confidence: 99%

Load Optimization Based on Edge Collaboration in Software Defined Ultra-Dense Networks

Yang

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

With the intelligence of user equipment and the popularization of emerging applications such as unmanned driving and face recognition, more and more computationally intensive and delay-sensitive tasks have been generated. As a new network paradigm, ultra-dense networks can greatly improve user access capabilities by deploying dense base stations (BSs). Edge computing can effectively guarantee the low-latency requirements of users in ultra-dense networks. However, the heterogeneity of servers, the distributed resources, and the dynamic energy consumption of mobile devices in ultra-dense networks make it extremely difficult for users to offload and load balance among servers. This paper applies the idea of software defined network to proposes an edge collaboration architecture to achieve resource sharing and efficient offloading of tasks based on the characteristics of global perception. In particular, considering the high load of the local server and the idle resources of the remote server, the best offloading strategy for users is obtained game theory. Simulation results show that the performance is improved by about 30% compared to the traditional local processing, edge offload and local edge random offload schemes. INDEX TERMS Software defined network, ultra dense network, load balancing, edge collaboration. I. INTRODUCTION

show abstract

Task offloading and resource allocation in mobile-edge computing system

Cited by 63 publications

References 9 publications

Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Load Optimization Based on Edge Collaboration in Software Defined Ultra-Dense Networks

Contact Info

Product

Resources

About