A Hierarchical Game for Joint Wireless and Cloud Resource Allocation in Mobile Edge Computing System

Lan, Zhuorui; Xia, Weiwei; Cui, Wenqing; Yan, Feng; Shen, Fei; Zuo, Xuzhou; Shen, Lianfeng

doi:10.1109/wcsp.2018.8555606

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…In [19], the authors proposed an auction-based resource allocation problem in the hierarchical mobile edge computing where they tired to maximize the profit of the service providers. Moreover, the work in [20] proposed the radio and computation resource allocation problem in the multi-access edge computing system. Then, the authors decomposed the proposed problem into upper and lower problems.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Communication and Computation Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond

et al. 2020

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Multi-access edge computing (MEC) enables mobile users to offload their computation tasks to the server located at the edge of the cellular network. Thereby, MEC prolongs the battery lifespan of mobile devices and significantly enhances their computation capacities. However, a significant challenge is to offload the computation tasks to the MEC server in an energy-efficient manner. Meanwhile, in the fifth-generation (5G) networks, mobile users with different service requirements classified as enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users will coexist in the current cellular network. Therefore, it is important to appropriately multiplex these users in the cellular network. In this work, we address the issues of these two promising technologies together. Firstly, we formulate an energy-efficient task offloading, and scheduling of eMBB and URLLC users as a mixedinteger non-linear problem. Then, we decompose the problem into multiple sub-problems in order to transform into convex form and alternately solve them until converging to the desired solutions by using the block coordinate descent (BCD) algorithm. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed algorithm over classical existing schemes. INDEX TERMS Multi-access edge computing (MEC), resource slicing, eMBB-URLLC coexistence, block coordinate descent (BCD).

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Communication and Computation Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond

et al. 2020

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“…, which is composed of three parts [25], where Δ ul i indicates the uplink communication time, Δ dl i is the the downlink time, and Δ bh i is the the backhaul link time. According to [26,27], Δ bh i can be infinitesimal, and the downlink time Δ dl i can usually be a constant ξ. erefore, Δ i can be expressed as…”

Section: Problem Formulationmentioning

confidence: 99%

“…where η i p � λ/disk(i, p) is decided by the distance between the task (user) and the server; as the distance increases, the bit error rate increases and the average transmission speed decreases [27]. And, ω p indicates the averaged bandwidth of the server p and d i is the the data size of task i.…”

Section: Problem Formulationmentioning

confidence: 99%

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Wang

Zheng

Chen

et al. 2020

Security and Communication Networks

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Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

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Multi-agent Reinforcement Learning for Joint Wireless and Computational Resource Allocation in Mobile Edge Computing System

et al. 2019

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A Hierarchical Game for Joint Wireless and Cloud Resource Allocation in Mobile Edge Computing System

Cited by 8 publications

References 12 publications

Energy Efficient Communication and Computation Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond

Energy Efficient Communication and Computation Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Multi-agent Reinforcement Learning for Joint Wireless and Computational Resource Allocation in Mobile Edge Computing System

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