Towards cost-efficient resource provisioning with multiple mobile users in fog computing

Lu, Shuaibing; Wu, Jie; Duan, Yubin; Wang, Ning; Fang, Juan

doi:10.1016/j.jpdc.2020.08.002

Cited by 16 publications

(7 citation statements)

References 7 publications

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“…We use D c u i (t) to denote the computing delay of user u i at time slot t. Let A i (t) denote the amount of computing resource required by the service request of user u i at time slot t. In this paper, we consider that each user shares the computing resource of the MEC sever evenly [14,22]. Here, the computing resources are measured by the number of CPU cycles.…”

Section: Computing Delaymentioning

confidence: 99%

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Shi

et al. 2022

Network

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Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency.

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Section: Computing Delaymentioning

confidence: 99%

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Shi

et al. 2022

Network

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“…Moreover, a constant Internet connection is not required for a fog computing paradigm to offer effective service. Those services can act autonomously with weak or no Internet connection and transfer significant update to the cloud network whenever connectivity is convenient [29]. Fog computing framework supports end-users to monitor, analyze, process, measure, distribute and control computation, provide storage, communication, and decision-making near the IoT nodes [30].…”

Section: B Motivationmentioning

confidence: 99%

“…As per the current scenario, there are various challenges related to the design, development, and deployment of fog computing frameworks [35,26,12,28,29,18,19]. The main challenges are based on the design phase: a) to design Service Level Agreement (SLA) management methods for the support of multi-vendor environment for fog computing systems, b) to introduce a scheme that considers multiple objectives such as latency, energy and bandwidth (waiting time, bandwidth, and availability for adequate task offloading techniques), c) to design scalable algorithms for the fog computing framework and also verify the scalability of the algorithms.…”

Section: B Motivationmentioning

confidence: 99%

Fog computing: A taxonomy, systematic review, current trends and research challenges

Singh

Gill

2021

Journal of Parallel and Distributed Computing

104

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“…Lu et al. [ 13 ] focus on minimizing the total cost for multiple mobile users to provide an efficient resource provisioning scheme by considering three different cases in edge computing. Yu et al.…”

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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Towards cost-efficient resource provisioning with multiple mobile users in fog computing

Cited by 16 publications

References 7 publications

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Fog computing: A taxonomy, systematic review, current trends and research challenges

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

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