Task Scheduling with Dynamic Voltage and Frequency Scaling for Energy Minimization in the Mobile Cloud Computing Environment

Lin, Xue; Wang, Yanzhi; Xie, Qing; Pedram, Massoud

doi:10.1109/tsc.2014.2381227

Cited by 221 publications

(111 citation statements)

References 15 publications

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“…Due to the fact that the computation of tasks on the P-MEC server does not consume any energy of mobile devices, similar to [29], we ignore the computation energy consumption of the P-MEC server. If U i is executed in mode j, the computation energy consumption is given as [28]…”

Section: A Computation Modelmentioning

confidence: 99%

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Huang

Wang

et al. 2020

IEEE Trans. Cybern.

105

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This paper studies a multi-user cooperative mobile edge computing offloading (CoMECO) system in a multi-user interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system.

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Section: A Computation Modelmentioning

confidence: 99%

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Huang

Wang

et al. 2020

IEEE Trans. Cybern.

105

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“…Lin, et al have presented a task scheduling framework to reduce energy consumption under time constraints in the mobile cloud environment. Energy minimization is achieved by a linear‐time rescheduling algorithm.…”

Section: Analysis Of Task Scheduling Approachesmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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Summary Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.

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“…In [10], the DVFS is adopted to minimize the power consumed in mobile cloud task scheduling, except that this approach does not consider the On and Off control of the servers. In the study [11] the three approaches including DVFS, request dispatching, and dynamic service management are joined so as to reduce the energy consumption.…”

Section: Related Work On Green Cloud Computingmentioning

confidence: 99%

Green Cloud Computing: Efficient Energy-Aware and Dynamic Resources Management in Data Centers

Diouani¹,

Medromi²

2018

ijacsa

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Abstract-The users of Cloud computing over the last years are constantly increasing since it has become a very important technology in the computing landscape. It provides client decentralized services and a pay-as-you-go model for consuming resources. The growing need for the cloud services oblige the providers to adopt an enlarged sized data center infrastructure which runs thousands of hosts and servers to store and process data. As a result, these large servers engender a lot of heat with visual carbon emission in the air, as well as important energy consumption and higher operating cost. This is why researches in energy economics continue to progress including energy saving techniques in servers, in the network, cooling, and renewable energies, etc. In this paper, we tackled the existing energy efficient methods in the green cloud computing fields and we put forward our green cloud solution for data center dynamic resource management. Our proposed approach aims to reduce the infrastructure energy consumption and maintain the required performances.

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Task Scheduling with Dynamic Voltage and Frequency Scaling for Energy Minimization in the Mobile Cloud Computing Environment

Cited by 221 publications

References 15 publications

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Task scheduling mechanisms in cloud computing: A systematic review

Green Cloud Computing: Efficient Energy-Aware and Dynamic Resources Management in Data Centers

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