A fast hybrid multi-site computation offloading for mobile cloud computing

Goudarzi, Mohammad; Zamani, Mehran; Haghighat, Abolfazl Toroghi

doi:10.1016/j.jnca.2016.12.031

Cited by 69 publications

(35 citation statements)

References 22 publications

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“…This fact has resulted in a growing interest in studying efficient computation offloading strategies. The existing literatures on computation-intensive application offloading can be roughly divided into two categories: 1) those where applications are directly mapped as bit streams and considered as a collection of sub-applications without considering the their dependencies, such as [17]- [19]; 2) those explicitly considering the structure of applications which can be modeled as directed/undirected graphs, such as [6], [7], [9], [10], [20], [21]. A reliability-oriented stochastic optimization model in vehicle-infrastructure systems is proposed in [17] based on the dynamic programming for computation offloading considering the deadline constraint on application execution.…”

Section: B Related Workmentioning

confidence: 99%

“…The authors in [20] focus on scheduling parallel jobs composed of a set of independent tasks and consider energy consumption while minimizing job completion time. A fast hybrid multi-site computation offloading mechanism is proposed in [21], which finds the offloading solution in a timely manner by considering the application size, where applications are modeled as weighted relation graphs.…”

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Liwang

Hosseinalipour

Gao

et al. 2020

IEEE Internet Things J.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Liwang

Hosseinalipour

Gao

et al. 2020

IEEE Internet Things J.

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“…Enzai and Tang (2016) implemented hill climbing algorithm to offload computational task in multisite cloud servers with decreased energy consumption, computation time and total computational cost without considering the scalability factor. Mohammad Goudarzi et al (2017) proposed fast hybrid multisite computational offloading (FHMCO) by implementing particle swarm optimization algorithm to obtain near optimal offloading solution with decreased cost, energy and time factors. They proved their solutions on applications like JESS, DB and RayTrace.…”

Section: Review Of Literaturementioning

confidence: 99%

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

Veeraiyan¹,

Kousika²,

Senthilkumar³

et al. 2020

Journal of Computer Science

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Offloading the execution of heavy computational modules from mobile devices to Mobile Cloud Computing (MCC) is inevitable in today's era as it mainly focuses in consuming less battery power and execution time. But, the problem incurred with identifying the most optimal cloud device to map each module still remains a challenge in cloud computing environment. In this paper, a novel MCC offloading framework is proposed to fasten the allocation and execution of high computational modules that runs in the mobile device, effectively on the cloud. The framework employs K-Means clustering algorithm to group the nearest cloud virtual machines that best suits for executing modules of software running in the mobile. The objective of the paper is to maximize the energy savings by extending the battery life and execution speed of mobile device when executing heavy computational modules. The optimal selection of cloud device is attained by grouping the requirements of each module with the nearest cloud devices offering the same requirements using K-Means Algorithm. The proposed framework is compared with the existing mobile computation offloading frameworks with respect to energy saving, execution time and energy consumption. The results show that the proposed work executes the modules of computationally intensive modules in minimum time span with maximized energy savings than the existing frameworks.

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“…There are some researchers who have contributed to the study of multi-objective problems as well. In [8], Goudarzi et al proposed a weighted cost model based on execution time and energy consumption to achieve a tradeoff between them for offloading. To solve this problem, a branch-and-bound algorithm with an optimal branching rule, a fast search strategy and an appropriate bounding function, was firstly designed for the small-scale applications, and then an improved Particle Swarm Optimization (PSO) algorithm was developed to achieve the best-possible offloading solution for the large-scale applications.…”

Section: Related Workmentioning

confidence: 99%

A Constrained Multi-objective Computation Offloading Algorithm in the Mobile Cloud Computing Environment

2019

KSII TIIS

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Mobile cloud computing (MCC) can offload heavy computation from mobile devices onto nearby cloudlets or remote cloud to improve the performance as well as to save energy for these devices. Therefore, it is essential to consider how to achieve efficient computation offloading with constraints for multiple users. However, there are few works that aim at multi-objective problem for multiple users. Most existing works concentrate on only single objective optimization or aim to obtain a tradeoff solution for multiple objectives by simply setting weight values. In this paper, a multi-objective optimization model is built to minimize the average energy consumption, time and cost while satisfying the constraint of bandwidth. Furthermore, an improved multi-objective optimization algorithm called D-NSGA-II-ELS is presented to get Pareto solutions with better convergence and diversity. Compared to other existing works, the simulation results show that the proposed algorithm can achieve better performance in terms of energy consumption, time and cost while satisfying the constraint of the bandwidth.

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A fast hybrid multi-site computation offloading for mobile cloud computing

Cited by 69 publications

References 22 publications

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

An Optimized Mobile Cloud Computational Offloading Framework using K-Means Algorithm

A Constrained Multi-objective Computation Offloading Algorithm in the Mobile Cloud Computing Environment

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