Distributed Multiuser Computation Offloading for Cloudlet-Based Mobile Cloud Computing: A Game-Theoretic Machine Learning Approach

Cao, Huijin; Cai, Jun

doi:10.1109/tvt.2017.2740724

Cited by 151 publications

(59 citation statements)

References 39 publications

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“…Multiple Users with Single Task -There are multiple users, each has a single task. Cao and Cai investigated the problem of multi-user computation offloading for cloudlet based mobile cloud computing in a multi-channel wireless contention environment, by formulating the multi-user computation offloading decision making problem as a non-cooperative game, where each mobile device user has one computation task with the same number of CPU cycles and attempts to minimize a weighted sum of execution time and energy consumption [5]. Chen formulated a decentralized computation offloading decision making problem among mobile device users as a decentralized computation offloading game, where each mobile device user has a computationally intensive and delay sensitive task and minimizes a weighted sum of computational time and energy consumption [8].…”

Section: Related Researchmentioning

confidence: 99%

Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing

2024

IEEE Trans. Sustain. Comput.

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Computation offloading from a user equipment (UE) to a mobile edge cloud (MEC) is an effective way to ease the computational burden of mobile devices, to improve the performance of mobile applications, to reduce the energy consumption and to extend the battery lifetime of mobile user equipments. In this paper, we consider computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing. Queueing models are established for a UE and multiple heterogeneous servers from different MECs, and the average task response time of the UE and each MEC server and the average response time of all offloadable and non-offloadable tasks generated on the UE are rigorously analyzed. Three multi-variable optimization problems are formulated, i.e., minimization of average response time with average power consumption constraint, minimization of average power consumption with average response time constraint, and minimization of cost-performance ratio, so that computation offloading strategy optimization, power-performance tradeoff, as well as power-time product can all be studied in the context of load balancing. An efficient numerical method (which consists of a series of fast numerical algorithms) is developed to solve the problems of minimization of average response time with average power consumption constraint, minimization of average power consumption with average response time constraint, and minimization of cost-performance ratio. Numerical examples and data are also demonstrated to show the effectiveness of our method and to show the power-performance tradeoff, the power-time product, and the impact of various parameters. To the best of the author's knowledge, this is the first work in the literature that analytically addresses computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing. Index Terms-Average response time, computation offloading strategy, cost-performance ratio, mobile edge cloud, mobile edge computing, power consumption, power-performance tradeoff, queueing model.

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Section: Related Researchmentioning

confidence: 99%

Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing

2024

IEEE Trans. Sustain. Comput.

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“…However, the cloud and the device side are usually very far; the migration between applications and the equipment side always results in a high migration delay. In order to address this challenge, the concept of "cloudlet" is proposed and studied, for example, [17,[45][46][47][48], all describe cloudlet computing and related technologies.…”

Section: Related Workmentioning

confidence: 99%

“…In [17], the nearest cloudlet acts as a proxy server, which selects the optimum cloudlet among its nearby cloudlets, with respect to minimum power consumption or minimum latency or both. Cao et al [45] presented a full distributed computation offloading algorithm based on machine learning, which can solve the multi-users Fig. 6 The number of required cloudlets after a time period of t computation offloading problem based on cloudlet in multi-channel wireless contention environment.…”

Section: Related Workmentioning

confidence: 99%

ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

Shen

Xue

2019

J Wireless Com Network

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The development of mobile cloud computing has greatly improved the computing and storage performance of mobile devices. And mobile cloud computing is undoubtedly the necessary way to solve the performance of the process for mobile applications with high performance requirements. However, migrating the mobile applications to the cloud brings about a migration delay, which is intolerable for high real-time demanding applications. This can be technically achieved by expanding mobile cloudlets, co-located with access points (AP). Then, how to deploy the cloudlet to reduce the energy consumption has become a part of major challenges in the current study. In this paper, we propose an energy-efficient cloudlet placement method, named ECPM, to effectively reduce the number of cloudlets, so as to achieve the energy savings. Specifically, the clustering of mobile devices at each time is obtained, and then the cloudlet will move to the clustering to achieve the best use of energy. Finally, the experimental results demonstrate that the proposed method is energy saving.

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“…Roy et al designed an application‐aware selection strategy for the cloudlet scenario, which can reduce the energy consumption of mobile devices and the latency for application. Cao and Cai studied the multiuser computation offloading problem based on cloudlet in multichannel wireless contention environment and proposed a full distributed computation offloading algorithm based on machine learning technology. Theoretically, the proposed full distributed computation offloading algorithm can effectively optimize the performance of mobile devices and system‐wide execution costs.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective computation offloading for workflow management in cloudlet‐based mobile cloud using NSGA‐II

Yuan

et al. 2018

Computational Intelligence

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Cloudlet is a novel computing paradigm, introduced to the mobile cloud service framework, which moves the computing resources closer to the mobile users, aiming to alleviate the communication delay between the mobile devices and the cloud platform and optimize the energy consumption for mobile devices. Currently, the mobile applications, modeled by the workflows, tend to be complicated and computation‐intensive. Such workflows are required to be offloaded to the cloudlet or the remote cloud platform for execution. However, it is still a key challenge to determine the offloading resolvent for the deadline‐constrained workflows in the cloudlet‐based mobile cloud, since a cloudlet often has limited resources. In this paper, a multiobjective computation offloading method, named MCO, is proposed to address the above challenge. Technically, an energy consumption model for the mobile devices is established in the cloudlet‐based mobile cloud. Then, a corresponding computation offloading method, by improving Nondominated Sorting Genetic Algorithm II, is designed to achieve the goal of energy saving for all the mobile device while satisfying the deadline constraints of the workflows. Finally, extensive experimental evaluations are conducted to demonstrate the efficiency and effectiveness of our proposed method.

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Distributed Multiuser Computation Offloading for Cloudlet-Based Mobile Cloud Computing: A Game-Theoretic Machine Learning Approach

Cited by 151 publications

References 39 publications

Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing

Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing

ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

Multiobjective computation offloading for workflow management in cloudlet‐based mobile cloud using NSGA‐II

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