An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud
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An energy‑efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud Yun Lin1 · Tundong Liu2 · Fufeng Chen3 · Kuan‑Ching Li4 · Yi Xie5 Accepted: 18 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The limitations of mobile devices have attracted researchers to work out an energyefficient mechanism to enhance user experience. The emerging mobile cloud computing (MCC) provides a new approach to solve this problem. Some parts of mobile applications, i.e., heavy computational tasks, are migrated to remote servers with powerful computational resources, which can improve the performance of mobile devices. This paper focuses on a popular MCC architecture, CloneCloud, and constructs a scheduling problem of task migration as a constrained stochastic shortest path problem in a directed acyclic graph. And then it designs a scheduling algorithm based on genetic algorithm to obtain the optimal task migrations. A user flexibly makes migration decisions through its own mobile device and migrates some tasks to the clone in CloneCloud without any change of application codes. Furthermore, this scheme facilitates mobile devices to collaboratively process computational applications. Real testbed experiments in Android smartphone demonstrate that the smartphone is able to save at most 59.42% energy within a time constraint by using the proposed task migration scheme. Keywords Mobile device · CloneCloud · Genetic algorithm (GA) · Task migration scheme
1 Introduction With the development of wireless communication, the worldwide market of mobile devices will reach 1.57 billion units shipped in 2022 [25]. With a fast-paced upgrade of hardware, mobile devices support more and more complex applications that change the way people live, work, and entertainment. However, compared with traditional PCs, mobile devices, especially smartphones, have fewer computational * Yi Xie [email protected] Extended author information available on the last page of the article
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resources and shorter battery life due to weight and size. People often complain about their degraded performance and short battery life. Therefore, it is crucial to improve the energy efficiency of mobile devices. Facing the challenge, researchers aspired to enhance the energy-saving capability of smartphone components [17, 29], as there is no breakthrough in battery technology in recent years [15]. For example, Liang et al. [17] proposed a critical speed-based DVFS mechanism, which searched an optimal CPU frequency to minimize the energy consumption of Android devices. Zhuang et al. [33] implemented a location-sensing framework, as a middleware, on Android-based smartphones. This framework is shown to improve battery life by up to 75%. These hardware-based methods exploit the CPU’s power distribution, memory, touchscreen, and graphics, but cause a considerable amount of expense for device updates. Other researchers proposed energy-efficient protocols and algorit
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