Proceedings ArticleDOI
Joint allocation of computation and communication resources in multiuser mobile cloud computing
Sergio Barbarossa,Stefania Sardellitti,Paolo Di Lorenzo +2 more
- pp 26-30
TLDR
This paper proposes a method to jointly optimize the transmit power, the number of bits per symbol and the CPU cycles assigned to each application in order to minimize the power consumption at the mobile side, under an average latency constraint dictated by the application requirements.Abstract:
Mobile cloud computing is offering a very powerful storage and computational facility to enhance the capabilities of resource-constrained mobile handsets. However, full exploitation of the cloud computing capabilities can be achieved only if the allocation of radio and computational capabilities is performed jointly. In this paper, we propose a method to jointly optimize the transmit power, the number of bits per symbol and the CPU cycles assigned to each application in order to minimize the power consumption at the mobile side, under an average latency constraint dictated by the application requirements. We consider the case of a set of mobile handsets served by a single cloud and we show that the optimization leads to a one-to-one relationship between the transmit power and the percentage of CPU cycles assigned to each user. Based on our optimization, we propose then a computation scheduling technique and verify the stability of the computations queue. Then we show how these queues are affected by the degrees of freedom of the channels between mobile handsets and server.read more
Citations
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Journal ArticleDOI
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
Posted Content
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
Journal ArticleDOI
Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing
TL;DR: In this article, a game theoretic approach for computation offloading in a distributed manner was adopted to solve the multi-user offloading problem in a multi-channel wireless interference environment.
Journal ArticleDOI
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Pavel Mach,Zdenek Becvar +1 more
TL;DR: This paper describes major use cases and reference scenarios where the mobile edge computing (MEC) is applicable and surveys existing concepts integrating MEC functionalities to the mobile networks and discusses current advancement in standardization of the MEC.
Journal ArticleDOI
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
Pavel Mach,Zdenek Becvar +1 more
TL;DR: In this paper, the authors present a survey of the research on computation offloading in mobile edge computing (MEC), focusing on user-oriented use cases and reference scenarios where the MEC is applicable.
References
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Journal ArticleDOI
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Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?
Kumar Karthik,Yung-Hsiang Lu +1 more
TL;DR: The cloud heralds a new era of computing where application services are provided through the Internet, but is it the ultimate solution for extending such systems' battery lifetimes?
Proceedings ArticleDOI
ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading
TL;DR: This paper proposes ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images.
Journal ArticleDOI
A Survey of Computation Offloading for Mobile Systems
TL;DR: An overview of the background, techniques, systems, and research areas for offloading computation is provided, and directions for future research are described.
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