一种移动云计算计算卸载算法及其仿真研究
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摘要
移动云计算可以通过将计算任务从资源受限的移动设备卸载至云端以增强移动设备的计算能力,而如何实现约束条件下的能效计算卸载机制是当前的主要挑战。针对以降低移动设备能耗和应用完成时间为目标,将移动云计算的卸载问题形式化为博弈问题,提出一种分布式计算卸载博弈算法。分析了博弈的结构属性,证明了博弈算法在同质和异质无线访问情形下均能产生Nash均衡解,量化了算法在集中式最优解基础上的效率。数值仿真结果表明,在以能耗与完成时间衡量的总体能效代价性能上,新算法是有效可行的,在计算规模增大时依然拥有良好性能。
Mobile cloud computing can significantly enhance computation capability of mobile devices by offloading computation from resource-constrainted mobile devices onto the cloud. How to achieve energy-efficient computation offloading under the hard constraint remains a challenge issue. For solving this problem, with reducing energy consumption and shorting application completion time as an objective, we formulate the computation offloading problem in mobile cloud computing to the game problem. And, we propose a decentralized computation offloading game algorithm. The structural property of the game is analyzed. We also prove that the game algorithm always admits a Nash equilibrium solution for both cases of homogenous and heterogenous wireless access and quantify its efficiency ratio over the centralized optimal solution. Numerical simulation results show, for the whole energy-efficiency cost combined with energy consumption and completion time, the proposed new algorithm is effective and feasible and scale well as the computation size increases.
Mobile cloud computing can significantly enhance computation capability of mobile devices by offloading computation from resource-constrainted mobile devices onto the cloud. How to achieve energy-efficient computation offloading under the hard constraint remains a challenge issue. For solving this problem, with reducing energy consumption and shorting application completion time as an objective, we formulate the computation offloading problem in mobile cloud computing to the game problem. And, we propose a decentralized computation offloading game algorithm. The structural property of the game is analyzed. We also prove that the game algorithm always admits a Nash equilibrium solution for both cases of homogenous and heterogenous wireless access and quantify its efficiency ratio over the centralized optimal solution. Numerical simulation results show, for the whole energy-efficiency cost combined with energy consumption and completion time, the proposed new algorithm is effective and feasible and scale well as the computation size increases.
引文
[1] Muraleedharan R. Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture[C]// Computers and communications. Cappadocia, Turker: IEEE, 2012:59-66.
[2] Shiraz M, Gani A, Khokhar R H, et al. A Review on distributed application processing frameworks in smart mobile devices for mobile cloud computing[J]. IEEE Communications Surveys & Tutorials,2013, 15(3):1294-1313.
[3] 李继芯,李小勇,高云全,等.5G网络下移动云计算节能措施研究[J],计算机学报,2017,40(7):1491-1516.
[4] Lin X, Wang Y, Xie Q, et al. Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment[J].IEEE Transactions on services computing,2015,8(2):175-186.
[5] Zhang W, Wen Y, Guan K, et al. Energy-Optimal mobile cloud computing under stochastic wireless channel[J]. IEEE Transactions on wireless communications, 2013, 12(9):4569-4581.
[6] Zhang W, Wen Y, Wu D O. Collaborative task execution in mobile cloud computing under a stochastic wireless channel[J]. Wireless communications IEEE Transactions on, 2015, 14(1):81-93.
[7] Huang D, Wang P, Niyato D. A dynamic offloading algorithm for mobile computing[J]. IEEE Transactions on wireless communications, 2012, 11(6):1991-1995.
[8] Chen X. Decentralized computation offloading game for mobile cloud computing[J]. Parallel & distributed systems IEEE Transactions on, 2014, 26(4):974-983.
[9] 柳兴,李建彬,杨震,等.移动云计算中的一种任务联合执行策略[J],计算机学报,2017,40(2):364-377.
[10] Yang L, Cao J, Tang S, et al. A framework for partitioning and execution of data stream applications in mobile cloud computing[C]// IEEE, International conference on cloud computing. Honolulu, HI, USA: IEEE, 2012:23-32.
[11] Rahimi M R, Venkatasubramanian N, Vasilakos A V. MuSIC: Mobility-Aware optimal service allocation in mobile cloud computing[C]// IEEE Sixth international conference on cloud computing. IEEE Computer society, Santa Clara, CA, USA: 2013:75-82.
[12] Barbarossa S, Sardellitti S, Lorenzo P D. Joint allocation of computation and communication resources in multiuser mobile cloud computing[C]// Signal Processing Advances in Wireless Communications. Darmstadt. Germany: IEEE, 2013:26-30.
[13] 胡海洋,刘润华,胡华.移动云计算环境下任务调度的多目标优化方法[J].计算机研究与发展,2017,54(9):1909-1919.
[14] 张文柱,曹琲琲,周雪婷.移动云环境下高能效的移动终端计算迁移策略[J].西安电子科技大学学报,2017,44(3): 175-180.
[15] 曹傧,梁裕丞,罗雷,等.ad hoc云环境中分布式博弈卸载策略[J].通信学报,2017,38(11):24-34.
[2] Shiraz M, Gani A, Khokhar R H, et al. A Review on distributed application processing frameworks in smart mobile devices for mobile cloud computing[J]. IEEE Communications Surveys & Tutorials,2013, 15(3):1294-1313.
[3] 李继芯,李小勇,高云全,等.5G网络下移动云计算节能措施研究[J],计算机学报,2017,40(7):1491-1516.
[4] Lin X, Wang Y, Xie Q, et al. Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment[J].IEEE Transactions on services computing,2015,8(2):175-186.
[5] Zhang W, Wen Y, Guan K, et al. Energy-Optimal mobile cloud computing under stochastic wireless channel[J]. IEEE Transactions on wireless communications, 2013, 12(9):4569-4581.
[6] Zhang W, Wen Y, Wu D O. Collaborative task execution in mobile cloud computing under a stochastic wireless channel[J]. Wireless communications IEEE Transactions on, 2015, 14(1):81-93.
[7] Huang D, Wang P, Niyato D. A dynamic offloading algorithm for mobile computing[J]. IEEE Transactions on wireless communications, 2012, 11(6):1991-1995.
[8] Chen X. Decentralized computation offloading game for mobile cloud computing[J]. Parallel & distributed systems IEEE Transactions on, 2014, 26(4):974-983.
[9] 柳兴,李建彬,杨震,等.移动云计算中的一种任务联合执行策略[J],计算机学报,2017,40(2):364-377.
[10] Yang L, Cao J, Tang S, et al. A framework for partitioning and execution of data stream applications in mobile cloud computing[C]// IEEE, International conference on cloud computing. Honolulu, HI, USA: IEEE, 2012:23-32.
[11] Rahimi M R, Venkatasubramanian N, Vasilakos A V. MuSIC: Mobility-Aware optimal service allocation in mobile cloud computing[C]// IEEE Sixth international conference on cloud computing. IEEE Computer society, Santa Clara, CA, USA: 2013:75-82.
[12] Barbarossa S, Sardellitti S, Lorenzo P D. Joint allocation of computation and communication resources in multiuser mobile cloud computing[C]// Signal Processing Advances in Wireless Communications. Darmstadt. Germany: IEEE, 2013:26-30.
[13] 胡海洋,刘润华,胡华.移动云计算环境下任务调度的多目标优化方法[J].计算机研究与发展,2017,54(9):1909-1919.
[14] 张文柱,曹琲琲,周雪婷.移动云环境下高能效的移动终端计算迁移策略[J].西安电子科技大学学报,2017,44(3): 175-180.
[15] 曹傧,梁裕丞,罗雷,等.ad hoc云环境中分布式博弈卸载策略[J].通信学报,2017,38(11):24-34.