移动云计算中的任务调度与计算迁移算法

随着互联网的发展，许多应用程序对计算机的计算能力和资源的需求越来越大，而移动设备具有有限的资源和计算能力，云计算迁移技术是解决计算密集型任务在移动端上顺利运行的主流方法。针对无线网络中联合调度和迁移的问题，提出了一个快速高效的启发式算法。算法将能够迁移的任务全部迁移到云端作为初始解，然后逐次计算可迁移任务在移动端运行的能耗节省量，依次将节省量最大的任务迁移到移动端。每迁移一个任务，该算法都会依据任务间的通信时间，及时更新各个任务的能耗节省量。为了进一步优化启发式算法得到的解，还构造了适用于此问题并以启发解为初始解的模拟退火算法，给出了相应的编码方法、目标函数、邻域解、温度参数以及算法终止准则。与无迁移、饱和迁移、随机迁移三类算法的对比实验结果表明，由启发式算法得出的解具有高效性，能给出使移动端能耗更小的解。

Algorithms for task joint scheduling and computation offloading in mobile cloud computing

With the development of the Internet, many applications have a growing demand for computing power and resources. However, mobile devices have limited resources, such as battery life, network bandwidth, storage capacity, and processor performance. Cloud offloading is a main solution to supporting computationally demanding applications on these resource constrained devices. We propose a fast and efficient heuristic algorithm for the scheduling and offloading problems of the application tasks in the wireless network. The heuristic algorithm initially moves the tasks which can be offloaded to the cloud, then successively calculates the energy saving of each offloaded task running on the mobile terminal, and sequentially moves the tasks with the highest energy saving to the mobile device. The saved energy is updated in each iteration in order to cater for the task concurrence. In addition, we also construct a simulated annealing algorithm, which uses the solution generated by the heuristic algorithm as the initial solution, to further optimize the solution obtained by the heuristic algorithm, and depict in detail the encoding method, objective function, neighborhood solution, temperature parameters, and algorithm termination rules. Experimental results show that in comparison to the three algorithms based on non offloading, full offloading, and random offloading respectively, the solution generated by the heuristic solutions is better and efficient.