基于二次规划的任务卸载决策和资源分配方法

主要研究移动用户均有多个独立任务的多用户移动云计算系统，这些移动用户将任务卸载到云端时共享通信资源。如何对所有用户的任务卸载决策和通信资源分配进行联合优化，以便使所有用户的能耗、计算量和延时降到最低是目前研究的难点。将该问题建模为NP难度的非凸的具有二次约束的二次规划(QCQP)问题，提出一种高效的近似算法进行求解，通过单独的半正定松驰(SDR)处理后，确定二元卸载决策和通信资源最优分配。采用代表最小系统成本的性能下界作为性能基准进行仿真实验，结果表明，本文算法在多种参数配置下的性能均接近最优性能。

Research on task offloading decision and resource allocation method based on quadratic programming

A general multi-user mobile cloud computing system where each mobile user has multiple independent tasks is consided. These mobile users share the communication resource while offloading tasks to the cloud. How to jointly optimize the task offloading decisions of all users as well as the allocation of communication resource in order to minimize the overall cost of energy, computation, and delay for all users is one of the difficult problems in current research. The optimization problem is formulated as a non-convex Quadratically Constrained Quadratic Program(QCQP), which is NP-hard, and an efficient approximate algorithm is proposed by using separable Semi-Definite Relaxation(SDR) to solve this problem, followed by recovery of the binary offloading decision and optimal allocation of the communication resource. The simulation experiments are carried out by using the lower bound of the minimum system cost as the performance benchmark, the results show that the proposed algorithm gives nearly optimal performance under various parameter settings.