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The execution of much sophisticated applications on the resource-constrained mobile device will lead to the fast exhaustion of the battery of mobile device. Therefore, mobile cloud computing (MCC) is regarded as an energy-effective approach by offloading tasks from mobile device to the resource-enough cloud, which cannot only save energy for mobile devices but also prolong the operation time of battery. However, it still remains a challenging issue to coordinate task offloading among mobile devices and get offloading results quickly at the same time. In this paper, we propose an agent-based MCC framework to enable the device to receive offloading results faster by making offloading decision on the agent. Moreover, to get an offloading strategy, we formulate the problem of maximizing energy savings among multiple users under the completion time and bandwidth constraints. To solve the optimization problem, we propose a Dynamic Programming After Filtering (DPAF) algorithm. In the algorithm, firstly, the original offloading problem is transformed to the classic 0–1 Knapsack problem by the filtering process on the agent. Furthermore, we adopt dynamic programming algorithm to find an optimal offloading strategy. Simulation results show that the framework can more quickly get response from agent than other schemes and the DPAF algorithm outperforms other solutions in energy saving.
•We propose an agent-based mobile cloud computing framework.•We formulate the offloading optimization problem into the maximum energy saving problem.•We propose a Dynamic Programming After Filtering (DPAF) algorithm.