A Multi-Objective Task Scheduling Scheme GMOPSO-BFO in Mobile Cloud Computing

Robin Prakash Mathur, Manmohan Sharma


Mobile cloud computing is currently an encouraging field in the cyber-physical world. It is an amalgamation of mobile computing and cloud computing. Computational offloading is one feature in the mobile cloud application that offloads the task to the cloud server, processes it, and gets the results back on the mobile device. During offload, the job needs to be queued on the cloud servers and allocated to the virtual machines. Task scheduling is an important step where the mobile task is assigned to the servers and processed somehow. In the overall offloading process, energy conservation is a significant concern. The scheduling problem involves mapping the offloaded task to the cloud server while satisfying the energy and time constraints. This paper proposes a hybrid scheduling scheme based on Gaussian-based multi-objective particle swarm optimization (GMOPSO) and bacterial foraging optimization BFO). This scheme performs better when compared to other variants of PSO in terms of makespan and energy efficiency.


Computational offloading, mobile cloud computing, MOPSO, bacteria foraging optimization, energy consumption, makespan

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