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Optimal Task Assignment in Mobile Cloud Computing by Queue Based Ant-Bee Algorithm

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Abstract

Mobile cloud computing (MCC) broadens the mobile devices capability by offloading tasks to the ‘cloud’. Hence, offloading numerous tasks simultaneously increases the ‘cloudlets’ load and augments the average completion duration of the offloaded tasks. To withstand this issue, we propose a hybrid Queue Ant Colony-Artificial Bee Colony Optimization (Ant-Bee) algorithm for optimal assignment of tasks in MCC environment. The proposed algorithm works on a two-way MCC model with offloading technique, that considers of both the ‘cloudlets’ and the public ‘cloud’. The ‘cloud’ and the ‘cloudlets’ are designed on the basis of queue model for the estimation of clients waiting time in the limitation of resources. The major concern of the proposed algorithm is to offload the tasks by identifying the accurate place preferably in a ‘cloud/cloudlet’. The ‘cloud/cloudlet’ is encompassed by a queue model with the end goal to minimize the drop rate by permitting the tasks to wait in the queue. It also aims for the optimal assignment of tasks to manage the ‘cloudlets’ load and to minimize the entire tasks average completion time. The performance of the proposed algorithm is analyzed with few Queue based conventional algorithms such as, “Round Robin”, “Weighted Round Robin” and “Random”. From the simulation result, it is analyzed that our proposed algorithm outperforms in the power consumption of the mobile devices, the average completion time of tasks, and drop rate. Also, to ensure the efficiency of our proposed hybrid QAnt-Bee algorithm, it is contrasted with the “HACAS” application scheduling algorithm, which fails to consider queue in the ‘cloudlets’.

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  1. Department of Electronics and Communication, Anna University, Chennai, Tamilnadu, India

    Vinu Sundararaj

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Correspondence to Vinu Sundararaj.

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Sundararaj, V. Optimal Task Assignment in Mobile Cloud Computing by Queue Based Ant-Bee Algorithm. Wireless Pers Commun 104, 173–197 (2019). https://doi.org/10.1007/s11277-018-6014-9

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