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Hybridization of immune with particle swarm optimization in task scheduling on smart devices

Abstract

The cloud environment allows enhanced task scheduling techniques for allocating tasks efficiently for smart devices. In this article, the task scheduling technique of artificial immune system (AIS), randomized gossip algorithm (RGA), and particle swarm optimization (PSO) implemented as proposed design to achieve uniform distribution in an optimized manner. The AIS technique is mainly focused on optimization and network security which is comprised of many applications. The peer-to-peer networks of sharing the information and make the interconnection possible are achieved by a RGA. For this kind of broadcasting the information, the RGA algorithms are mainly suitable. The PSO algorithm was executed for the independent task and allocated in a sensible self-organized way. The proposed method response time, performance ratio, and the makespan ratio defines as the total length of the schedule measured and compared with other time scheduling algorithms discussed later in this method. The above-proposed algorithm is used to allocate the resources efficiently even though the tasks have increased further. The comparative analysis of this proposed work was figured and tabulated. The decrease in makespan ratio, reduced response time, uniform distribution of tasks, no failures or crashes as disruption, and reduced overload make the proposed system optimized.

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Correspondence to Jeevanantham Balusamy.

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Balusamy, J., Karunakaran, M. Hybridization of immune with particle swarm optimization in task scheduling on smart devices. Distrib Parallel Databases 40, 85–107 (2022). https://doi.org/10.1007/s10619-021-07337-y

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  • DOI: https://doi.org/10.1007/s10619-021-07337-y

Keywords

  • Cloud computing
  • Data storage
  • Virtual machine
  • Particle swarm optimization
  • Artificial immune system