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FDLA: Fractional Dragonfly based Load balancing Algorithm in cluster cloud model

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Abstract

Cloud computing is a developing technology that enables on-demand network access to the users through a shared pool of cluster computing resources. However, maintaining the stability of processing several tasks in the cloud environment is a complex issue. Hence, it requires a load balancing technique that allocates the task to the Virtual Machines (VMs) without affecting the performance of the system. This paper presents a technique for load balancing, called fractional dragonfly based load balancing algorithm (FDLA), by proposing two selection probabilities and fractional dragonfly algorithm. The proposed load balancing model utilizes certain parameters of VMs and Physical Machines (PMs) to select the tasks to be reallocated in the VMs for load balancing. The selection is based on the probabilities, Task selection probability (TSP) and VM selection probability (VSP), which are newly designed. Further, the proposed fractional dragonfly algorithm that combines dragonfly algorithm (DA) with Fractional Calculus (FC) performs an optimal selection of VMs for the reallocation of the task using a newly designed fitness function. In the performance analysis of FDLA based on load and number of tasks reallocated, the proposed FDLA could achieve a minimum load of 0.2133 with 14 reallocated tasks, indicating its effectiveness in load balancing.

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Correspondence to C. Ashok Kumar.

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Ashok Kumar, C., Vimala, R., Aravind Britto, K.R. et al. FDLA: Fractional Dragonfly based Load balancing Algorithm in cluster cloud model. Cluster Comput 22 (Suppl 1), 1401–1414 (2019). https://doi.org/10.1007/s10586-018-1977-6

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  • DOI: https://doi.org/10.1007/s10586-018-1977-6

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