Optimising time in cloud using multi-hold inherited maximisation algorithm to reduce computational time

Sriramya, P.; Kumar, D.S. Manoj

doi:10.1504/ijie.2021.10034693

Cited by 1 publication

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“…It does not take into account the current state of VMs and then divides all traffic equivalently among all VMs in a similar manner such as RR and random scheduling algorithms. Heuristic scheduling algorithms Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), genetic algorithm (GA) provide approximated solutions (Sriramya & Manoj Kumar, 2021).…”

Section: Introductionmentioning

confidence: 99%

Optimizing time delay in cloud using enhanced multi‐hold inherited maximization algorithm to reduce cost and improve bandwidth

Kumar

Sriramya

2022

Expert Systems

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Optimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM.

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Section: Introductionmentioning

confidence: 99%