Dynamic Resource Allocation Using an Adaptive Multi-Objective Teaching-Learning Based Optimization Algorithm in Cloud

Abstract

Resource allocation is a non-polynomial complete problem in the cloud data center that selects the proper resources to execute many fine computational granularity tasks. Customer requirements and capacity of applications change frequently. To bridge the gap between frequently changing customer requirement and available infrastructure for the services, we propose a dynamic resource allocation strategy using an adaptive multi-objective teaching-learning based optimization (AMO-TLBO) algorithm in Cloud computing. To improve the exploration and exploitation capacities, AMO-TLBO introduces the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. Moreover, a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive is used. The objectives of AMO-TLBO include minimizing makespan, cost and maximizing utilization using well-balanced load across virtual machines. The evaluation results show that the proposed algorithm outperforms TLBO, MOPSO and NSGA-II algorithms in terms of different performance metrics.