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

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