GA-Based Customer-Conscious Resource Allocation and Task Scheduling in Multi-cloud Computing

Abstract

Resource allocation in multi-cloud computing is a complicated chore; there are many constraints and configuration in accordance with cloud providers as well as cloud customers. Mapping the incoming job request to available virtual machines (VMs) is a non-polynomial complete problem as the nature of traffic quite arbitrary. 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 Genetic Algorithm-based Customer-Conscious Resource Allocation and Task Scheduling in multi-cloud computing. The algorithm is basically divided into two phases, namely genetic algorithm-based resource allocation and shortest task first scheduling. The objective is to map the tasks to VMs of the multi-cloud federation in order to have minimum makespan time and maximum customer satisfaction. Rigorous experiments were done on synthetic data and compared the simulation results with the existing scheduling algorithm. Results of simulation illustrate that the proposed algorithm outrun the existing ones as per concerned metrics.