A multi-objective task scheduling and resource allocation for energy efficiency in cloud computing using a heuristic approach

Abstract

Cloud computing is one of the modern and trending technologies that touches lives in 
today’s world. Resource allocation and task scheduling are the key aspects of today’s cloud. 
A hybridized heuristic approach that commingles the Modified Ant Colony Optimization 
(MACO) and deterministic divide-and-conquer approach to perform resource allocation 
and task scheduling is purported in this paper. In the proposed scheme, before cloud 
resource allocation takes effect, each task unit is processed by the ACO. MACO allocates 
the resources considering cloud resources load as constraints and bandwidth. In addition, 
the divide-and-conquer preempts resource-intensive tasks thereby improving the solu
tion. The proposed technique is found to be efficient with waiting time, response time, 
and energy consumption as compared with bat and ACO algorithms. Based on the exten
sive simulations conducted, the proposed method consumed energy of 700 joules while 
1,200 and 1,400 joules were consumed by a bat and ACO, respectively.