A Multi-Objective Task scheduling and Resource Allocation for Energy Efficiency in Cloud Computing using a Heuristic Approach
Fatima Umar Zambuk,Mohammed Jiya,Mohammed Kabir Dauda,Ismail Aliyu,Maryam Maishanu,Maryam Abdullahi Musa.
Abstract
Cloud computing is one of the modern and trending technologies that touched lives in todays world. Resource allocation and task scheduling are the key aspects of todays 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 solution. 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 extensive simulations conducted, the proposed method consumed energy of 700 joules while 1200 joules and 1400 joules were consumed by bat and ACO respectively.
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