Efficient Task Scheduling in Cloud Computing using Multi-objective Hybrid Ant Colony Optimization Algorithm for Energy Efficiency

• Published in: International journal of advanced computer science & applications 2021, Vol.12 (3)
• Main Authors: Zambuk, Fatima Umar, Ya’u, Abdulsalam, Jiya, Mohammed, Ado, Nahuru, Ja’afaru, Badamasi, Muhammad, Aliyu
• Format: Article
• Language: English
• Subjects: Algorithms; Ant colony optimization; Cloud computing; Energy consumption; Experimentation; Multiple objective analysis; Resource utilization; Task scheduling; Virtual environments
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2021.0120353

The efficiency of Internet services is determined by the Cloud computing process. Various challenges in computing are being faced, such as security, the efficient allocation of resources, which in turn results in the waste of resources. Researchers have explored a number of approaches over the past decade to overcome these challenges. The main objective of this research is to explore the task scheduling of cloud computing using multi-objective hybrid Ant Colony Optimization (ACO) with Bacterial Foraging (ACOBF) behavior. ACOBF technique maximized resource utilization (Service Provider Profit) and also reduced Makespan and user wait times Job request. ACOBF classifies the user job request in three classes based on the sensitivity of the protocol associated with each request, Schedule Job request in each class based on job request deadline and create a Virtual Machine (VM) cluster to minimize energy consumption. Based on comprehensive experimentation, the simulated results show that the performance of ACOBF outperforms the benchmarked techniques in terms of convergence, diversity of solutions and stability.