Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers. (October 2021)

• Record Type: Journal Article
• Authors: Ajmal, Muhammad Sohaib; Iqbal, Zeshan; Khan, Farrukh Zeeshan; Ahmad, Muneer; Ahmad, Iftikhar; Gupta, Brij B.
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Ant colony algorithm -- Cloud computing -- Data center cost -- Evolutionary algorithm -- Genetic algorithm -- Multi-objective optimization -- Quality of service (QoS) -- Resource allocation -- Service level agreement (SLA) -- Task scheduling; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107419
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Hybrid ant genetic algorithm for efficient task scheduling in cloud datacenter is proposed. Reduces scheduling solution space. Minimizes workflow execution time by load balancing. Less response time than genetic algorithm. Abstract: Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs. Graphical abstract: Image, graphical abstract