Energy aware task scheduling using hybrid firefly - GA in big data. (26th March 2020)

• Record Type: Journal Article
• Authors: Senthilkumar, M.; Ilango, P.
• Is Part Of: International journal of advanced intelligence paradigms. Volume 16:Number 2(2020)
• Page Start: 99
• Page End: 112
• Publication Date: 2020-03-26
• Subjects: firefly algorithm -- FA -- genetic algorithm -- GA -- task scheduling -- Hadoop -- MapReduce framework; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

Abstract

Task scheduling is important of research in big data and it is made in two traditions user level and system level; in user level issues with scheduling between the service provider and customer; in system level issues in scheduling with resources management in the data centre. The drawbacks of various existing methods to increase in power consumption of data centres have become a significant issue. Now the MapReduce clusters constitute a major piece of the data centre for big data applications. Simply the absolute size, high fault-tolerant nature and low utilisation levels make them less energy efficient. The complexity of scheduling increases when there is an increase in the size of the task, it becomes very tedious to perform scheduling effectively. The drawback with existing scheduling algorithm generates higher computational cost and less efficient; the multi-objective scheduling with cloud computing makes it difficult to resolve the problem in the case of complex tasks. These are the primary drawbacks of several existing works, which prompt us to manage this research on task scheduling in cloud computing.