An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning. Issue 4 (2nd October 2021)

• Record Type: Journal Article
• Authors: Daid, Rohit; Kumar, Yogesh; Hu, Yu-Chen; Chen, Wu-Lin
• Is Part Of: Connection science. Volume 33:Issue 4(2021)
• Page Start: 954
• Page End: 974
• Publication Date: 2021-10-02
• Subjects: Task scheduling -- data centres -- machine learning -- server management -- energy consumption; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2021.1926929
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Energy efficiency is one of the important parameters in cloud computing which is managed by the data centres. Data centres are computer warehouses that are responsible for storing large volumes of data to deal with the daily transaction handling needs of different productions. Effective scheduling for the execution of the request on machines is still a problem. In addition, the power consumption, as well as management of the node clusters is also a problematic situation when the CPU utilisation increases up to the limit. In this paper, efficient minimum execution and completion time scheduling are accomplished by using a machine learning approach for effectual CPU usage and service level agreement fulfilment in data centres, considered in terms of average accuracy which will reduce costs for the maintenance of the data centres in real-time scenarios. The simulation of the proposed work is achieved and the performance is evaluated in terms of power consumption and CPU usage. The proposed research utilises the neural network and linear regression analysis to perform the classification and compares the performance for the efficient CPU usage.