%T Incentive-Scheduling Algorithms to Provide Green Computational Data Center
%I Springer
%V 2
%A A.A. Haruna
%A L.T. Jung
%A V. Arputharaj
%A L.J. Muhammad
%X The increased computational loads in grid servers are dissipating more heat to eventually amplifies the cooling demand in the data center (DC). This can lead to more submitted jobs missing their job completion deadlines. Unfortunately, the conventional DC job scheduling approaches do not provide compensation to the resource users on their submitted jobs that missed the deadlines. The absence of compensation may dissuade users from submitting jobs to the DCs. While the free air-cooling strategy is used elsewhere, it is not generally applicable in tropical countries such as Malaysia. A constant artificial cooling (air conditioning) is needed to sustain the DC long-hour operation. To solve this issue in the tropical region, green incentive-scheduling algorithms were devised to significantly save the cooling electricity consumption cost in DC and to be able to compensate users (as an incentive) for their submitted jobs that missed the job completion deadline(s). However, there is a need to further analyse the performance of the proposed green incentive-scheduling algorithms using a different benchmark traces file and to conduct a cost-saving comparison analysis between the green incentive-scheduling and the non-green incentive-scheduling algorithms. Therefore, this paper's authors observed and analysed the performance comparison of the proposed green incentive-based job scheduling algorithms using a benchmark traces file and conducted a cost-saving comparison analysis between the green incentive and the non-green incentive-based scheduling algorithms. Ultimately, the green incentive-scheduling algorithms are to show their impact on reducing the DC operation costs (e.g. electricity bills for cooling) and to provide fair compensation to grid users for a win�win situation. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
%O cited By 2
%J SN Computer Science
%L scholars14771
%D 2021
%R 10.1007/s42979-021-00633-5
%N 4