Abstract
An efficient scheduling reduces the time required to process the jobs, and energy management decreases the service cost as well as increases the lifetime of a battery. A balanced trade-off between the energy consumed and processing time gives an ideal objective for scheduling jobs in data centers and battery based devices. An online multiprocessor scheduling multiprocessor with bounded speed (MBS) is proposed in this paper. The objective of MBS is to minimize the importance-based flow time plus energy (IbFt+E), wherein the jobs arrive over time and the job’s sizes are known only at completion time. Every processor can execute at a different speed, to reduce the energy consumption. MBS is using the tradition power function and bounded speed model. The functioning of MBS is evaluated by utilizing potential function analysis against an offline adversary. For processors m ≥ 2, MBS is O(1)-competitive. The working of a set of jobs is simulated to compare MBS with the best known non-clairvoyant scheduling. The comparative analysis shows that the MBS outperforms other algorithms. The competitiveness of MBS is the least to date.
Highlights
There are number of server farms equipped with hundreds of processors
A set of jobs and processors are used to simulate the working of multiprocessor with bounded speed (MBS) and NC-PAR
The average turnaround and response time of jobs, when they are executed by using MBS is lesser than NC-PAR
Summary
There are number of server farms equipped with hundreds of processors. The cost of energy used for cooling and running a machine for around three years surpasses the hardware cost of the machine [1]. In the c-competitive onlineis speed scaling, which decides every processor’s execution speed at all time; the third policy is job scheduling algorithm, for each input the cost received is less than or equal to c times the cost of optimal assignment, which indicates that to which processor should be assigned. Chan et al [24] proposed an O(1)-competitive algorithm using sleep management for the objective of minimizing the flow time plus energy. Chan et al [27] studied an online clairvoyant sleepmanagement algorithm scheduling with arrival-time-alignment (SATA) which is (1 + ∆)-speed O ∆2 -competitive for the objective of minimizing the flow time plus energy. The problem of online non-clairvoyant (ON-C) DSS scheduling is studied and an algorithm multiprocessor with bounded speed (MBS) is proposed with an objective to minimize the.
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