Factors Influencing Job Rejections in Cloud Environment

TL;DR

This paper analyzes how job scheduling and load balancing strategies like Round Robin and Shortest Job First impact job rejection rates in cloud computing environments, proposing an improved load balancing method to prevent deadlocks.

Contribution

It evaluates the effectiveness of RR and SJFS strategies in reducing job rejections and introduces a new load balancing approach to prevent deadlocks in cloud systems.

Findings

RR and SJFS reduce job rejections during peak hours

Proposed load balancing approach effectively prevents deadlocks

Analysis highlights importance of scheduling in cloud efficiency

Abstract

The IT organizations invests heavy capital by consuming large scale infrastructure and advanced operating platforms. The advances in technology has resulted in emergence of cloud computing, which is promising technology to achieve the aforementioned objective. At the peak hours, the jobs arriving to the cloud system are normally high demanding efficient execution and dispatch. An observation that has been carried out in this paper by capturing a job arriving pattern from a monitoring system explains that most of the jobs get rejected because of lack of efficient technology. The job rejections can be controlled by certain factors such as job scheduling and load balancing. Therefore, in this paper the efficiency of Round Robin (RR) scheduling strategy used for job scheduling and Shortest Job First Scheduling (SJFS) technique used for load balancing in reducing the job rejections are analyzed. Further, a proposal for an effective load balancing approach to avoid deadlocks has been discussed.