Credit Based Multilevel Queue Scheduling in Cloud Environment

Abstract

Cloud computing is an IT paradigm, a model for enabling access to shared huge configurable resources, which is provisioned with minimal management effort, often over the web. Users access the cloud applications through a browser or a mobile app while the software and data are stored on servers at a remote location. The benefits of using the cloud are storage, security, cost, accessibility, flexibility, maintenance, backup and recovery etc. It also provides easy access to data at any time. Cloud application providers strive to give the better service and performance as if the software programs were installed locally on end-users system. One of major issue in cloud computing is resource allocation. Resource allocation is difficult to solve, especially in case of complex task and performance computing. To satisfy the performance, mapping the tasks to processor is done, which helps in resource utilization in the process efficiently. The existing Shortest Job First (SJF) algorithm uses the CPU processing time and memory size to perform the shortest job scheduling first. The starvation in SJF exists only if processes with low burst time appears in queue before the processes with the high burst time is executed. Since this algorithm always chooses the process with low burst time, the process with the high burst time will never get the share of CPU. So, in order to avoid starvation, the proposed system combines the SJF and the Multilevel Queue (MQ) Scheduling based on credit.