Open AccessONLINE RESOURCE ALLOCATION OPTIMIZATION AND WORKLOAD BALANCING SCHEDULING ON IAAS CLOUD SYSTEM
Author(s) -
Deepika Saxena,
R.K. Chauhan,
Shilpi Saxena
Publication year - 2016
Publication title -
zenodo (cern european organization for nuclear research)
Language(s) - English
DOI - 10.5281/zenodo.163783
Subject(s) - cloud computing , computer science , workload , distributed computing , scheduling (production processes) , resource allocation , load balancing (electrical power) , computer network , operating system , mathematical optimization , mathematics , geometry , grid
Cloud computing is a rapidly emerging paradigm in this very new era of technology. Basically, cloud is a cluster of distributed and interlinked servers providing on-demand services to customers. Broadly, it offers software-as-a-service(SAAS), platform-as-a-service(PAAS), infrastructure-as-a-service(IAAS).Here we are focusing on IAAS cloud system which offers computational resources to remote customers in the form of leases. Here we are defining real-time or online optimized scheduling of requests of various resources arriving simultaneously at data center of IAAS cloud service provider. Being more practical, our algorithm is providing best resource utilization and better results in terms of execution time as compared to DFPT algorithm for task scheduling in cloud computing which do not considers dependency between tasks(requests). Our algorithm proposes dynamic task allocation on IAAS clouds and resource scheduling by utilizing the updated status of various Virtual machines available at real time. We have simulated this experiment using CloudSim toolkit. Surely, there is very beneficial improvement in results as compared to default FCFS scheduling and other available scheduling algorithms