Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing

Cloud computing is computing in which large groups of remote servers are networked to allow centralized data storage and on-line access to computer services. Cloud computing providers install and operate application software in the cloud through Software as a Service (SaaS) model. Cloud users access the software and utilizes the services by cloning tasks onto multiple virtual machines (VMs). In recent years, offering high-performance computing capacity reminds cloud providers to utilize resources fully due to limitation of resource. Enhancing resource utilization is also essential for achieving cost effectiveness. In addition, SaaS applications support various services such as email, FTP, and multimedia services with different delay requirement. Among these application multimedia service has relatively low delay requirements. Therefore, in this paper, we propose a scheduling algorithm to enhance both deadline guarantee and resource utilization. We modified the conservative backfilling algorithm by utilizing the earliest deadline first (EDF) algorithm and the largest weight first (LWF) algorithm. The proposed algorithm first score all the jobs arrived at the data center(DC) and sort the jobs in ascending order to serve high priority job first. The proposed algorithm then select the largest possible backfill job as guaranteeing deadline. Simulation results show that the proposed algorithm significantly improve the performance in terms of resource utilization and deadline guarantee.

[2]  A Suresh,et al.  Improving scheduling of backfill algorithms using balanced spiral method for cloud metascheduler , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[3]  A. S. Radhamani,et al.  Network Traffic Monitoring and Control for Multi core processors in cloud computing applications , 2012 .

[4]  Bin Chen,et al.  Backfilling under Two-tier Virtual Machines , 2012, 2012 IEEE International Conference on Cluster Computing.

[5]  Nong Ye,et al.  Job Scheduling Methods for Reducing Waiting Time Variance , 2022 .

[6]  E. Ramaraj,et al.  An efficient Tri Queue job Scheduling using dynamic quantum time for cloud environment , 2013, 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE).

[7]  Sanjay Chaudhary,et al.  Policy based resource allocation in IaaS cloud , 2012, Future Gener. Comput. Syst..

[8]  David A. Lifka,et al.  The ANL/IBM SP Scheduling System , 1995, JSSPP.

[9]  Dror G. Feitelson,et al.  Utilization, Predictability, Workloads, and User Runtime Estimates in Scheduling the IBM SP2 with Backfilling , 2001, IEEE Trans. Parallel Distributed Syst..

[10]  Albert Y. Zomaya,et al.  Priority-Based Consolidation of Parallel Workloads in the Cloud , 2013, IEEE Transactions on Parallel and Distributed Systems.

[11]  Moni Naor,et al.  Job Scheduling Strategies for Parallel Processing , 2017, Lecture Notes in Computer Science.

[12]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[13]  James S. Collofello,et al.  Transaction Level Economics of Cloud Applications , 2011, 2011 IEEE World Congress on Services.

[14]  N. Nagaveni,et al.  Design and Implementation of an Efficient Two-level Scheduler for Cloud Computing Environment , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[15]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[16]  Paul Marshall,et al.  Improving Utilization of Infrastructure Clouds , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.