Efficient and Fair Bandwidth Scheduling in Cloud Environments

Mustafa Khaleel; Mengxia Zhu

doi:10.14500/aro.10441

Mustafa Khaleel Department of Computer, College of Science, University of Sulaimani, Kurdistan Region http://orcid.org/0000-0002-2520-6924
Mengxia Zhu Computer Science Department, College of Science and Mathematics, Montclair State University, New Jersey, USA http://orcid.org/0000-0003-1715-4041

Keywords: Cloud Infrastructure, Cloud Scheduler, Pay-as-you-go, Makespan

Abstract

Hundreds of thousands of servers from data centers are operated to provide users with pay-as-yougo infrastructure as a service, platform as a service, and software as a service. Many different types of virtual machine (VM) instances hosted on these servers oftentimes need to efficiently communicate with data movement under current bandwidth capacity. This motivates providers to seek for a bandwidth scheduler to satisfy objectives, namely assuring the minimum bandwidth per VM for the guaranteed deadline and eliminating network congestion as much as possible. Based on some rigorous mathematical models, we formulated a cloud-based bandwidth scheduling algorithm which enables dynamic and fair bandwidth management by categorizing the total bandwidth into several categories and adjusting the allocated bandwidth limit per VM for both upstream and downstream traffics in real time. The simulation showed that paradigm was able to utilize the total assigned bandwidth more efficiently compared to algorithms such as bandwidth efficiency persistence proportional sharing (PPS), PPS, and PS at the network level.

Downloads

Download data is not yet available.

Author Biographies

Mustafa Khaleel, Department of Computer, College of Science, University of Sulaimani, Kurdistan Region

Mustafa Ibrahim Khaleel is a memeber of IEEE and Head of Computer Science Department at the College of Science, University of Sulaimani

Mengxia Zhu, Computer Science Department, College of Science and Mathematics, Montclair State University, New Jersey, USA

Mengxia Zhu is a member of IEEE and working at the Department of Computer Science, Montclair State University, Montclair, NJ 07043

References

Anshul, G., Mor H.B., Rajarshi, D., and Charles, L., 2009. Optimal power allocation in server farms. Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems. (SIGMETRICS ‘09). ACM, New York, USA, pp.157-168.

Anthony, A. Jr., 2016. Improving Bandwidth Allocation in cloud Computing Environments via Bandwidth as A Service Partitioning. Available from: https://www.digital.library.txstate.edu/handle/10877/6315. [Last accessed on 2018 Aug 01].

Dara, K., Jeffery, O.K., James, E.H., Nagarajan, K., and Guofei J., 2009. Power and performance management of virtualized computing environments via look ahead control. Cluster Computing, 12(1), pp.1-15.

Dejene, B., Dzmitry, K., Fabrizio, G., Pascal B., and Albert. Y.Z., 2015. Energyefficient data replication in cloud computing datacenters. Cluster Computing, 18(1), pp.385-402.

Dingzhu, W., Guanding, Y., Rongpeng, L., Yan, C., and Geoffrey, Y.L., 2017. Results on energy-and spectral-efficiency tradeoff in cellular networks with fullduplex enabled base stations. IEEE Transactions on Wireless Communications, 16(3), pp.1494-1507.

Dinh-Mao, B., YongIk, Y., Eui-Nam, H., SungIk, J., and Sungyoung, L., 2017. Energy efficiency for cloud computing system based on predictive optimization. Journal of Parallel and Distributed Computing, 102, pp.103-114.

Giorgio, L.V., Samee. K., and Pascal B., 2013. Energy-efficient resource utilization in cloud computing. Large Scale Network-Centric Distributed Systems, 45, pp.377-408.

Jonathan, G.K., 2007. Estimating Total Power Consumption Report by Servers in the US and the World. Available from: http://www-sop.inria.fr/mascotte/Contrats/DIMAGREEN/wiki/uploads/Main/svrpwrusecompletefinal.pdf. [Last accessed on 2016 Jul19].

Maria, A.R., and Rajkumar, B., 2017. Budget-driven scheduling of scientific workflows in IaaS clouds with fine-grained billing periods. ACM Transactions onAutonomous and Adaptive Systems, 12(2), DOI: https://doi.org/10.1145/3041036.

Massoud, P., and Inkwon, H., 2010. Power and Performance Modeling in a Virtualized Server System. 39th International Conference on Parallel Processing Workshops (ICPPW), San Diego, CA, USA: September 13-16.

Pierre, D., and Josh, W., 2014. Scaling Up Energy Efficiency Across the Data Center Industry: Evaluating Key Drivers and Barriers. Available from: https://www.nrdc.org/sites/default/files/data-center-efficiency-assessment-IP.pdf. [Last accessed on 2014 Aug 29].

Richard, B., Eric, M., Bruce, N., Bill, T., Arman, S., John, S., Jonathan, K., Dale, S., and Peter, C., 2008. Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431. Lawrence Berkeley National Laboratory.

Saurabh, K.G., Chee, S.Y., Arun A., and Rajkumar, B., 2011. Environmentconscious scheduling of HPC applications on distributed cloud-oriented data centers. Journal of Parallel and Distributed Computing, 71(6), pp.732-749.

Saurabh, K.G., Chee, S.Y., Arun, A., and Rajkumar, B., 2009. Energy-Efficient Scheduling of HPC Applications in Cloud Computing Environments. Ithaca, New York, CoRR, abs/0909.1146.

Tarun, G., Ajit, S., and Aakanksha, A., 2012. Cloudsim: Simulator for cloud computing infrastructure and modeling. Procedia Engineering, (38), pp.3566-3572.

Xiang, S., and Nirwan, A., 2013. Improving Bandwidth Efficiency and fairness in cloud computing. IEEE Global Communications Conference (GLOBECOM). Atlanta, GA, USA: December 9-13.

Yogesh, S., Bahman, J., Weisheng, S., and Daniel, S., 2016. Reliability and energy efficiency in cloud computing systems: Survey and taxonomy. Journal of Network and Computer Applications, 74, pp.66-85.

Zhuofu, Z., Jun, P., Xiaoyong, Z., Kaiyang, L., and Fu, J., 2016. A gametheoretical approach for spectrum efficiency improvement in cloud-RAN. Mobile Information Systems, 2014, 11.