Abstract
Cloud Computing represents a paradigm shift in computing. It advocates the use of computing resources as a service rather than as a product. The numerous advantages which the Cloud offers has led to many users adopting it at a phenomenal rate. Providing service to this ever growing number of users in a fast and effective manner is a major challenge. Numerous researchers have proposed various approaches to scheduling user workloads, notable among which are the First-Come-First-Serve and Weight Round Robin (WRR), and have obtained varied levels of successes. Unfairness and excess allocation delay are some of the shortcomings of these approach. There is also the assumption that all Cloud users’ workloads belong to a single class of requirement. This work proposes an efficient and fair Cloud workload scheduling algorithm called Adaptive Carry-On Weighted Round Robin (ACWRR), and also takes into consideration multiple workloads classes. Experimental simulations were conducted with ACWRR benchmarked against WRR. Results show that ACWRR performs better than WRR by at least 13% in terms of system latency and 38% for makespan.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mell, P., Grance, T.: The NIST Definition of Cloud Computing. NIST Special Publication 800-145. Whitepaper, NIST (2011). http://acm.org/citation.cfm?id=2206223
Cisco Inc.: Cisco Global Cloud Index: Forecast and Methodology, 2015–2020. Whitepaper, Cisco (2016). http://www.cisco.com
Gartner: Worldwide Public Cloud Services Market to Grow 17 Percent in 2016, Press Release, Gartner (2016). http://www.gartner.com/newsroom/id/3443517
Ajayi, O., Oladeji, F., Uwadia, C.: Multi-class load balancing for QoS and energy conservation in cloud computing. West Afr. J. Ind. Acad. Res. 17, 28–36 (2016)
Sotomayor, B., Montero, R., Llorente, I., Foster, I.: Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput. 13(5), 14–22 (2009). IEEE
Mahajan, K., Makroo, A., Dahiya, D.: Round robin with server affinity: a VM load balancing algorithm for cloud based infrastructure. J. Inf. Process Syst. 9(3), 379–394 (2013)
Patel, D., Rajawat, A.: Efficient throttled load balancing algorithm in cloud environment. Int. J. Modern Trends Eng. Res. 2(3), 463–480 (2015)
Wickremasinghe, B., Calheiros, R., Buyya, R.: Cloudanalyst: a cloudsim-based visual modeller for analysing cloud computing environments and applications. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 446–452. IEEE (2010)
Lu, Y., Xie, G., Kliot, G., Geller, A., Larus, J., Greenberg, A.: Join-Idle-Queue: a novel load balancing algorithm for dynamically scalable web services. Perform. Eval. 68(11), 1056–1071 (2011)
Mitzenmacher, M.: Analyzing distributed Join-Idle-Queue: a fluid limit approach. In: 2016 54th Annual Allerton Conference on Communication, Control and Computing (Allerton), pp. 312–318. IEEE (2016)
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurrency Comput.: Pract. Experience 24(13), 1397–1420 (2012)
Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N., Tenhunen, H.: Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans. Cloud Comput. 13 (2016). http://ieeexplore.ieee.org/document/7593250/
Oladeji, F., Oyetunji, M., Okunoye, O.: CWRR: a scheduling algorithm for maximizing performance of quality of service network router. Int. J. Comput. Appl. 41(2), 30–34 (2012)
Shimonishi, H., Yoshida, M., Fan, R., Suzuki, H.: An improvement of weighted round robin cell scheduling in ATM networks. In: Global Telecommunications Conference, GLOBECOM 1997, vol. 2, pp. 1119–1123. IEEE (1997)
Shreedar, M., Varghese, G.: Efficient fair queuing using deficit round robin. IEEE/ACM Trans. Netw. 4(3), 375–385 (1996). ACM
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency Comput.: Pract. Experience (CCPE) 24(13), 1397–1420 (2012)
Park, K., Pai, V.: CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper. Syst. Rev. 40(1), 65–74 (2006). ACM
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ajayi, O., Oladeji, F., Uwadia, C., Omosowun, A. (2018). Scheduling Cloud Workloads Using Carry-On Weighted Round Robin. In: Odumuyiwa, V., Adegboyega, O., Uwadia, C. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 250. Springer, Cham. https://doi.org/10.1007/978-3-319-98827-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-98827-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98826-9
Online ISBN: 978-3-319-98827-6
eBook Packages: Computer ScienceComputer Science (R0)