QoS Aware Load Balancing in Multi-tenant Cloud Environments

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Susara Lakmal De Saram
Srinath Perera
Mahen Jayawardane

Abstract

Current enterprise cloud services need to meet the challenge of serving customers, who expect different service levels, while achieving higher resource utilization. To catch a dominant market share, cloud vendors often need to provide different kinds of offerings to meet the emerging needs of their customers, rather than having a “one size fits all approach. Multi-tenancy technology has dramatically increased the level of resource sharing. The improved sharing makes it challenging to provide differentiated services without losing the key benefits achieved by resource sharing. In this research, we investigated into the problem of serving different classes of users with different levels of Quality of Service, adhering to multi-tenancy. We propose a dynamic load balancing policy based on the principals of Queuing Theory to offer differentiated services. Our solution achieves the differentiation by using the level of concurrency in servers as the key attribute. The mechanism is self-adaptive to dynamic load conditions thus able to maintain the desired distance in average service times among the service classes. We implemented and tested the system on a prototype of a commercial cloud platform. Furthermore, we demonstrate the possibility of extending the proposed mechanism to support auto scaling as well.

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How to Cite
Susara Lakmal De Saram, Srinath Perera, & Mahen Jayawardane. (2013). QoS Aware Load Balancing in Multi-tenant Cloud Environments. International Journal of Next-Generation Computing, 4(1), 28–44. https://doi.org/10.47164/ijngc.v4i1.43

References

  1. Azeez, A., Perera, S., Gamage, D., Linton, R., Siriwardana, P., Leelaratne, D., Weerawarana, S., and Fremantle, P. 2010. Multi-tenant SOA middleware for cloud computing. In Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing. CLOUD '10. IEEE Computer Society, Washington, DC, USA, 458-465.
  2. Azeez, A., Perera, S., Weerawarana, S., Fremantle, P., Uthaiyashankar, S., and Abesinghe, S. 2011. Wso2 stratos: An application stack to support cloud computing. it - Information Technology 53, 4, 180-187.
  3. Bianco, P., Lewis, G., and Merson, P. 2008. Service level agreements in service-oriented architecture envi- ronments. Tech. Rep. CMU/SEI-2008-TN-021, Software Engineering Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  4. Broadwell, P. M. 2004. Response time as a performability metric for online services. Tech. Rep. UCB/CSD- 04-1324, EECS Department, University of California, Berkeley. May.
  5. Buyya, R., Yeo, C. S., and Venugopal, S. 2008. Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. In Department of Computer Science and Software Engineering (CSSE), The University of Melbourne, Australia. He. 10-1016.
  6. Chen, H. and Li, S. 2010. A queueing-based model for performance management on cloud. In Advanced Infor- mation Management and Service (IMS), 2010 6th International Conference on. 83-88.
  7. Chong, F. and Carraro, G. 2006. Architecture strategies for catching the long tail. Tech. rep., MSDN Library, Microsoft Corporation.
  8. D. Banks, J. Erickson, M. R. 2009. Multi-tenancy in cloud-based collaboration services. Tech. Rep. HPL-2009- 17, HP Laboratories. February.
  9. Goudarzi, H. and Pedram, M. 2011. Maximizing profit in cloud computing system via resource allocation. In Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops. ICDCSW '11. IEEE Computer Society, Washington, DC, USA, 1-6.
  10. Hayel, Y., Ros, D., and Tuffin, B. 2004. Less-than-best-e ort services: Pricing and scheduling. In INFOCOM (2005-04-18).
  11. Khazaei, H., Jelena;, and B.;, V. 2012. Performance analysis of cloud computing centers using m/g/m/m+r queuing systems. IEEE Transactions on Parallel and Distributed Systems 23, 936-943.
  12. Little, J. D. C. 2011. Or forum|little's law as viewed on its 50th anniversary. Oper. Res. 59, 3 (May), 536-549.
  13. Lu, C., Abdelzaher, T. F., Stankovic, J. A., and Son, S. H. 2001. A feedback control approach for guaranteeing relative delays in web servers. In Proceedings of the Seventh Real-Time Technology and Applications Symposium (RTAS '01). RTAS '01. IEEE Computer Society, Washington, DC, USA, 51.
  14. Lucas, J. M., Saccucci, M. S., Baxley, Jr., R. V., Woodall, W. H., Maragh, H. D., Faltin, F. W., Hahn, G. J., Tucker, W. T., Hunter, J. S., MacGregor, J. F., and Harris, T. J. 1990. Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32, 1 (Jan.), 1-29.
  15. Mudigonda, J., Yalagandula, P., Mogul, J. C., Stiekes, B., and Pouffary, Y. 2011. Netlord: a scalable multi-tenant network architecture for virtualized datacenters. In SIGCOMM'11. 62-73.
  16. Simchi-Levi, D. and Trick, M. A. 2011. Introduction to little's law as viewed on its 50th anniversary. Oper. Res. 59, 3 (May), 535-535.
  17. Susitaival, R. and Aalto, S. 2003. Providing di erentiated services by load balancing and scheduling in mpls networks. Tech. Rep. TD(03)03, COST279. Jan.
  18. Zhang, S., Zhang, S., Chen, X., and Huo, X. 2010. Cloud computing research and development trend. In Proceedings of the 2010 Second International Conference on Future Networks. ICFN '10. IEEE Computer Society, Washington, DC, USA, 93-97.