Elastic Resource Allocation for a Cloud-Based Web Caching System

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Published Mar 1, 2014
https://doi.org/10.47164/ijngc.v5i1.58
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Volume 5, Issue 1, March 2014

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Farhana Kabir
Washington State University
Travis Hall
Washington State University
Scott A Wallace
Washington State University
David Chiu
Washington State University

Abstract

Web and service applications are generally I/O bound and follow a highly skewed request distribution, ushering in potential for significant latency reduction by caching and reusing results. However, such web caches require manual resource allocation, and when deployed in the cloud, costs may further complicate the provisioning process. We propose a fully autonomous, self-scaling, and cost-aware cloud cache with the objective of accelerating data-intensive applications.  Our system, which is distributed over multiple cloud nodes, intelligently provisions resources at runtime based on user's cost and performance expectations while abstracting away the various low-level decisions regarding efficient cloud resource management and data placement within the cloud. Our prediction model lends the system the capability to auto-configure the optimal resource requirement to automatically scale itself up (or down) to accommodate demand peaks while staying within certain cost constraints and fulfilling the performance expectations.

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How to Cite
Farhana Kabir, Travis Hall, Scott A Wallace, & David Chiu. (2014). Elastic Resource Allocation for a Cloud-Based Web Caching System. International Journal of Next-Generation Computing, 5(1), 01–21. https://doi.org/10.47164/ijngc.v5i1.58
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References

  1. Amazon EC2. 2013. Amazon Auto Scaling. http://aws.amazon.com/autoscaling/.
  2. Amazon Web Services Inc. 2013. Amazon ElastiCache. http://aws.amazon.com/elasticache.
  3. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., and Zaharia, M. 2010. A view of cloud computing. Commun. ACM 53, 4 (Apr.), 50–58.
  4. Armbrust, et al., M. 2009. Above the clouds: A berkeley view of cloud computing. Tech. Rep. UCB/EECS2009-28, EECS Department, University of California, Berkeley. Feb.
  5. Barford, P. and Crovella, M. E. 1998. Generating representative Web workloads for network and server performance evaluation. In Proceedings of Performance ’98/SIGMETRICS ’98. 151–160.
  6. Bayer, R. and McCreight, E. 1970. Organization and maintenance of large ordered indices. In SIGFIDET ’70: Proceedings of the 1970 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control. ACM, New York, NY, USA, 107–141.
  7. Bhattacharjee, A. and Debnath, B. 2005. A new web cache replacement algorithm. In Communications, Computers and signal Processing, 2005. PACRIM. 2005 IEEE Pacific Rim Conference on. 420 – 423.
  8. Bicer, T., Chiu, D., and Agrawal, G. 2012. Time and cost sensitive data-intensive computing on hybrid clouds.In Proceedings of the 2012 IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid’12).
  9. Breslau, L., Cao, P., Fan, L., Phillips, G., and Shenker, S. 1999. Web Caching and Zipf-like Distributions: Evidence and Implications. In Proceedings of Infocom.
  10. Cardosa, et al., M. 2011. Exploring mapreduce efficiency with highly-distributed data. In MapReduce’11. ACM, 27–34.
  11. Chiu, D. and Agrawal, G. 2010. Evaluating caching and storage options on the amazon web services cloud. In Proceedings of the 2010 11th IEEE/ACM International Conference on Grid Computing, Brussels, Belgium.
  12. Chiu, D., Shetty, A., and Agrawal, G. 2010. Elastic cloud caches for accelerating service-oriented computations.In Proceedings of International Conference on High Performance Computing, Networking, Storage and Analysis (SC’10), New Orleans, LA, USA. 1–11.
  13. Chiu, D., Shetty, A., and Agrawal, G. 2011. Evaluating and optimizing indexing schemes for a cloud-based elastic key-value store. In Proceedings of the 11th IEEE International Symposium on Cluster Computing and the Grid (CCGRID). IEEE.
  14. Das, S., Agrawal, D., and El Abbadi, A. 2009. Elastras: an elastic transactional data store in the cloud.In Proceedings of the 2009 conference on Hot topics in cloud computing. HotCloud’09. USENIX Association, Berkeley, CA, USA.de Assuncao, M. D., di Costanzo, A., and Buyya, R. 2009. Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters. In Proceedings of HPDC’09. ACM, 141–150.
  15. Dutta, K., Guin, R., Banerjee, S., Chakrabarti, S., and Biswas, U. 2012. A smart job scheduling system for cloud computing service providers and users: Modeling and simulation. In Recent Advances in Information Technology (RAIT), 2012 1st International Conference on. 346 –351.
  16. Eicken, T. V. 2008. The Rightscale Blog. http://blog.rightscale.com/2008/04/23/animoto-facebook-scale-up/.
  17. Fitzpatrick, B. 2004. Distributed caching with memcached. Linux J. 2004, 5–.
  18. Gong, Z., Gu, X., and Wilkes, J. 2011. Press: Predictive elastic resource scaling for cloud systems. In Proceedings of the 6th IEEE/IFIP International Conference on Network and Services Management, CNSM 2010, Niagara Falls, Canada.
  19. Karger, et al., D. 1997. Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the world wide web. In ACM Symposium on Theory of Computing. 654–663.
  20. Karger, et al., D. 1999. Web caching with consistent hashing. In WWW’99: Proceedings of the 8th International Conference on the World Wide Web. 1203–1213.
  21. Lin, H., Ma, X., Archuleta, J., Feng, W.-c., Gardner, M., and Zhang, Z. 2010. Moon: Mapreduce on opportunistic environments. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. HPDC ’10. ACM, New York, NY, USA, 95–106.
  22. Mao, M. and Humphrey, M. 2011. Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis, SC11, Seattle, WA, USA.
  23. Mao, M., Li, J., and Humphrey, M. 2010. Cloud auto-scaling with deadline and budget constraints. In Proceedings of 11th ACM/IEEE International Conference on Grid Computing, GRID 2010, Brussels, Belgium.
  24. Marshall, P., Keahey, K., and Freeman, T. 2010. Elastic site: Using clouds to elastically extend site resources.In Proceedings of CCGrid’10. 43–52.
  25. Nissen, S. 2003. Implementation of a fast artificial neural network library (fann). Tech. rep., Department of Computer Science University of Copenhagen (DIKU). http://fann.sf.net.
  26. O’Neil, E. J., O’Neil, P. E., and Weikum, G. 1993. The lru-k page replacement algorithm for database disk buffering. In Proceedings of SIGMOD’93. ACM, New York, NY, USA, 297–306.
  27. Papadopoulos, A. and Katsaros, D. 2011. A-tree: Distributed indexing of multidimensional data for cloud computing environments. In Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on. 407 –414.
  28. Pham, T. V., Truong, H.-L., and Dustdar, S. 2011. Elastic high performance applications – a composition framework. In Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific. 416 –423.
  29. Podlipnig, S. and Bosz ¨ ormenyi, L. ¨ 2003. A survey of web cache replacement strategies. ACM Comput.Surv. 35, 4 (Dec.), 374–398.
  30. Psounis, K. and Prabhakar, B. 2001. A randomized web-cache replacement scheme. In INFOCOM 2001.
  31. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE.Vol. 3. 1407 –1415 vol.3.
  32. Ramaswamy, L., Liu, L., and Iyengar, A. 2005. Cache clouds: Cooperative caching of dynamic documents in edge networks. In Distributed Computing Systems, 2005. ICDCS 2005. Proceedings. 25th IEEE International Conference on. 229 –238.
  33. Reddy, M. and Fletcher, G. P. 1998. Intelligent web caching using document life histories: A comparison with existing cache management techniques. In In 3rd International WWW Caching Workshop. 35–50.
  34. Ruby Documents. Module:kernel (ruby 1.9.2). In http://www.ruby-doc.org/core-1.9.2/Kernel.html#method-irand.
  35. Shen, Z., Subbiah, S., Gu, X., and Wilkes, J. 2011. Cloudscale: Elastic resource scaling for multi-tenant cloud systems. In Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC’11, Cascais, Portugal.
  36. Vaquero, L. M., Rodero-Merino, L., and Buyya, R. 2011. Dynamically scaling applications in the cloud. Computer Communication Review 41, 1, 45–52.
  37. Wang, L., Tao, J., Kunze, M., Castellanos, A., Kramer, D., and Karl, W. 2008. Scientific cloud computing:
  38. Early definition and experience. In High Performance Computing and Communications, 2008. HPCC ’08. 10th IEEE International Conference on. 825 –830.
  39. Wong, K.-Y. 2006. Web cache replacement policies: a pragmatic approach. Network, IEEE 20, 1 (jan.-feb.), 28–34.
  40. Zhao, W. and Schulzrinne, H. 2003. Predicting the Upper Bound of Web Traffic Volume Using a Multiple Time Scale Approach. In Proceedings of WWW’03. Budapest Hungary.
  41. Zhu, T., Gandhi, A., Harchol-Balter, M., and Kozuch, M. 2012. Saving Cash by Using Less Cache. In HotCloud ’12. Boston, MA.

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