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Providing Geo-Elasticity in Geographically Distributed Clouds

Published:17 April 2018Publication History
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Abstract

Geographically distributed cloud platforms are well suited for serving a geographically diverse user base. However, traditional cloud provisioning mechanisms that make local scaling decisions are not adequate for delivering the best possible performance for modern web applications that observe both temporal and spatial workload fluctuations. We propose GeoScale, a system that provides geo-elasticity by combining model-driven proactive and agile reactive provisioning approaches. GeoScale can dynamically provision server capacity at any location based on workload dynamics. We conduct a detailed evaluation of GeoScale on Amazon’s geo-distributed cloud and show up to 40% improvement in the 95th percentile response time when compared to traditional elasticity techniques.

References

  1. Bernhard Ager, Wolfgang Mhlbauer, Georgios Smaragdakis, and Steve Uhlig. 2010. Comparing DNS resolvers in the wild. In Proceedings of the 2010 Internet Measurement Conference (IMC’10). Melbourne, Australia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Alicherry and T. V. Lakshman. 2012. Network aware resource allocation in distributed clouds. In Proceedings of INFOCOM 2012. IEEE, 963--971.Google ScholarGoogle Scholar
  3. Amazon Auto Scaling Service. 2013. AWS Auto Scaling. Retrieved from https://aws.amazon.com/autoscaling/.Google ScholarGoogle Scholar
  4. Amazon EBS Pricing. 2017. Amazon EBS Pricing. Retrieved from https://aws.amazon.com/ebs/pricing/.Google ScholarGoogle Scholar
  5. P. Bodik, A. Fox, M. J. Franklin, and M. I. Jordan. 2010. Characterizing, modeling, and generating workload spikes for stateful services. In Proceedings of the 1st ACM Symposium on Cloud Computing (SoCC). ACM, New York, 241--252. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility. In Proceedings of the 17th ACM SIGKDD International Conference. ACM Press, New York, 1082--1090. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C. Clark, K. Fraser, S. Hand, J. G. Hansen, and E. Jul. 2005. Live migration of virtual machines. In Proceedings of the 2nd Conference on Symposium on Networked Systems Design and Implementation (NSDI), Vol. 2. USENIX Association, Berkeley, CA, 273--286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Content Delivery Network 2013. Content Delivery Network. Retrieved from http://www.akamai.com/html/resources/content-distribution-network.html.Google ScholarGoogle Scholar
  9. Brian F. Cooper, Raghu Ramakrishnan, Utkarsh Srivastava, Adam Silberstein, Philip Bohannon, Hans-Arno Jacobsen, Nick Puz, Daniel Weaver, and Ramana Yerneni. 2008. PNUTS. Proceedings of the VLDB Endowment 1, 2 (Aug. 2008), 1277--1288. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Sudipto Das, Shoji Nishimura, Divyakant Agrawal, and Amr El Abbadi. 2011. Albatross: Lightweight elasticity in shared storage databases for the cloud using live data migration. Proceedings of the VLDB Endowment 4, 8 (May 2011), 494--505. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Aaron J. Elmore, Sudipto Das, Divyakant Agrawal, and Amr El Abbadi. 2011. Zephyr: Live migration in shared nothing databases for elastic cloud platforms. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. ACM, New York, 301--312. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Fabric Python Module. 2013. Fabric documentation. Retrieved from http://www.fabfile.org/.Google ScholarGoogle Scholar
  13. Tobias Flach, Nandita Dukkipati, Andreas Terzis, Barath Raghavan, Neal Cardwell, Yuchung Cheng, Ankur Jain, Shuai Hao, Ethan Katz-Bassett, and Ramesh Govindan. 2013. Reducing web latency: The virtue of gentle aggression. In Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM. ACM, New York, 159--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Gandhi, P. Dube, and A. Karve. 2014. Adaptive, model-driven autoscaling for cloud applications. In Proceedings of the 11th International Conference on Autonomic Computing (ICAC'14). USENIX Association, Philadelphia, PA, 57--64. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. 2007. Workload analysis and demand prediction of enterprise data center applications. In Proceedings of the IEEE 10th International Symposium on Workload Characterization (IISWC 2007). IEEE, Washington, DC, 171--180. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Tian Guo, Upendra Sharma, Prashant Shenoy, Timothy Wood, and Sambit Sahu. 2013. Cost-aware cloud bursting for enterprise applications. Transactions on Internet Technology. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. T. Guo, P. Shenoy, and H. Hakan. 2015. GeoScale: Providing Geo-Elasticity in Distributed Clouds. Technical Report UM-CS-2015-009. School of Computer Science, University of Massachusetts at Amherst.Google ScholarGoogle Scholar
  18. T. Guo, P. Shenoy, and H. Hakan. 2016. GeoScale: Providing geo-elasticity in distributed clouds. In International Conference on Cloud Engineering (IC2E). IEEE, Berlin, 123--126.Google ScholarGoogle Scholar
  19. J. L. Hellerstein, Fan Zhang, and P. Shahabuddin. 1999. An approach to predictive detection for service management. In Proceedings of the 6th IFIP/IEEE International Symposium on Integrated Network Management (INM). IEEE, Boston, MA, 309--322.Google ScholarGoogle Scholar
  20. J. F. C. Kingman. 1961. The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society 57 (1961), 902--904.Google ScholarGoogle ScholarCross RefCross Ref
  21. Thomas Knauth and Christof Fetzer. 2011. Scaling non-elastic applications using virtual machines. In Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, Washington, DC, 468--475. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Tim Kraska, Gene Pang, Michael J. Franklin, Samuel Madden, and Alan Fekete. 2013. MDCC: Multi-data center consistency. In Proceedings of the 8th ACM European Conference on Computer Systems (EuroSys). ACM, New York, 113--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. H. A. Lagar-Cavilla, J. A. Whitney, and A. M. Scannell. 2009. SnowFlock: Rapid virtual machine cloning for cloud computing. In Proceedings of the 4th ACM European Conference on Computer Systems (EuroSys). ACM, New York, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Ewnetu Bayuh Lakew, Cristian Klein, Francisco Hernandez-Rodriguez, and Erik Elmroth. 2014. Towards faster response time models for vertical elasticity. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC’14). IEEE Computer Society, Washington, DC, 560--565. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. H. C. Lim, S. Babu, and J. S. Chase. 2010. Automated control for elastic storage. In Proceedings of the 7th International Conference on Autonomic Computing (ICAC). ACM, New York, 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Zhenhua Liu, Minghong Lin, Adam Wierman, Steven H. Low, and Lachlan L. H. Andrew. 2011. Greening geographical load balancing. In Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS’11). ACM, New York, 233--244. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Simon J. Malkowski, Markus Hedwig, Jack Li, Calton Pu, and Dirk Neumann. 2011. Automated control for elastic n-tier workloads based on empirical modeling. In Proceedings of the 8th ACM International Conference on Autonomic Computing (ICAC). ACM, New York, 131--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. M. Mao and M. Humphrey. 2012. A performance study on the VM startup time in the cloud. In Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 423--430. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. P. Marshall, K. Keahey, and T. Freeman. 2010. Elastic site: Using clouds to elastically extend site resources. In Proceedings of the 2019 10th International Conference on Cluster, Cloud and Grid Computing (CCGrid). IEEE Computer Society, Washington, DC, 43--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Maxmind GeoIP Service. 2013. IP Geolocation and Online Fraud Prevention: MaxMind. Retrieved from https://www.maxmind.com/en/home.Google ScholarGoogle Scholar
  31. Nathan D. Mickulicz, Priya Narasimhan, and Rajeev Gandhi. 2013. To auto scale or not to auto scale. In Proceedings of the 10th International Conference on Autonomic Computing (ICAC'13). USENIX, 145--151.Google ScholarGoogle Scholar
  32. F. J. A. Morais, F. V. Brasileiro, R. V. Lopes, R. A. Santos, W. Satterfield, and L. Rosa. 2013. Autoflex: Service agnostic auto-scaling framework for IaaS deployment models. In Proceedings of the 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). USENIX, 42--49.Google ScholarGoogle Scholar
  33. T. S. E. Ng and Hui Zhang. 2002. Predicting internet network distance with coordinates-based approaches. In INFOCOM. USENIX, 170--179.Google ScholarGoogle Scholar
  34. Hiep Nguyen, Zhiming Shen, Xiaohui Gu, Sethuraman Subbiah, and John Wilkes. 2013. Agile: Elastic distributed resource scaling for infrastructure-as-a-service. In Proceedings of the 10th International Conference on Autonomic Computing (ICAC’13). USENIX, 69--82.Google ScholarGoogle Scholar
  35. John O’Loughlin and Lee Gillam. 2014. Performance evaluation for cost-efficient public infrastructure cloud use. In Proceedings of the 11th International Conference on Economics of Grids, Clouds, Systems, and Services (GECON 2014), Cardiff UK, September 16-18, 2014. Revised Selected Papers. 133--145.Google ScholarGoogle ScholarCross RefCross Ref
  36. P. N. Shankaranarayanan, Ashiwan Sivakumar, Sanjay Rao, and Mohit Tawarmalani. 2014. Performance sensitive replication in geo-distributed cloud datastores. In Proceedings of the 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 240--251. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Pradeep Padala, Kai-Yuan Hou, Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, Sharad Singhal, and Arif Merchant. 2009. Automated control of multiple virtualized resources. In Proceedings of the 4th ACM European Conference on Computer Systems (EuroSys). ACM, New York, 13--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Marta Patiño Martinez, Ricardo Jiménez-Peris, Bettina Kemme, and Gustavo Alonso. 2005. MIDDLE-R: Consistent database replication at the middleware level. ACM Transactions on Computer Systems 23, 4 (2005), 375--423. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Josep M. Pujol, Vijay Erramilli, Georgos Siganos, Xiaoyuan Yang, Nikos Laoutaris, Parminder Chhabra, and Pablo Rodriguez. 2010. The little engine(s) that could: Scaling online social networks. In Proceedings of the ACM SIGCOMM 2010 Conference on SIGCOMM. ACM, New York, 375--386. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Shriram Rajagopalan, Dan Williams, Hani Jamjoom, and Andrew Warfield. 2013. Split/merge: System support for elastic execution in virtual middleboxes. In Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation (NSDI). USENIX Association, Berkeley, CA, 227--240. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Mahadev Satyanarayanan, P. Bahl, R. Caceres, and N. Davies. 2009. The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing 8, 4 (2009), 14--23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Jörg Schad, Jens Dittrich, and Jorge-Arnulfo Quiané-Ruiz. 2010. Runtime measurements in the cloud: Observing, analyzing, and reducing variance. Proceedings of the VLDB Endowment 3, 1--2 (Sept. 2010), 460--471. 2150-8097 Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Scryer. 2013. Scryer: Netflix’s Predictive Auto Scaling Engine. https://medium.com/netflix-techblog/scryer-netflixs-predictive-auto-scaling-engine-a3f8fc922270.Google ScholarGoogle Scholar
  44. Abhishek B. Sharma, Ranjita Bhagwan, Monojit Choudhury, Leana Golubchik, Ramesh Govindan, and Geoffrey M. Voelker. 2008. Automatic request categorization in internet services. SIGMETRICS Performance Evaluation Review 36, 2 (2008), 16--25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Prateek Sharma, Stephen Lee, Tian Guo, David Irwin, and Prashant Shenoy. 2015. SpotCheck: Designing a derivative IaaS cloud on the spot market. In Proceedings of the 10th European Conference on Computer Systems (EuroSys’15). ACM, New York, 16:1--16:15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Zhiming Shen, Sethuraman Subbiah, Xiaohui Gu, and John Wilkes. 2011. CloudScale: Elastic resource scaling for multi-tenant cloud systems. In Proceedings of the 2nd ACM Symposium on Cloud Computing (SoCC). ACM, New York, 5:1--5:14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Rahul Singh, Upendra Sharma, Emmanuel Cecchet, and Prashant Shenoy. 2010. Autonomic mix-aware provisioning for non-stationary data center workloads. In Proceedings of the 7th International Conference on Autonomic Computing (ICAC). ACM, New York, 21--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. stopped vs terminated instances. 2017. What is the difference between terminating and stopping an EC2 instance? Retrieved from http://docs.rightscale.com/faq/clouds/aws/Whats_the_difference_between_Terminating_and_Stopping_an_EC2_Instance.html.Google ScholarGoogle Scholar
  49. Stream Control Transmission Protocol. 2013. Stream Control Transmission Protocol. Retrieved from http://tools.ietf.org/html/draft-natarajan-http-over-sctp-00.Google ScholarGoogle Scholar
  50. The ObjectWeb TPC-W implementation. 2005. The ObjectWeb TPC-W implementation. Retrieved from http://jmob.ow2.org/tpcw.html.Google ScholarGoogle Scholar
  51. Omesh Tickoo, Ravi Iyer, Ramesh Illikkal, and Don Newell. 2010. Modeling virtual machine performance: Challenges and approaches. SIGMETRICS Performance Evaluation Review 37, 3 (Jan. 2010), 55--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. R. Tolosana-Calasanz, J. Diaz-Montes, O. Rana, and M. Parashar. 2014. Extending cometcloud to process dynamic data streams on heterogeneous infrastructures. In Proceedings of the 2014 International Conference on Cloud and Autonomic Computing (ICCAC). 196--205. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. D. Tsoumakos, I. Konstantinou, C. Boumpouka, S. Sioutas, and N. Koziris. 2013. Automated, elastic resource provisioning for NoSQL clusters using TIRAMOLA. In Proceedings of the 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). 34--41.Google ScholarGoogle Scholar
  54. B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal. 2005. Dynamic provisioning of multi-tier internet applications. In Proceedings of the 2nd International Conference on Autonomic Computing (ICAC’05). 217--228. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Timothy Wood, Prashant Shenoy, Arun Venkataramani, and Mazin Yousif. 2007. Black-box and gray-box strategies for virtual machine migration. In Proceedings of the 4th USENIX Conference on Networked Systems Design and Implementation (NSDI’07). USENIX Association, Berkeley, CA, 17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Q. Zhang, Q. Zhu, M. F. Zhani, R. Boutaba, and J. L. Hellerstein. 2013. Dynamic service placement in geographically distributed clouds. IEEE Journal on Selected Areas in Communications 31, 12 (December 2013), 762--772.Google ScholarGoogle ScholarCross RefCross Ref

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        • Published in

          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 18, Issue 3
          Special Issue on Artificial Intelligence for Secruity and Privacy and Regular Papers
          August 2018
          314 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3185332
          • Editor:
          • Munindar P. Singh
          Issue’s Table of Contents

          Copyright © 2018 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 17 April 2018
          • Accepted: 1 November 2017
          • Revised: 1 October 2017
          • Received: 1 March 2016
          Published in toit Volume 18, Issue 3

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