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HCloud: Resource-Efficient Provisioning in Shared Cloud Systems

Published:25 March 2016Publication History
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Abstract

Cloud computing promises flexibility and high performance for users and cost efficiency for operators. To achieve this, cloud providers offer instances of different sizes, both as long-term reservations and short-term, on-demand allocations. Unfortunately, determining the best provisioning strategy is a complex, multi-dimensional problem that depends on the load fluctuation and duration of incoming jobs, and the performance unpredictability and cost of resources. We first compare the two main provisioning strategies (reserved and on-demand resources) on Google Compute Engine (GCE) using three representative workload scenarios with batch and latency-critical applications. We show that either approach is suboptimal for performance or cost. We then present HCloud, a hybrid provisioning system that uses both reserved and on-demand resources. HCloud determines which jobs should be mapped to reserved versus on-demand resources based on overall load, and resource unpredictability. It also determines the optimal instance size an application needs to satisfy its Quality of Service (QoS) constraints. We demonstrate that hybrid configurations improve performance by 2.1x compared to fully on-demand provisioning, and reduce cost by 46% compared to fully reserved systems. We also show that hybrid strategies are robust to variation in system and job parameters, such as cost and system load.

References

  1. Amazon ec2. http://aws.amazon.com/ec2/.Google ScholarGoogle Scholar
  2. K. Annapureddy. Security challenges in hybrid cloud infrastructures. In Aalto University, T-110.5290 Seminar on Network Security. 2010.Google ScholarGoogle Scholar
  3. Autoscale. https://cwiki.apache.org/cloudstack/autoscaling.html.Google ScholarGoogle Scholar
  4. Aws autoscaling. http://aws.amazon.com/autoscaling/.Google ScholarGoogle Scholar
  5. G. Banga, P. Druschel, and J. Mogul. Resource containers: a new facility for resource management in server systems. In Proceeedings of OSDI. New Orleans, 1999.Google ScholarGoogle Scholar
  6. S. K. Barker and P. Shenoy. Empirical evaluation of latency-sensitive application performance in the cloud. In Proceedings of the First Annual ACM SIGMM Conference on Multimedia Systems (MMsys). Scottsdale, AR, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. L. Barroso. Warehouse-scale computing: Entering the teenage decade. ISCA Keynote, SJ, June 2011.Google ScholarGoogle Scholar
  8. L. Barroso and U. Hoelzle. The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines. MC Publishers, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. O. A. Ben-Yehuda, M. Ben-Yehuda, A. Schuster, and D. Tsafrir. Deconstructing amazon ec2 spot instance pricing. In ACM TEAC, 1(3), September 2013.Google ScholarGoogle Scholar
  10. G. Breiter and V. Naik. A framework for controlling and managing hybrid cloud service integration. In Proceedings of the 2013 IEEE International Conference on Cloud Engineering (IC2E). Redwood City, CA, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Carvalho, W. Cirne, F. Brasileiro, and J. Wilkes. Long-term slos for reclaimed cloud computing resources. In Proceedings of ACM Symposium on Cloud Computing (SOCC). Seattle, WA, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. Chase, D. Anderson, P. Thakar, A. Vahdat, and R. Doyle. Managing energy and server resources in hosting centers. In Proceedings of the Symposium on Operating Systems Principles (SOSP). Banff, CA, 2001.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Linux containers. http://lxc.sourceforge.net/.Google ScholarGoogle Scholar
  14. J. Dean and L. A. Barroso. The tail at scale. In Communications of the ACM (CACM), Vol. 56 No. 2, Pages 74--80.Google ScholarGoogle Scholar
  15. E. Deelman, G. Singh, M. Livny, B. Berriman, and J. Good. The cost of doing science on the cloud: The montage example. In Proceedings of Supercomputing (SC). Austin, TX, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  16. C. Delimitrou, N. Bambos, and C. Kozyrakis. QoS-Aware Admission Control in Heterogeneous Datacenters. In Proceedings of the International Conference of Autonomic Computing (ICAC). San Jose, CA, USA, 2013.Google ScholarGoogle Scholar
  17. C. Delimitrou and C. Kozyrakis. iBench: Quantifying Interference for Datacenter Workloads. In Proceedings of the IEEE International Symposium on Workload Characterization (IISWC). Portland, OR, September 2013.Google ScholarGoogle Scholar
  18. C. Delimitrou and C. Kozyrakis. Paragon: QoS-Aware Scheduling for Heterogeneous Datacenters. In Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). Houston, TX, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. C. Delimitrou and C. Kozyrakis. QoS-Aware Scheduling in Heterogeneous Datacenters with Paragon. In ACM Transactions on Computer Systems (TOCS), Vol. 31 Issue 4. December 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. C. Delimitrou and C. Kozyrakis. Quality-of-Service-Aware Scheduling in Heterogeneous Datacenters with Paragon. In IEEE Micro Special Issue on Top Picks from the Computer Architecture Conferences. May/June 2014.Google ScholarGoogle Scholar
  21. C. Delimitrou and C. Kozyrakis. Quasar: Resource-Efficient and QoS-Aware Cluster Management. In Proceedings of the Nineteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). Salt Lake City, UT, USA, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. C. Delimitrou, D. Sanchez, and C. Kozyrakis. Tarcil: Reconciling Scheduling Speed and Quality in Large Shared Clusters. In Proceedings of the Sixth ACM Symposium on Cloud Computing (SOCC). Kohala Coast, HI, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. B. Farley, A. Juels, V. Varadarajan, T. Ristenpart, K. D. Bowers, and M. M. Swift. More for your money: Exploiting performance heterogeneity in public clouds. In Proceedings of the ACM Symposium on Cloud Computing (SOCC). San Jose, CA, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Google compute engine. https://developers.google.com/compute/.Google ScholarGoogle Scholar
  25. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Workload analysis and demand prediction of enterprise data center applications. In Proceedings of the IEEE International Symposium on Workload Characterization (IISWC). Boston, MA, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Z. Gong, X. Gu, and J. Wilkes. Press: Predictive elastic resource scaling for cloud systems. In Proceedings of the International Conference on Network and Service Management (CNSM). Niagara Falls, ON, 2010.Google ScholarGoogle Scholar
  27. G. R. Grimmett and D. R. Stirzaker. Probability and random processes. 2nd Edition. Clarendon Press, Oxford, 1992.Google ScholarGoogle Scholar
  28. M. Guevara, B. Lubin, and B. Lee. Navigating heterogeneous processors with market mechanisms. In Proceedings of the IEEE Symposium on High Performance Computer Architecture (HPCA). Shenzhen, China, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. J. Hamilton. Cost of power in large-scale data centers. http://perspectives.mvdirona.com.Google ScholarGoogle Scholar
  30. B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. Joseph, R. Katz, S. Shenker, and I. Stoica. Mesos: A platform for fine-grained resource sharing in the data center. In Proceedings of the USENIX Sumposium on Networked Systems Design and Implementation (NSDI). Boston, MA, 2011.Google ScholarGoogle Scholar
  31. M. Hoseinyfarahabady, H. Samani, L. Leslie, Y. C. Lee, and A. Zomaya. Handling uncertainty: Pareto-efficient bot scheduling on hybrid clouds. In Proceedings of the International Conference for Parallel Processing (ICPP). Lyon, France, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. A. Iosup, N. Yigitbasi, and D. Epema. On the performance variability of production cloud services. In Proceedings of IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). Newport Beach, CA, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. V. Khadilkar, K. Y. Oktay, B. Hore, M. Kantarcioglu, S. Mehrotra, and B. Thuraisingham. Risk-aware data processing in hybrid clouds. TR-UTDCS-31--11, 2011.Google ScholarGoogle Scholar
  34. Y. E. Khamra, H. Kim, S. Jha, and M. Parashar. Exploring the performance fluctuations of hpc workloads on clouds. In Proceedings of IEEE International Conference on Cloud Computing Technology and Science (CloudCom). Indianapolis, IN, 2010.Google ScholarGoogle Scholar
  35. L. Kleinrock. Queueing systems volume 1: Theory. pp. 101--103, 404.Google ScholarGoogle Scholar
  36. E. Lau, J. E. Miller, I. Choi, D. Yeung, S. Amarasinghe, and A. Agarwal. Multicore performance optimization using partner cores. In Proceedings of the USENIX Workshop on Hot Topics in Parallelism (HotPar). Berkeley, CA, 2011.Google ScholarGoogle Scholar
  37. J. Laudon. Performance/watt: the new server focus. In ACM SIGARCH Computer Architecture News: dasCMP. Vol. 33 Issue 4, p. 5--13, November 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. A. Li, X. Yang, S. Kandula, and M. Zhang. Cloudcmp: Comparing public cloud providers. In Proceedings of Internet Measurement Conference (IMC). Melbourne, Australia, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Host server cpu utilization in amazon ec2 cloud. http://goo.gl/nCfYFX.Google ScholarGoogle Scholar
  40. J. Lorch and A. Smith. Reducing processor power consumption by improving processor time management in a single-user operating system. In Proceedings of Annual International Conference on Mobile Computing and Networking (Mobicom). New York, NY, 1996.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Mahout. http://mahout.apache.org/.Google ScholarGoogle Scholar
  42. D. Mangot. Ec2 variability: The numbers revealed. http://goo.gl/NAH2lm.Google ScholarGoogle Scholar
  43. J. Mars and L. Tang. Whare-map: heterogeneity in "homogeneous" warehouse-scale computers. In Proceedings of the International Symposium on Computer Architecture (ISCA). Tel-Aviv, Israel, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. J. Mars, L. Tang, R. Hundt, K. Skadron, and M. L. Soffa. Bubble-up: increasing utilization in modern warehouse scale computers via sensible co-locations. In Proceedings of the IEEE/ACM International Symposium on Microarchitecture (MICRO). Porto Alegre, Brazil, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. S. M. Martin, K. Flautner, T. Mudge, and D. Blaauw. Combined dynamic voltage scaling and adaptive body biasing for lower power microprocessors under dynamic workloads. In Proceedings of International Conference On Computer Aided Design (ICCAD). 2002.Google ScholarGoogle Scholar
  46. R. Nathuji, C. Isci, and E. Gorbatov. Exploiting platform heterogeneity for power efficient data centers. In Proceedings of the International Conference on Autonomic Computing (ICAC). Jacksonville, FL, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. R. Nathuji, A. Kansal, and A. Ghaffarkhah. Q-clouds: Managing performance interference effects for qos-aware clouds. In Proceedings of EuroSys France, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. H. Nguyen, Z. Shen, X. Gu, S. Subbiah, and J. Wilkes. Agile: Elastic distributed resource scaling for infrastructure-as-a-service. In Proceedings of the International Conference on Autonomic Computing (ICAC). 2013.Google ScholarGoogle Scholar
  49. D. Novakovic, N. Vasic, S. Novakovic, D. Kostic, and R. Bianchini. Deepdive: Transparently identifying and managing performance interference in virtualized environments. In Proceedings of the USENIX Annual Technical Conference (ATC). San Jose, CA, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. S. Ostermann, A. Iosup, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema. A performance analysis of ec2 cloud computing services for scientific computing. In Lecture Notes on Cloud Computing. Volume 34, p.115--131, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  51. Z. Ou, H. Zhuang, J. K. Nurminen, A. Yla-Jaaski, and P. Hui. Exploiting hardware heterogeneity within the same instance type of amazon ec2. In HotCloud. 2012.Google ScholarGoogle Scholar
  52. K. Ousterhout, P. Wendell, M. Zaharia, and I. Stoica. Sparrow: Distributed, low latency scheduling. In Proceedings of the Symposium on Operating Systems Principles (SOSP). Farminton, PA, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. M. Rehman and M. Sakr. Initial findings for provisioning variation in cloud computing. In Proceedings of CloudCom. Indianapolis, IN, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. C. Reiss, A. Tumanov, G. Ganger, R. Katz, and M. Kozych. Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In Proceedings of ACM Symposium on Cloud Computing (SOCC). 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Rightscale. https://aws.amazon.com/solution-providers/isv/rightscale.Google ScholarGoogle Scholar
  56. J. Schad, J. Dittrich, and J.-A. Quiané-Ruiz. Runtime measurements in the cloud: Observing, analyzing, and reducing variance. Proceedings VLDB Endow., 3(1--2):460--471, Sept. 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. M. Schwarzkopf, A. Konwinski, M. Abd-El-Malek, and J. Wilkes. Omega: flexible, scalable schedulers for large compute clusters. In Proceedings of EuroSys. 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Z. Shen, S. Subbiah, X. Gu, and J. Wilkes. Cloudscale: elastic resource scaling for multi-tenant cloud systems. In Proceedings of ACM Symposium on Cloud Computing (SOCC). Cascais, Portugal, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. T. Simunic and S. Boyd. Managing power consumption in networks on chips. In Proceedings of Design Automation Conference (DAC). Paris, France, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  60. S. Swanson, A. Putnam, M. Mercaldi, K. Michelson, A. Petersen, A. Schwerin, M. Oskin, and S. J. Eggers. Area-performance trade-offs in tiled dataflow architectures. In Proceedings of ACM SIGARCH Computer Architecture News, v.34 n.2, p.314--326, May 2006.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. K. Therdsteerasukdi, G. Byun, J. Cong, F. Chang, and G. Reinman. Utilizing rf-i and intelligent scheduling for better throughput/watt in a mobile gpu memory system. In Transactions on Architecture and Code Optimization (TACO) 8(4). 2012.Google ScholarGoogle Scholar
  62. Torque resource manager. http://www.adaptivecomputing.com/products/open-source/torque/.Google ScholarGoogle Scholar
  63. N. Vasić, D. Novaković, S. Miucin, D. Kostić, and R. Bianchini. Dejavu: accelerating resource allocation in virtualized environments. In Proceedings of the Seventeenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. G. Wang and T. S. E. Ng. The impact of virtualization on network performance of amazon ec2 data center. In Proceeedings of IEEE International Conference on Computer Communications (INFOCOM). San Diego, CA, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  65. Windows azure. http://www.windowsazure.com/.Google ScholarGoogle Scholar
  66. H. Yang, A. Breslow, J. Mars, and L. Tang. Bubble-flux: precise online qos management for increased utilization in warehouse scale computers. In Proceedings of the International Symposium on Computer Architecture (ISCA). 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. Franklin, S. Shenker, and I. Stoica. Spark: Cluster computing with working sets. In Proceedings of the USENIX Sumposium on Networked Systems Design and Implementation (NSDI). San Jose, CA, 2012.Google ScholarGoogle Scholar
  68. S. M. Zahed and B. C. Lee. Ref: Resource elasticity fairness with sharing incentives for multiprocessors. In Proceedings of the Nineteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). Salt Lake City, UT, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. J. Y. Zhang, P. Wu, J. Zhu, H. Hu, and F. Bonomi. Privacy-preserved mobile sensing through hybrid cloud trust framework. In Proceedings of IEEE International Conference on Cloud Computing (CLOUD). 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. X. Zhang, E. Tune, R. Hagmann, R. Jnagal, V. Gokhale, and J. Wilkes. Cpi2: Cpu performance isolation for shared compute clusters. In Proceedings of EuroSys. Prague, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. L. Zhao, R. Iyer, J. Moses, R. Illikkal, S. Makineni, and D. Newell. Exploring large-scale cmp architectures using manysim. In IEEE Micro, vol.27 n.4, July 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 51, Issue 4
        ASPLOS '16
        April 2016
        774 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/2954679
        • Editor:
        • Andy Gill
        Issue’s Table of Contents
        • cover image ACM Conferences
          ASPLOS '16: Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems
          March 2016
          824 pages
          ISBN:9781450340915
          DOI:10.1145/2872362
          • General Chair:
          • Tom Conte,
          • Program Chair:
          • Yuanyuan Zhou

        Copyright © 2016 ACM

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        • Published: 25 March 2016

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