Journal of Computer Science and Technology

, Volume 32, Issue 2, pp 250–257 | Cite as

Experience Availability: Tail-Latency Oriented Availability in Software-Defined Cloud Computing

  • Bin-Lei Cai
  • Rong-Qi Zhang
  • Xiao-Bo ZhouEmail author
  • Lai-Ping Zhao
  • Ke-Qiu Li
Regular Paper


Resource sharing, multi-tenant interference and bursty workloads in cloud computing lead to high tail-latency that severely affects user quality of experience (QoE), where response latency is a critical factor. A lot of research efforts are dedicated to reducing high tail-latency and improving user QoE, such as software-defined cloud computing (SDC). However, the traditional availability analysis of cloud computing captures the pure failure-repair behavior with user QoE ignored. In this paper, we propose a conceptual framework, experience availability, to properly assess the effectiveness of SDC while taking into account both availability and response latency simultaneously. We review the related work on availability models and methods of cloud systems, and discuss open problems for evaluating experience availability in SDC. We also show some of our preliminary results to demonstrate the feasibility of our ideas.


cloud computing software-defined cloud computing (SDC) availability tail-latency 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11390_2017_1719_MOESM1_ESM.pdf (103 kb)
ESM 1 (PDF 103 kb)


  1. [1]
    Dean J, Barrosoluiz A B. The tail at scale. Commun. ACM, 2013, 56(2): 74-80.CrossRefGoogle Scholar
  2. [2]
    Grandl R, Chen Y, Khalid J, Yang S, Anand A, Benson T, Akella A. Harmony: Coordinating network, compute, and storage in software-defined clouds. In Proc. the 4th Annual Symposium on Cloud Computing (poster), Oct. 2013.Google Scholar
  3. [3]
    Buyya R, Calheiros R N, Son J, Dastjerdi A V, Yoon Y. Software-defined cloud computing: Architectural elements and open challenges. In Proc. International Conference on Advances in Computing, Communications and Informatics, Sept. 2014.Google Scholar
  4. [4]
    Jararweh Y, Al-Ayyoub M, Benkhelifa E, Vouk M, Rindos A et al. Software defined cloud: Survey, system and evaluation. Future Generation Computer Systems, 2016, 58: 56-74.CrossRefGoogle Scholar
  5. [5]
    Bao Y G, Wang S. Labeled von Neumann architecture for software-defined cloud. Journal of Computer Science and Technology, 2017, 32(2): 220-224.MathSciNetCrossRefGoogle Scholar
  6. [6]
    Amazon EC2 service level agreement. 2013., Feb. 2017.
  7. [7]
    App engine service level agreement (SLA)., Feb. 2017.
  8. [8]
    Microsoft. Service level agreements. Feb. 2017.
  9. [9]
    Neamtiu I, Dumitraş T. Cloud software upgrades: Challenges and opportunities. In Proc. International Workshop on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems, Sept. 2011.Google Scholar
  10. [10]
    Lu Q, Xu X, Zhu L, Bass L, Li Z, Sakr S, Bannerman P L, Liu A. Incorporating uncertainty into in-cloud application deployment decisions for availability. In Proc. IEEE International Conference on Cloud Computing, Jun. 2013, pp.454-461.Google Scholar
  11. [11]
    Meyer J F. On evaluating the performability of degradable computing systems. IEEE Transactions on computers, 1980, 100(8): 720-731.CrossRefzbMATHGoogle Scholar
  12. [12]
    Smith R, Trivedi K S, Ramesh A. Performability analysis: Measures, an algorithm, and a case study. IEEE Transactions on Computers, 1988, 37(4): 406-417.CrossRefGoogle Scholar
  13. [13]
    Amari S V, Xing L, Shrestha A, Akers J, Trivedi K S. Performability analysis of multistate computing systems using multivalued decision diagrams. IEEE Transactions on Computers, 2010, 59(10): 1419-1433.MathSciNetCrossRefGoogle Scholar
  14. [14]
    Ghosh R, Trivedi K S, Naik V K, Kim D S. End-to-end performability analysis for Infrastructure-as-a-Service cloud: An interacting stochastic models approach. In Proc. the 16th IEEE Pacific Rim International Symposium on Dependable Computing, Dec. 2010, pp.125-132.Google Scholar
  15. [15]
    Entezari-Maleki R, Trivedi K S, Movaghar A. Performability evaluation of grid environments using stochastic reward nets. IEEE Transactions on Dependable and Secure Computing, 2015, 12(2): 204-216.CrossRefGoogle Scholar
  16. [16]
    Wei B, Lin C, Kong X. Dependability modeling and analysis for the virtual data center of cloud computing. In Proc. High Performance Computing and Communications, Sept. 2011, pp.784-789.Google Scholar
  17. [17]
    Ahmed W, Hasan O, Tahar S. Formalization of reliability block diagrams in higher-order logic. Journal of Applied Logic, 2016, 18: 19-41.MathSciNetCrossRefzbMATHGoogle Scholar
  18. [18]
    Wang Y, Luo C, Liu Z. Reliability analysis of multi-node SDDC using fault tree. In Proc. International Industrial Informatics and Computer Engineering Conference, Jan. 2015, pp.1155-1158.Google Scholar
  19. [19]
    Trivedi K S. Probability and Statistics with Reliability, Queuing and Computer Science Applications. John Wiley & Sons, 2008.Google Scholar
  20. [20]
    Ivanchenko O, Kharchenko V. Semimarkov availability models for an Infrastructure as a Service cloud with multiple pools. In Proc. International Conference on ICT in Education, Research, and Industrial Applications, Nov. 2016, pp.349-360.Google Scholar
  21. [21]
    Longo F, Ghosh R, Naik V K, Trivedi K S. A scalable availability model for Infrastructure-as-a-Service cloud. In Proc. the 41st IEEE/IFIP International Conference on Dependable Systems & Networks, Jun. 2011, pp.335-346.Google Scholar
  22. [22]
    Ghosh R, Longo F, Frattini F, Russo S, Trivedi K S. Scalable analytics for IaaS cloud availability. IEEE Transactions on Cloud Computing, 2014, 2(1): 57-70.CrossRefGoogle Scholar
  23. [23]
    Wei B, Lin C, Kong X. Dependability modeling and analysis for the virtual clusters. In Proc. International Conference on Computer Science and Network Technology, Volume 4, Dec. 2011, pp.2316-2320.Google Scholar
  24. [24]
    Dantas J, Matos R, Araujo J, Maciel P. Models for dependability analysis of cloud computing architectures for eucalyptus platform. International Transactions on Systems Science and Applications, 2012, 8: 13-25.Google Scholar
  25. [25]
    Dantas J, Matos R, Araujo J, Maciel P. Eucalyptus-based private clouds: Availability modeling and comparison to the cost of a public cloud. Computing, 2015, 97(11): 1121-1140.MathSciNetCrossRefzbMATHGoogle Scholar
  26. [26]
    Qiu X, Dai Y, Xiang Y, Xing L. A hierarchical correlation model for evaluating reliability, performance, and power consumption of a cloud service. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46(3): 401-412.CrossRefGoogle Scholar
  27. [27]
    Cooper B F, Silberstein A, Tam E, Ramakrishnan R, Sears R. Benchmarking cloud serving systems with YCSB. In Proc. the 1st ACM Symposium on Cloud Computing, Jun. 2010, pp.143-154.Google Scholar
  28. [28]
    Leitner P, Cito J. Patterns in the chaos — A study of performance variation and predictability in public IaaS clouds. ACM Transactions on Internet Technology, 2014, 16(3): 1-15.CrossRefGoogle Scholar
  29. [29]
    Iosup A, Prodan R, Epema D. IaaS cloud benchmarking: Approaches, challenges, and experience. In Cloud Computing for Data-Intensive Applications, Li X, Qiu J (eds.), Springer, 2014, pp.83-104.Google Scholar
  30. [30]
    Varghese B, Subba L T, Thai L T, Barker A D. DocLite: A Docker-based lightweight cloud benchmarking tool. In Proc. the 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016), May. 2016, pp.213-222.Google Scholar
  31. [31]
    Fujita H, Matsuno Y, Hanawa T, Sato M, Kato S, Ishikawa Y. DS-Bench Toolset: Tools for dependability bench-marking with simulation and assurance. In Proc. IEEE/IFIP International Conference on Dependable Systems and Networks, Jun. 2012.Google Scholar
  32. [32]
    Sangroya A, Serrano D, Bouchenak S. Benchmarking dependability of MapReduce systems. In Proc. the 31st IEEE Symposium on Reliable Distributed Systems, Feb. 2012, pp.21-30.Google Scholar
  33. [33]
    Sangroya A, Bouchenak S, Serrano D. Experience with benchmarking dependability and performance of MapReduce systems. Perform. Eval., 2016, 101: 1-19.CrossRefGoogle Scholar
  34. [34]
    Little J D C. A proof for the queuing formula: L = λw. Operations Research, 1961, 9(3): 383-387.MathSciNetCrossRefzbMATHGoogle Scholar
  35. [35]
    Trivedi K S, Sahner R. Sharpe at the age of twenty two. ACM SIGMETRICS Performance Evaluation Review, 2009, 36(4): 52-57.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Bin-Lei Cai
    • 1
  • Rong-Qi Zhang
    • 1
  • Xiao-Bo Zhou
    • 1
    Email author
  • Lai-Ping Zhao
    • 2
  • Ke-Qiu Li
    • 1
  1. 1.Tianjin Key Laboratory of Advanced Networking, School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.School of Computer SoftwareTianjin UniversityTianjinChina

Personalised recommendations