Skip to main content
SpringerLink
Log in

An adaptive switching scheme for iterative computing in the cloud

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Delta-based accumulative iterative computation (DAIC) model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers fromthe serious network jitter and load jitter caused bymultitenancy in the cloud. In this paper, we develop a system, namely HybIter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Horowitz D, Kamvar S D. The anatomy of a large-scale social search engine. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 431–440

    Chapter  Google Scholar 

  2. Song H, Cho T, Dave V, Zhang Y, Qiu L. Scalable proximity estimation and link prediction in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference. 2009, 322–335

    Chapter  Google Scholar 

  3. Gao B, Liu T, Wei W, Wang T, Li H. Semi-supervised ranking on very large graphs with rich metadata. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 96–104

    Google Scholar 

  4. Baluja S, Seth R, Sivakumar D, Jing Y, Jay Y, Kumar S, Deepak R, Aly M. Video suggestion and discovery for youtube: taking random walks through the view graph. In: Proceedings of the 17th International Conference on World Wide Web. 2008, 895–904

    Chapter  Google Scholar 

  5. Zhou T, Kuscsik Z, Liu J, Medo M, Wakeling J R, Zhang Y. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 2010, 107(10): 4511–4515

    Article  Google Scholar 

  6. David L N, Jon K. The link prediction problem for social networks. In: Proceedings of the 12th International Conference on Information and Knowledge Management. 2003, 556–559

    Google Scholar 

  7. Shroff G M. A parallel algorithm for the eigenvalues and eigenvectors of a general complex matrix. Numerische Mathematik, 1990, 58(1):779–805

    Article  MathSciNet  Google Scholar 

  8. Ekanayake J, Li H, Zhang B, Gunarathne T, Bae S H, Qiu J, Fox G. Twister: a runtime for iterative MapReduce. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. 2010, 810–818

    Chapter  Google Scholar 

  9. Bu Y, Howe B, Balazinska M, Ernst M D. HaLoop: efficient iterative data processing on large clusters. Proceedings of the VLDB Endowment, 2010, 3(1): 285–296

    Article  Google Scholar 

  10. Zaharia M, Chowdhury M, Franklin M J, Shenker S, Stoica I. Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing. 2010, 1–10

    Google Scholar 

  11. Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C, Hellerstein J M. Graphlab: a new framework for parallel machine learning. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence. 2010, 1–10

    Google Scholar 

  12. Zhang Y, Gao Q, Gao L, Wang C. Maiter: an asynchronous graph processing framework for delta-based accumulative iterative computation. IEEE Transactions on Parallel and Distributed System, 2013, http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.235

    Google Scholar 

  13. Zou T, Wang G, Salles M V, Bindel D, Demers A, Gehrke J, White W. Making time-stepped applications tick in the cloud. In: Proceedings of the 2nd ACM Symposium on Cloud Computing. 2011, 1–20

    Chapter  Google Scholar 

  14. Zhang Y, Gao Q, Gao L, Wang C. Imapreduce: a distributed computing framework for iterative computation. In: Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum. 2011, 1112–1121

    Google Scholar 

  15. Power R, Li J. Piccolo: building fast, distributed programs with partitioned tables. In: Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation. 2010, 1–14

    Google Scholar 

  16. Logothetis D, Olston C, Reed B, Webb K C, Yocum K. Stateful bulk processing for incremental analytics. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010, 51–62

    Chapter  Google Scholar 

  17. Murray D G, Schwarzkopf M, Smowton C, Smith S, Madhavapeddy A, Hand S. CIEL: a universal execution engine for distributed data-flow computing. In: Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation. 2011, 1–9

    Google Scholar 

  18. Malewicz G, Austern M H, Bik A, Dehnert J C, Horn I, Leiser N, Czajkowski G. Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 2010, 135–146

    Chapter  Google Scholar 

  19. Chazan D, Miranker W. Chaotic relaxation. Linear Algebra and Its Applications, 1969, 2(2): 199–222

    Article  MATH  MathSciNet  Google Scholar 

  20. Baudet G M. Asynchronous iterative methods for multiprocessors. Journal of the ACM, 1978, 25(2): 226–244

    Article  MATH  MathSciNet  Google Scholar 

  21. Bertsekas D P. Distributed asynchronous computation of fixed points. Mathematical Programming, 1983, 27(1): 107–120

    Article  MATH  MathSciNet  Google Scholar 

  22. Kambatla K, Rapolu N, Jagannathan S, Grama A. Asynchronous algorithms in mapreduce. In: Proceedings of the 2010 IEEE International Conference on Cluster Computing. 2010, 245–254

    Chapter  Google Scholar 

  23. Low Y, Bickson D, Gonzalez J, Guestrin C, Kyrola A, Hellerstein J M. Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment, 2012, 5(8): 716–727

    Article  Google Scholar 

  24. Zhang Y, Gao Q, Gao L, Wang C. Accelerate large-scale iterative computation through asynchronous accumulative updates. In: Proceedings of the 3rdWorkshop on Scientific Cloud Computing Date. 2012, 13–22

    Google Scholar 

  25. Stanford. Stanford Large Network Dataset Collection. http://snap.stanford.edu/data/, 2013

    Google Scholar 

  26. Takács G, Pilászy I, Németh B, Tikk D. Scalable collaborative filtering approaches for large recommender systems. The Journal of Machine Learning Research, 2009, 10: 623–656

    Google Scholar 

  27. Haewoon K. What is Twitter, a Social Network or a New Media?. http://an.kaist.ac.kr/traces/www2010.html, 2013

    Google Scholar 

  28. Zhang Y, Gao Q, Gao L, Wang C. PrIter: a distributed framework for prioritized iterative computations. In: Proceedings of the 2nd ACM Symposium on Cloud Computing. 2011, 1–14

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yu Zhang, Xiaofei Liao, Hai Jin, Li Lin & Feng Lu

Authors
  1. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofei Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feng Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaofei Liao.

Additional information

Yu Zhang is now a PhD candidate in computer science and technology of Huazhong University of Science and Technology (HUST), Wuhan, China. His research interests include big data processing, cloud computing and distributed systems. His current topic mainly focuses on application-driven big data processing and optimizations.

Xiaofei Liao received his PhD degree in computer science and engineering from Huazhong University of Science and Technology (HUST), China in 2005. He is now a professor in school of Computer Science and Engineering at HUST. His research interests are in the areas of system virtualization, system software, and cloud computing.

Hai Jin received the BS, MA, and PhD degrees in computer engineering from the Huazhong University of Science and Technology (HUST) in 1988, 1991, and 1994, respectively. Currently, he is a professor of Computer Science and Engineering at HUST in China. He is currently the dean of School of Computer Science and Technology at HUST. His research interests include virtualization technology for computing system, cluster computing and grid computing, peer-to-peer computing, network storage, network security, and high-assurance computing. He is the member of grid forum steering group (GFSG). He is a senior member of the IEEE and a member of the ACM.

Li Lin is a PhD candidate in computer science and engineering from the Huazhong University of Science and Technology (HUST). He received the BS, MA degree in computer science and engineering from Sichuan University. He is also a lecturer in Fujian Normal University. His research interests are in the area of cloud gaming, mobile computing, and cloud-mobile computing fusion.

Feng Lu received her PhD degree in computer science and engineering from Huazhong University of Science and Technology (HUST), China. She is now an associate professor in the school of Computer Science and Engineering at HUST. Her main research interests include mobile internet, parallel computing, cloud service and social network.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Liao, X., Jin, H. et al. An adaptive switching scheme for iterative computing in the cloud. Front. Comput. Sci. 8, 872–884 (2014). https://doi.org/10.1007/s11704-014-3472-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-014-3472-4

Keywords

Search

Navigation