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%.
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References
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
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
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
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
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
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
Shroff G M. A parallel algorithm for the eigenvalues and eigenvectors of a general complex matrix. Numerische Mathematik, 1990, 58(1):779–805
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
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
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
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
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
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
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
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
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
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
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
Chazan D, Miranker W. Chaotic relaxation. Linear Algebra and Its Applications, 1969, 2(2): 199–222
Baudet G M. Asynchronous iterative methods for multiprocessors. Journal of the ACM, 1978, 25(2): 226–244
Bertsekas D P. Distributed asynchronous computation of fixed points. Mathematical Programming, 1983, 27(1): 107–120
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
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
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
Stanford. Stanford Large Network Dataset Collection. http://snap.stanford.edu/data/, 2013
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
Haewoon K. What is Twitter, a Social Network or a New Media?. http://an.kaist.ac.kr/traces/www2010.html, 2013
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
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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.
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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
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DOI: https://doi.org/10.1007/s11704-014-3472-4