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Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (6): 1167-1184.doi: 10.1007/s11390-019-1968-y
Special Issue: Data Management and Data Mining
• Data Management and Data Mining • Next Articles
Rui Ren1,2, Member, CCF, IEEE, Jiechao Cheng3, Xi-Wen He1, Lei Wang1, Member, CCF, Jian-Feng Zhan1,*, Member, CCF, ACM, IEEE, Wan-Ling Gao1, Member, CCF, ACM, IEEE, Chun-Jie Luo1,2, Member, CCF
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In Proc. the 24th International Conference on Very Large Data Bases, August 1998, pp.392-403. [14] Ming Z, Luo C, Gao W, Han R, Yang Q, Wang L, Zhan J. BDGS:A scalable Big Data generator suite in Big Data benchmarking. In Proc. the 2013 Workshop Series on Big Data Benchmarking, July 2014, pp.138-154. [15] Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D B, Amde M, Owen S, Xin D, Xin R, Franklin M J, Zadeh R, Zaharia M, Talwalkar A. MLlib:Machine learning in Apache Spark. J. Mach. Learn. Res., 2016, 17:Article No. 34. [16] Wang C, Talwar V, Schwan K, Ranganathan P. Online detection of utility cloud anomalies using metric distributions. In Proc. the IEEE/IFIP Network Operations and Management Symposium, April 2010, pp.96-103. [17] Ousterhout K, Rasti R, Ratnasamy S, Shenker S, Chun B. Making sense of performance in data analytics frameworks. In Proc. the 12th USENIX Symposium on Networked Systems Design and Implementation, May 2015, pp.293-307. [18] Jayathilaka H, Krintz C, Wolski R. Detecting performance anomalies in cloud platform applications. IEEE Transactions on Cloud Computing. doi:10.1109/TCC.2018.2808289. [19] Ramaswamy S, Rastogi R, Shim K. Efficient algorithms for mining outliers from large data sets. In Proc. the 2000 ACM SIGMOD International Conference on Management of Data, May 2000, pp.427-438. [20] Breunig M M, Kriegel H P, Ng R T, Sander J. LOF:Identifying density-based local outliers. In Proc. ACM SIGMOD International Conference on Management of Data, May 2000, pp.93-104. [21] Yu D, Sheikholeslami G, Zhang A. FindOut:Finding outliers in very large datasets. Knowledge and Information Systems, 2002, 4(4):387-412. [22] Yu L, Lan Z. A scalable, non-parametric method for detecting performance anomaly in large scale computing. IEEE Transactions on Parallel and Distributed Systems, 2016, 27(7):1902-1914. [23] Tan J, Pan X, Marinelli E, Kavulya S, Gandhi R, Narasimhan P. Kahuna:Problem diagnosis for MapReducebased cloud computing environments. In Proc. the IEEE/IFIP Network Operations and Management Symposium, April 2010, pp.112-119. [24] Pan X, Tan J, Kavulya S, Gandhi R, Narasimhan P. Ganesha:BlackBox diagnosis of MapReduce systems. SIGMETRICS Performance Evaluation Review, 2009, 37(3):8-13. [25] Gupta C, Sinha R, Zhang Y. Eagle:User profile-based anomaly detection for securing Hadoop clusters. In Proc. the 2015 IEEE International Conference on Big Data, October 2015, pp.1336-1343. [26] Kasick M P, Tan J, Gandhi R, Narasimhan P. Black-box problem diagnosis in parallel file systems. In Proc. the 8th USENIX Conference on File and Storage Technologies, February 2010, pp.43-56. [27] Fu X, Ren R, McKeez S A, Zhan J, Sun N. Digging deeper into cluster system logs for failure prediction and root cause diagnosis. In Proc. IEEE International Conference on Cluster Computing, September 2014, pp.103-112. [28] Khan L, Awad M, Thuraisingham B. A new intrusion detection system using support vector machines and hierarchical clustering. The VLDB Journal, 2007, 16(4):507-521. [29] Lee S, Shin K G. Probabilistic diagnosis of multiprocessor systems. ACM Computing Surveys, 1994, 26(1):121-139. [30] Das K, Schneider J. Detecting anomalous records in categorical datasets. In Proc. the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2007, pp.220-229. [31] Mi H, Wang H, Zhou Y, Lyu M R, Cai H. Toward finegrained, unsupervised, scalable performance diagnosis for production cloud computing systems. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(6):1245-1255. [32] Jia T, Chen P, Yang L, Li Y, Meng F, Xu J. An approach for anomaly diagnosis based on hybrid graph model with logs for distributed services. In Proc. the 2017 IEEE International Conference on Web Services, June 2017, pp.25-32. [33] Ren R, Tian S, Wang L. Online anomaly detection framework for Spark systems via stage-task behavior modeling. In Proc. the 15th ACM International Conference on Computing Frontiers, May 2018, pp.256-259. |
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