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Emerging service orchestration discovery and monitoring
Chu,Victor W.1; Wong,Raymond K.1; Fong,Simon2; Chi,Chi Hung3
2017-11-01
Source PublicationIEEE Transactions on Services Computing
Volume10Issue:6Pages:889-901
AbstractDue to the popularity of web services on the Internet, it is important to have a clear view of their utilization behaviors. Despite asynchronous service invocations and distributed executions can provide better user experience, our views are blurred by out-of-order and fragmented service logs. Researchers have been trying various methods to reveal emerging service orchestration patterns, but nearly all of them have taken deterministic approaches. Hence, they do not natively cater for incomplete data and noises. In this paper, we propose to address these problems by using topic models aiming to reveal service orchestration patterns from sparse service logs. Probabilistic approaches do not only tolerate data defects, but their associated approximation methods also overcome combinatorial explosion. We first investigate the implications of sparsity on topic models. Secondly, we propose an extended time-series form of susceptible-infectious-recovered model to monitor the dynamics of emerging service orchestrations. We quantify their emerging-potential by estimated effective-reproduction-number, which is obtained incrementally by Bayesian parameter estimations. Guided by our proposed emerging-potential measure, one can profile and categorize emerging service orchestration patterns, and generate automated alerts on upcoming consumption peaks. In practice, our model enables service providers to better allocate their resources to meet demands dynamically. While our findings affirm that biterm topic model can be applied to service logs with short and sparse log entries, the effectiveness of our proposed monitoring solutions is also shown by experiments.
KeywordData sparsity problem SIR model Topic model Web services orchestration
DOI10.1109/TSC.2015.2511000
URLView the original
Language英语
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.School of Computer Science and Engineering,University of New South Wales,Australia
2.Department of Computer and Information Science,University of Macau,Taipa,Macao
3.Intelligent Sensing and Systems Laboratory,CSIRO,Australia
Recommended Citation
GB/T 7714
Chu,Victor W.,Wong,Raymond K.,Fong,Simon,et al. Emerging service orchestration discovery and monitoring[J]. IEEE Transactions on Services Computing,2017,10(6):889-901.
APA Chu,Victor W.,Wong,Raymond K.,Fong,Simon,&Chi,Chi Hung.(2017).Emerging service orchestration discovery and monitoring.IEEE Transactions on Services Computing,10(6),889-901.
MLA Chu,Victor W.,et al."Emerging service orchestration discovery and monitoring".IEEE Transactions on Services Computing 10.6(2017):889-901.
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