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Emerging service orchestration discovery and monitoring

Citation

Chu, VW and Wong, RK and Fong, S and Chi, C, Emerging service orchestration discovery and monitoring, IEEE Transactions on Services Computing, 10, (6) pp. 889-901. ISSN 1939-1374 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 IEEE

DOI: doi:10.1109/TSC.2015.2511000

Abstract

Due 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.

Item Details

Item Type:Refereed Article
Keywords:data sparsity problem, SIR model, topic model, web services orchestration
Research Division:Information and Computing Sciences
Research Group:Information Systems
Research Field:Information Systems Management
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Chi, C (Dr Chi-Hung Chi)
ID Code:117885
Year Published:2015
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2017-06-29
Last Modified:2018-07-11
Downloads:0

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