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Event Pattern Discovery in Multi-Cloud Service-Based Applications

Event Pattern Discovery in Multi-Cloud Service-Based Applications

Chrysostomos Zeginis, Kyriakos Kritikos, Dimitris Plexousakis
Copyright: © 2015 |Volume: 5 |Issue: 4 |Pages: 26
ISSN: 1947-3052|EISSN: 1947-3060|EISBN13: 9781466678545|DOI: 10.4018/IJSSOE.2015100104
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MLA

Zeginis, Chrysostomos, et al. "Event Pattern Discovery in Multi-Cloud Service-Based Applications." IJSSOE vol.5, no.4 2015: pp.78-103. http://doi.org/10.4018/IJSSOE.2015100104

APA

Zeginis, C., Kritikos, K., & Plexousakis, D. (2015). Event Pattern Discovery in Multi-Cloud Service-Based Applications. International Journal of Systems and Service-Oriented Engineering (IJSSOE), 5(4), 78-103. http://doi.org/10.4018/IJSSOE.2015100104

Chicago

Zeginis, Chrysostomos, Kyriakos Kritikos, and Dimitris Plexousakis. "Event Pattern Discovery in Multi-Cloud Service-Based Applications," International Journal of Systems and Service-Oriented Engineering (IJSSOE) 5, no.4: 78-103. http://doi.org/10.4018/IJSSOE.2015100104

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Abstract

The adoption of Cloud computing in the Service Oriented Architecture (SOA) world is continuously increasing. However, as developers try to optimize their application deployment cost and performance, they may also deploy application parts redundantly on different VMs. In such heterogeneous and distributed environments, it is important to have a clear view of the system's state and its components' interrelationships. This paper aims at proposing a novel monitoring and adaptation framework for Service-based Applications (SBAs) deployed on multiple Clouds. The main functionality of this framework is the discovery of critical event patterns within monitoring event streams, leading to specific Service Level Objective (SLO) violations. Furthermore, two main meta-models are proposed for describing the SBA's components and their dependencies, and the supported adaptation actions in a specific context respectively. The proposed approach is empirically evaluated based on a real-world traffic management application.

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