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Toward Proactive Learning of Multi-layerd Cloud Service Based Application

  • Ameni MeskiniEmail author
  • Yehia Taher
  • Amal El Gammal
  • Béatrice Finance
  • Yahya Slimani
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 740)

Abstract

Cloud computing is becoming a popular platform to deliver service-based applications (SBAs) based on service oriented architecture (SOA) principles. Monitoring the performance and functionality in all the layers which affects the final step of adaptations of SBAs deployed on multiple Cloud providers and adapting them to variations/events produced by several layers (infrastructure, platform, application, service, etc.) are challenges for the research community, and the major challenge is handling the impact of the adaptation operations. A crucial dimension in industrial practice is the non-functional service aspects, which are related to Quality-of-Service (QoS) aspects. Service Level Agreements (SLAs) define quantitative QoS objectives and is a part of a contract between the service provider and the service consumer. Although significant work exists on how SLA may be specified, monitored and enforced, few efforts have considered the problem of SLA monitoring in the context of Cloud Service-Based Application (CSBA), which caters for tailoring of services using a mixture of Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) solutions. With a preventive focus, the main contribution of this paper is a novel learning and prediction approach for SLA violations, which generates models that are capable of proactively predicting upcoming SLAs violations, and suggesting recovery actions to react to such SLA violations before their occurrence. A prototype has been developed as a Proof-Of-Concept (POC) to ascertain the feasibility and applicability of the proposed approach.

Keywords

Cloud Service Based Application SLA violations prevention Cloud environments Decision tree 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ameni Meskini
    • 1
    Email author
  • Yehia Taher
    • 2
  • Amal El Gammal
    • 3
  • Béatrice Finance
    • 2
  • Yahya Slimani
    • 1
  1. 1.INSAT, LISI Research LaboratoryUniversity of CarthageTunisTunisia
  2. 2.Laboratoire PRiSMUniversite de Versailles/Saint-Quentin-en-YvelinesVersaillesFrance
  3. 3.Faculty of Computers and InformationCairo UniversityCairoEgypt

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