Toward Proactive Learning of Multi-layerd Cloud Service Based Application

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.