Presentation

Semantically Rich, Policy Based Framework to Automate Lifecycle of Cloud Based Services

Karuna Pande Joshi

November 19, 2012

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Tags: cloud computing, cloud service, service automation, service negotiation, service quality

Managing virtualized services efficiently over the cloud is an open challenge. Traditional models of software development are very time consuming and labor intensive for the cloud computing domain, where software (and other) services are acquired on demand. Virtualized services are often composed of pre-existing components that are assembled on an as-needed basis. We have developed a new framework to automate the acquisition, composition and consumption/ monitoring of virtualized services delivered on the cloud. We have divided the service lifecycle into five phases of requirements, discovery, negotiation, composition, and consumption and have developed ontologies to represent the concepts and relationships for each phase. These are represented in Semantic Web languages. We have developed a protocol to automate the negotiation process when acquiring virtualized services. This protocol allows complex relaxation of constraints being negotiated based on user defined policies. We have also developed detailed ontologies to define service level agreements for cloud services. To illustrate and validate how this framework can automate the acquisition of cloud services, we have built two applications from real world scenarios. The Smart cloud services application enables users to determine and procure the cloud storage application that matches most of their constraints and policies. We have also built a VCL broker application that allows users to automatically reserve the VCL Image that will best meet their requirements. We have developed a framework to measure and semi-automatically track quality of a virtualized service delivery system. The framework provides a mechanism to relate hard metrics typically measured at the backstage of the delivery process to quality related hard and soft metrics tracked at the front stage where the consumer interacts with the service. While this framework is general enough to be applied to any type of IT service, in this dissertation we have primarily concentratated on the Helpdesk service and include the performance rules we have created by mining Helpdesk data.

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