PhD Defense: Karuna Joshi
Cloud Computing
Monday, November 19, 2012 · 10 - 11 AM
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.
Thesis Committee:
- Dr. Yelena Yesha (chair)
- Dr. Tim Finin
- Dr. Milton Halem
- Dr. Yaacov Yesha
- Dr. Aryya Gangopadhyay