Toward Autonomic Data-Oriented Scalability in Cloud Computing Environments

The applications deployed in modern data centers are highly diverse in terms of architecture and performance needs. It is a challenge to provide consistent services to all applications in a shared environment. This thesis proposes a generic analytical engine that can optimize the use of cloud-based resources according to service needs in an autonomic manner. The proposed system is capable of ingesting large amounts of data generated by various monitoring services within data centers. Then, by transforming that data into actionable knowledge, the system can make the necessary decisions to maintain a desired level of quality of service. The contributions of this work are the following: First, we define a scalable architecture to collect the metrics and store the data. Second, we design and implement a process for building prediction models that characterize application performance using data mining and statistical techniques. Lastly, we evaluate the accuracy of the prediction models.