Availability Modeling and Evaluation on High Performance Cluster Computing Systems

Cluster computing has been attracting more and more attention from both the industrial and the academic world for its enormous computing power and scalability. Beowulf type cluster, for example, is a typical High Performance Computing (HPC) cluster system. Availability, as a key attribute of the system, needs to be considered at the system design stage and monitored at mission time. Moreover, system monitoring is a must to help identify the defects and ensure the system’s availability requirement. In this paper, novel solutions which provide availability modeling, model evaluation, and data analysis as a single framework have been investigated. Three key components in the investigation are availability modeling, model evaluation, and data analysis. The general availability concepts and modeling techniques are briefly reviewed. The system’s availability model is divided into submodels based upon their functionalities. Furthermore, an object oriented Markov model specification to facilitate availability modeling and runtime configuration has been developed. Numerical solutions for Markov models are examined, especially on the uniformization method. The paper also presents a monitoring and data analysis framework, which is responsible for failure analysis and availability reconfiguration. ACM Classification: D.2.11, D.2.12, D.2.13