Detecting performance anomalies in scientific workflows using hierarchical temporal memory

Abstract

Technological advances and the emergence of the Internet of Things have lead to the collection of vast amounts of scientific data from increasingly powerful scientific instruments and a growing number of distributed sensors. This has not only exacerbated the significance of the analyses performed by scientific applications but has also increased their complexity and scale. Hence, emerging extreme-scale scientific workflows are becoming widespread and so is the need to efficiently automate their deployment on a variety of platforms such as high performance computers, dedicated clusters, and cloud environments. Performance anomalies can considerably affect the execution of these applications. ..