Engineering Commons^™

Predicting performance of an application running on high performance computing (HPC) platforms in a cloud environment is increasingly becoming important because of its influence on development time and resource management. However, predicting the performance with respect to parallel processes is complex for iterative, multi-stage applications. This research proposes a performance approximation approach FiM to model the computing performance of iterative, multi-stage applications running on a master-compute framework. FiM consists of two key components that are coupled with each other: 1) Stochastic Markov Model to capture non-deterministic runtime that often depends on parallel resources, e.g., number of processes. 2) Machine Learning …

Hypervisor-based virtualization technology has been successfully used to deploy high-performance and scalable infrastructure for Hadoop, and now Spark applications. Container-based virtualization techniques are becoming an important option, which is increasingly used due to their lightweight operation and better scaling when compared to Virtual Machines (VM). With containerization techniques such as Docker becoming mature and promising better performance, we can use Docker to speed-up big data applications. However, as applications have different behaviors and resource requirements, before replacing traditional hypervisor-based virtual machines with Docker, it is important to analyze and compare performance of applications running in the cloud with VMs and …