Due to the diversity in the applications that run in large distributed environments, many different application frameworks have been developed, such as MapReduce for data-intensive batch jobs and Spark for interactive data analytics. After initial deployment, a framework starts executing a large set of jobs that are submitted over time. When multiple such frameworks with time-varying resource demands are consolidated in a large distributed environment, static allocation of resources on a per-framework basis leads to low system utilization and to resource fragmentation. The goal of my PhD research is to improve the system utilization and framework performances in such consolidated environments by using dynamic resource allocation for efficient resource sharing among frameworks. My contribution towards this goal is a design and an implementation of a scalable resource manager that dynamically balances resources across set of multiple diverse frameworks in a large distributed environment based on resource requirements, system utilization or performance levels in the deployed frameworks.
|Title of host publication||16th IEEE/ACM Int'l Symp. on Cluster, Cloud and Grid Computing: Doctoral Symposium|
|Number of pages||4|
|Publication status||Published - 2016|