Robust resource allocation of DAGs in a heterogeneous multicore system

In this study, we consider an environment composed of a heterogeneous cluster of multicore-based machines used to analyze satellite images. The workload involves large data sets, and is typically subject to deadline constraints. Multiple applications, each represented by a directed acyclic graph (DAG), are allocated to a dedicated heterogeneous distributed computing system. Each vertex in the DAG represents a task that needs to be executed and task execution times vary substantially across machines. The goal of this research is to assign applications to multicore-based parallel system in such a way that all applications complete before a common deadline, and their completion times are robust against uncertainties in execution times. We define a measure that quantifies robustness in this environment. We design, compare, and evaluate two resource allocation heuristics that attempt to maximize robustness.

• Type: Journal
• Year: 2010
• Where: IPPS
• Authors: Luis D. Briceo, Jay Smith, Howard Jay Siegel, Anthony A. Maciejewski, Paul Maxwell, Russ Wakefield, Abdulla Al-Qawasmeh, Ron Chi-Lung Chiang, Jiayin Li
• Keywords: Directed Acyclic Graph, Distributed And Parallel Computing, Execution Times, Heterogeneous Distributed Computing, IPPS 2010