Quincy

Citations Over TimeTop 1% of 2009 papers

Abstract

This paper addresses the problem of scheduling concurrent jobs on clusters where application data is stored on the computing nodes. This setting, in which scheduling computations close to their data is crucial for performance, is increasingly common and arises in systems such as MapReduce, Hadoop, and Dryad as well as many grid-computing environments. We argue that data-intensive computation benefits from a fine-grain resource sharing model that differs from the coarser semi-static resource allocations implemented by most existing cluster computing architectures. The problem of scheduling with locality and fairness constraints has not previously been extensively studied under this resource-sharing model.

Related Papers

• → Huddersfield University Campus Grid: QGG of OSCAR Clusters(2010)8 cited
• → On Integration of GUI and Portal of Cluster and Grid Computing Platforms for Parallel Bioinformatics(2006)3 cited
• → 13. Distributed Computing – GRID Computing(2005)1 cited
• Implementation Method for Parallel Computing in Multi-Cluster Grid(2007)
• The Analysis of Distributed Computing Methods Based on Grid Nodes(2003)