Tasks are organized in queues based on task data size and location. We implement our technique in MATRIX, a distributed task scheduler for many-task computing.

Sep 12, 2014 · Abstract—Load balancing techniques (e.g. work stealing) are important to obtain the best performance for distributed task scheduling systems ...

Load balancing techniques (e.g. work stealing) are important to obtain the best performance for distributed task scheduling systems that have multiple ...

An analytical suboptimal upper bound is devised of the proposed data‐aware work‐stealing technique to optimize both load balancing and data locality and ...

Tasks are organized in queues based on task data size and location. We implement our technique in MATRIX, a distributed task scheduler for many-task computing.

Abstract: Load balancing techniques (e.g. work stealing) are important to obtain the best performance for distributed task scheduling systems that have ...

Abstract: Load balancing techniques (e.g. work stealing) are important to obtain the best performance for distributed task scheduling systems that have ...

“Optimizing Load Balancing and Data-. Locality with Data-aware Scheduling”, IEEE International Conference on Big Data 2014. 11.! M. H. Willebeek-LeMair ...

"Optimizing load balancing and data-locality with data-aware scheduling ", IEEE International Conference on Big Data 2014. Crossref · Google Scholar. [15].

Jul 11, 2024 · This paper investigates a data-locality-aware task assignment and scheduling problem aimed at minimizing job completion times for distributed job executions.