Optimizing end-to-end performance of data-intensive computing pipelines in heterogeneous network environments

DOI10.1016/j.jpdc.2010.08.003zbMath1219.68076OpenAlexW2011416912MaRDI QIDQ635308

Qishi Wu, Yi Gu

Publication date: 19 August 2011

Published in: Journal of Parallel and Distributed Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jpdc.2010.08.003

zbMATH Keywords

performance optimizationpipelineend-to-end delaydata-intensive computingframe rate

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Dynamic programming (90C39) Performance evaluation, queueing, and scheduling in the context of computer systems (68M20)

Cites Work

• Unnamed Item
• Unnamed Item
• Mapping pipeline skeletons onto heterogeneous platforms
• Genetic algorithms for task scheduling problem
• A scheduling framework for large-scale, parallel, and topology-aware applications
• The directed subgraph homeomorphism problem
• A comparison of clustering heuristics for scheduling directed acyclic graphs on multiprocessors
• Configuring sessions in programmable networks.
• Performance of local search heuristics on scheduling a class of pipelined multiprocessor tasks