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The quest for scalable support of data-intensive workloads in distributed systems
2009
Proceedings of the 18th ACM international symposium on High performance distributed computing - HPDC '09
Data-intensive applications involving the analysis of large datasets often require large amounts of compute and storage resources, for which data locality can be crucial to high throughput and performance. We propose a "data diffusion" approach that acquires compute and storage resources dynamically, replicates data in response to demand, and schedules computations close to data. As demand increases, more resources are acquired, thus allowing faster response to subsequent requests that refer to
doi:10.1145/1551609.1551642
dblp:conf/hpdc/RaicuFZLMCT09
fatcat:vqz7isgmrzfetmq6l4mzlz43jq