Student: Caitlin Sim (University of California, Berkeley)
Supervisor: Chin Guok (Energy Sciences Network (ESnet))
Abstract: Today’s scientific projects and simulations often require repeated transfer of large data volumes between the storage system and the client. This increases the load on the network, leading to congestion. In order to mitigate these effects, regional data storage cache systems are used to store data locally. This project examines the XCache storage system to closely analyze data trend patterns in the data volume and data throughput performance, while also creating a model for predicting how caches could potentially impact network traffic and data transfer performance overall. The results of the data access patterns demonstrated that traffic volume was reduced by an average factor of 2.35. The hourly and daily prediction models also showed low error values, reinforcing the learning methods used in this effort.
ACM-SRC Semi-Finalist: no
Poster Summary: PDF
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