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This paper proposes a scalable and efficient cacheupdate technique to improve the performance of in-memorycluster computing in Spark, a popular open-source system forbig data computing. Although the memory cache speeds up dataprocessing in Spark, its data immutability constraint requiresreloading the whole RDD when part of its data is updated. Suchconstraint makes the RDD update inefficient. To address...
We present an efficient distributed graph database architecture for large scale social computing. The architecture consists of a distributed graph data processing system and a distributed graph data storage system. We leverage the advantages of both systems to achieve efficient social computing. We conduct extensive experiments to demonstrate the performance of our system. We employ four real-world,...
With the emergence of large-scale storage systems that separate metadata management from file read/write operations, and with requests targetting metadata account for over 80% of the total number of I/O requests, metadata management has become an interesting research problem on its own. When designing a metadata server cluster, the partitioning of the metadata among the servers is of critical importance...
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