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UniCache: Efficient Log Replication through Learning Workload Patterns
KTH Royal Institute of Technology, Sweden.
KTH Royal Institute of Technology, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-9351-8508
2023 (English)In: Advances in Database Technology - EDBT, OpenProceedings.org , 2023, Vol. 46, no 3, p. 471-477, article id 3Conference paper, Published paper (Refereed)
Abstract [en]

Most of the world's cloud data service workloads are currently being backed by replicated state machines. Production-grade log replication protocols used for the job impose heavy data transfer duties on the primary server which need to disseminate the log commands to all the replica servers. UniCache proposes a principal solution to this problem using a learned replicated cache which enables commands to be sent over the network as compressed encodings. UniCache takes advantage of that each replica has access to a consistent prefix of the replicated log which allows them to build a uniform lookup cache used for compressing and decompressing commands consistently. UniCache achieves effective speedups, lowering the primary load in application workloads with a skewed data distribution. Our experimental studies showcase a low pre-processing overhead and the highest performance gains in cross-data center deployments over wide area networks. 

Place, publisher, year, edition, pages
OpenProceedings.org , 2023. Vol. 46, no 3, p. 471-477, article id 3
Keywords [en]
Data transfer, Cloud data, Compressed encoding, Data services, Lookups, Primary loads, Principal solutions, Replica servers, Replication protocol, State-machine, Workload patterns, Wide area networks
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:ri:diva-65680DOI: 10.48786/edbt.2023.39Scopus ID: 2-s2.0-85165119241OAI: oai:DiVA.org:ri-65680DiVA, id: diva2:1787188
Conference
26th International Conference on Extending Database Technology, EDBT 2023. Ioannina. 28 March through 31 March 2023.
Note

This work has been supported by the Swedish Foundation of Strategic Research (Grant No.: BD15-0006), RISE AI, Google Cloud Research Credits Program, and the Wallenberg AI: Autonomous Systems and Software Program (Data-Bound NEST Project).

Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-08-11Bibliographically approved

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