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TRIAD: Creating Synergies Between Memory, Disk and Log in Log Structured Key-Value Stores.

, , , , , , , and . USENIX ATC, page 363-375. USENIX Association, (2017)

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Towards a General Framework for ML-based Self-tuning Databases., , , , and . CoRR, (2020)Optimistic Causal Consistency for Geo-Replicated Key-Value Stores., , and . ICDCS, page 2626-2629. IEEE Computer Society, (2017)Wren: Nonblocking Reads in a Partitioned Transactional Causally Consistent Data Store., , and . DSN, page 1-12. IEEE Computer Society, (2018)An Extremum Seeking Algorithm for Message Batching in Total Order Protocols., , , and . SASO, page 89-98. IEEE Computer Society, (2012)TRIAD: Creating Synergies Between Memory, Disk and Log in Log Structured Key-Value Stores., , , , , , , and . USENIX ATC, page 363-375. USENIX Association, (2017)A flexible framework for accurate simulation of cloud in-memory data stores., , , , , , , and . Simul. Model. Pract. Theory, (2015)Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs., , , , , and . ICDCS, page 56-66. IEEE, (2020)Performance Modelling of Partially Replicated In-Memory Transactional Stores., and . MASCOTS, page 265-274. IEEE Computer Society, (2014)Automated Workload Characterization in Cloud-based Transactional Data Grids., , , , , , and . IPDPS Workshops, page 1525-1533. IEEE Computer Society, (2012)Lynceus: Tuning and Provisioning Data Analytic Jobs on a Budget., , , , and . CoRR, (2019)