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Mo 23.11.2020 16:00 - 16:45 Uhr, Online: "Automated and Fine-Grained Memory Tiering for In-Memory Databases" (Markus Dreseler, HPI)

By moving less frequently accessed data from DRAM to lower memory and storage tiers, such as non-volatile memory and SSD, an in-memory DBMS can reduce its DRAM footprint. Current approaches either statically move pre-defined parts of the data or depend on the DBA's selection of suitable data. By enabling the DBMS to make automatic tiering decisions, more efficient configurations can be found in cases where static or manual approaches fail. As a research and evaluation platform, we have (1) built a new DBMS whose TPC-H results are on par with state-of-the art research systems. We (2) contribute low-overhead, iterator-based access counters that track all data accesses. Not only can we identify hot, warm, and cold data, but can also distinguish between different access patterns. We further (3) introduce a lightweight memory abstraction layer to allocate data on DRAM, NVM, or secondary storage. This way, we can move table and temporary data from one memory tier to another without having to pause query processing. Our (4) Dynamic Tiering Plugin uses the tracking information and automatically improves the memory utilization.

Short bio:
Markus Dreseler is a Ph.D. student in Prof. Plattner’s research group Enterprise Platform and Integration Concepts at HPI in Potsdam. His research focuses on using new memory technologies to increase the versatility of in-memory databases. Besides his work on enterprise data management, he has participated in the German Digitalgipfel, promoting the use of smart data in Germany. Prior to joining Prof. Plattner's research group, Mr. Dreseler has interned at SAP Labs in Palo Alto and Intel Labs in Santa Clara.

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