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The concept of memory disaggregation has recently been gaining traction inresearch. With memory disaggregation, data center compute nodes can directlyaccess memory on adjacent nodes and are therefore able to overcome local memoryrestrictions, introducing a new data management paradigm for distributedcomputing. This paper proposes and demonstrates a memory disaggregatedin-memory object store framework for big data applications by leveraging thenewly introduced ThymesisFlow memory disaggregation system. The frameworkextends the functionality of the pre-existing Apache Arrow Plasma object storeframework to distributed systems by enabling clients to easily and efficientlyproduce and consume data objects across multiple compute nodes. This allows bigdata applications to increasingly leverage parallel processing at reduceddevelopment costs. In addition, the paper includes latency and throughputmeasurements that indicate only a modest performance penalty is incurred forremote disaggregated memory access as opposed to local (~6.5 vs ~5.75 GiB/s).The results can be used to guide the design of future systems that leveragememory disaggregation as well as the newly presented framework. This work isopen-source and publicly accessible at https://doi.org/10.5281/zenodo.6368998.

Memory-Disaggregated In-Memory Object Store Framework for Big Data Applications