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Remote memory techniques for datacenter applications have recently gained agreat deal of popularity. Existing remote memory techniques focus on theefficiency of a single application setting only. However, when multipleapplications co-run on a remote-memory system, significant interference couldoccur, resulting in unexpected slowdowns even if the same amounts of physicalresources are granted to each application. This slowdown stems from massivesharing in applications' swap data paths. Canvas is a redesigned swap systemthat fully isolates swap paths for remote-memory applications. Canvas allowseach application to possess its dedicated swap partition, swap cache,prefetcher, and RDMA bandwidth. Swap isolation lays a foundation for adaptiveoptimization techniques based on each application's own access patterns andneeds. We develop three such techniques: (1) adaptive swap entry allocation,(2) semantics-aware prefetching, and (3) two-dimensional RDMA scheduling. Athorough evaluation with a set of widely-deployed applications demonstratesthat Canvas minimizes performance variation and dramatically reducesperformance degradation.

Canvas: Isolated and Adaptive Swapping for Multi-Applications on Remote Memory