To explore the opportunity of expanding the page cache using disaggregated memory for file-backed pages, this study presents BalloonStasher, an RDMA-based disaggregated memory for data-intensive applications. Utilizing the ephemeral nature of the page cache, BalloonStasher dynamically adapts to the changing page cache demands of multiple clients. BalloonStasher supports one-sided RDMA-based memory pooling or two-sided RDMA-based memory sharing when using memory nodes. Additionally, it also supports peer memory mode, which utilizes the idle memory of peer nodes. Our extensive performance study compares the benefits and limitations of the three cache modes, and shows that BalloonStasher can mitigate the memory underutilization problem and improve the performance of data-intensive applications by a large margin.

High-speed data ingestion is critical in time-series workloads that are driven by the growth of Internet of Things (IoT) applications. We observe that traditional tree-based indexes encounter severe scalability bottlenecks for time-series workloads that insert monotonically increasing timestamp keys into an index; all insertions go to a small memory region that sees extremely high contention. In this work, we present a new index design, B link -hash, that enhances a tree-based index with hash leaf nodes to mitigate the contention of monotonic insertions --- insertions go to random locations within a hash node (which is much larger than a B+-tree node) to reduce conflicts. We develop further optimizations (median approximation and lazy split) to accelerate hash node splits. We also develop structure adaptation optimizations to dynamically convert a hash node to B+-tree nodes for good scan performance. Our evaluation shows that B link -hash achieves up to 91.3× higher throughput than conventional indexes in a time-series workload that monotonically inserts timestamps into an index, while showing comparable scan performance to a well-optimized B+-tree.

In this work, we propose B 3 -tree , a hybrid index for persistent memory that leverages the byte-addressability of the in-memory index and the page locality of B-trees. As in the byte-addressable in-memory index, B 3 -tree is updated by 8-byte store instructions. Also, as in disk-based index, B 3 -tree is failure-atomic since it makes every 8-byte store instruction transform a consistent index into another consistent index without the help of expensive logging. Since expensive logging becomes unnecessary, the number of cacheline flush instructions required for B 3 -tree is significantly reduced. Our performance study shows that B 3 -tree outperforms other state-of-the-art persistent indexes in terms of insert and delete performance. While B 3 -tree shows slightly worse performance for point query performance, the range query performance of B 3 -tree is 2x faster than FAST and FAIR B-tree because the leaf page size of B 3 -tree can be set to 8x larger than that of FAST and FAIR B-tree without degrading insertion performance. We also show that read transactions can access B 3 -tree without acquiring a shared lock because B 3 -tree remains always consistent while a sequence of 8-byte write operations are making changes to it. As a result, B 3 -tree provides high concurrency level comparable to FAST and FAIR B-tree.