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Having dominated databases and various data management systems for decades,B⁺-tree is infamously subject to a logging dilemma: One could improveB⁺-tree speed performance by equipping it with a larger log, which nevertheless will degrade its crash recovery speed. Such a logging dilemma is particularly prominent in the presence of modern workloads that involve intensive small writes. In this paper, we propose a novel solution, calledper-page loggingbasedB⁺-tree, which leverages the emerging computational storage drive (CSD) with built-in transparent compression to fundamentally resolve the logging dilemma. Our key idea is to divide the large single log into many small (e.g., 4KB), highly compressibleper-page logs, each being statically bounded with aB⁺-tree page. All per-page logs together form a very large over-provisioned log space forB⁺-tree to improve its operational speed performance. Meanwhile, during crash recovery,B⁺-tree does not need to scan any per-page logs, leading to a recovery latency independent from the total log size. We have developed and open-sourced a fully functional prototype. Our evaluation results show that, under small-write intensive workloads, our design solution can improveB⁺-tree operational throughput by up to 625.6% and maintain a crash recovery time of as low as 19.2 ms, while incurring a minimal storage overhead of only 0.5-1.6%. 

In-memory key-value cache systems, such as Memcached and Redis, are essential in today's data centers. A key mission of such cache systems is to identify the most valuable data for caching. To achieve this, the current system design keeps track of each key-value item's access and attempts to make accurate estimation on its temporal locality. All it aims is to achieve the highest cache hit ratio. However, as cache capacity quickly increases, the overhead of managing metadata for a massive amount of small key-value items rises to an unbearable level. Put it simply, the current fine-grained, heavy-cost approach cannot continue to scale. In this paper, we have performed an experimental study on the scalability challenge of the current key-value cache system design and quantitatively analyzed the inherent issues related to the metadata operations for cache management. We further propose a key-value cache management scheme, calledCatalyst, based on a highly efficient metadata structure, which allows us to make effective caching decisions in a scalable way. By offloading non-essential metadata operations to GPU, we can further dedicate the limited CPU and memory resources to the main service operations for improved throughput and latency. We have developed a prototype based on Memcached. Our experimental results show that our scheme can significantly enhance the scalability and improve the cache system performance by a factor of up to 4.3.