Modern data center systems need to achieve several critical goals in security, performance, and cost efficiency. However, realizing these goals simultaneously is highly challenging. In secure data storage systems, a common practice is to first compress and encrypt data on the host side and then transmit it to the storage system using a log-based structure. This approach, unfortunately, leads to increased complexity and performance penalty. As an emerging storage technology, Computational Storage Drives (CSD) can not only offload heavy computation burdens to storage device hardware, but also provide a virtualized logical storage space, creating new optimization opportunities. In this paper, we showcase two unique opportunities enabled by the new CSD technology in data storage management. By replacing ordinary SSDs with CSDs, we can realize efficient one-to-one mapping from host-side blocks to storage-side CSD blocks, eliminating the need for a complex log-based structure and the associated heavy-cost operations, such as garbage collections (GC). Moreover, with a carefully redesigned data format in each compression unit, CSDs can transparently remove redundant data across encrypted snapshots in data-intensive environments, such as databases. We have developed a prototype and conducted experiments on ScaleFlux’s CSD 3000 devices to demonstrate the efficacy of these solutions. We hope that our system investigations in this work provide valuable insight into CSDs and inspire researchers and practitioners to explore additional cases for adopting CSDs to improve the performance and productivity of data center systems.

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, called Catalyst , 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.

In recent years, Log-Structured Merge Tree (LSMtree) based key-value stores, such as LevelDB and RocksDB, have been widely adopted in data center systems. Though optimized for high-speed write processing, the severe I/O amplification remains a critical constraint that hinders them from reaching their maximum performance potential. Unfortunately, this problem is deeply rooted in the fundamental design of the LSMtree structure. A small number of frequently updated key-value items could quickly pollute the entire tree structure, causing repeated changes in the structure and quickly amplifying the amount of disk IOs across the levels in the tree. In this paper, we present a novel scheme, called Log-assisted LSM-tree (L2SM), to fundamentally address the long-existing I/O amplification problem. L2SM adopts a small-size, multi-level log structure to isolate selected key-value items that have a disruptive effect on the tree structure, accumulates and absorbs the repeated updates in a highly efficient manner, and removes obsolete and deleted key-value items at an early stage. We have prototyped the L2SM structure based on LevelDB. Our evaluation with the YCSB benchmark shows promising results by reducing the amount of disk IOs by up to 40.2%, increasing the throughput by up to 67.4%, and decreasing the average latency by up to 40.1%.

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

In today's data centers, memory-based key-value systems, such as Memcached and Redis, play an indispensable role in providing high-speed data services. The rapidly growing capacity and quickly falling price of DRAM memory in the past years have enabled us to create a large memory-based key-value store, which is able to serve hundreds of Gigabytes to even Terabytes of key-value data all in memory. Unfortunately, CPU cache in modern processors has not seen a similar growth in capacity, still remaining at the level of a few dozens of Megabytes. Such an extremely low cache-to-memory ratio (less than 0.1%) poses a significant new challenge---the limited CPU cache is becoming a severe performance bottleneck that hinders us from fully exploiting the great potential of high-speed memory-based key-value stores. To address this critical challenge, we propose a highly cache-efficient scheme, called Cavast , to optimize the cache utilization of large-capacity in-memory key-value stores. Our goal is to maximize cache efficiency and system performance without any hardware changes. We first present two light-weight, software-only mechanisms to enable user to indirectly control the cache content at application level. Then we propose a set of optimization policies to address several critical design issues that impair cache's efficacy in the current key-value store systems. By carefully reorganizing the data layout in memory, redesigning the hash indexing structure, and offloading garbage collection, we can effectively improve the utilization of the limited cache space. We have developed a module in Linux as a kernel-level support, and implemented two prototypes based on Memcached and Redis with the proposed Cavast scheme. Our experimental studies show promising results. On a 6-core Intel Xeon processor with only 15-MB cache, we can raise the cache hit ratio up to 82.7% with a very small cache-to-memory ratio (0.023%), and significantly increase the key-value system throughput by a factor of up to 4.2.

As storage systems become increasingly heterogeneous and complex, it adds burdens on DBAs, causing suboptimal performance even after a lot of human efforts have been made. In addition, existing monitoring-based storage management by access pattern detections has difficulties to handle workloads that are highly dynamic and concurrent. To achieve high performance by best utilizing heterogeneous storage devices, we have designed and implemented a heterogeneity-aware software framework for DBMS storage management called hStorage-DB, where semantic information that is critical for storage I/O is identified and passed to the storage manager. According to the collected semantic information, requests are classified into different types. Each type is assigned a proper QoS policy supported by the underlying storage system, so that every request will be served with a suitable storage device. With hStorage-DB, we can well utilize semantic information that cannot be detected through data access monitoring but is particularly important for a hybrid storage system. To show the effectiveness of hStorage-DB, we have implemented a system prototype that consists of an I/O request classification enabled DBMS, and a hybrid storage system that is organized into a two-level caching hierarchy. Our performance evaluation shows that hStorage-DB can automatically make proper decisions for data allocation in different storage devices and make substantial performance improvements in a cost-efficient way.

In a typical commercial multi-core processor, the last level cache (LLC) is shared by two or more cores. Existing studies have shown that the shared LLC is beneficial to concurrent query processes with commonly shared data sets. However, the shared LLC can also be a performance bottleneck to concurrent queries, each of which has private data structures, such as a hash table for the widely used hash join operator, causing serious cache conflicts. We show that cache conflicts on multi-core processors can significantly degrade overall database performance. In this paper, we propose a hybrid system method called MCC-DB for accelerating executions of warehouse-style queries, which relies on the DBMS knowledge of data access patterns to minimize LLC conflicts in multi-core systems through an enhanced OS facility of cache partitioning. MCC-DB consists of three components: (1) a cacheaware query optimizer carefully selects query plans in order to balance the numbers of cache-sensitive and cache-insensitive plans; (2) a query execution scheduler makes decisions to co-run queries with an objective of minimizing LLC conflicts; and (3) an enhanced OS kernel facility partitions the shared LLC according to each query's cache capacity need and locality strength. We have implemented MCC-DB by patching the three components in PostgreSQL and Linux kernel. Our intensive measurements on an Intel multi-core system with warehouse-style queries show that MCC-DB can reduce query execution times by up to 33%.