Numerous data-intensive applications benefit from offloading data processing to storage devices, typically Computational Storage Devices (CSD). Co-locating requests from diverse applications to one CSD offers better performance and power efficiency than dedicating CSDs to a single application. However, current CSD scheduling frameworks struggle to effectively manage contention among CPU, flash I/O, and buffer resources across requests due to less consideration in their inter-dependencies. This paper proposes REMUS, a CSD scheduling framework handling multiple requests for commercial SSD with multiple homogeneous cores. The key idea of REMUS is to allocate workloads across multiple cores based on the distribution of the Logical Block Address (LBA) of requests, and to mitigate stall time by sorting requests according to their urgency for resources, where urgency is quantified by each request’s remaining buffer capacity. Furthermore, a request batching scheme that intelligently groups the requests to be scheduled according to their characteristics is proposed to provide congestion control for REMUS and to minimize the contention it introduces. We conduct experiments on both a simulator and a real CSD platform. The experiment results show that REMUS improved throughput by 1.51× on the simulator and 1.39× on the real platform on average compared to the baselines.

The latency and energy consumption incurred by I/O accesses are significant in data-centric computing systems. Computational Storage Drive (CSD) can largely reduce data movement, and thus reduce I/O latency and energy consumption by offloading data-intensive processing to processors inside the storage device. In this paper, we study the problem of how to efficiently utilize the limited processing and memory resources of CSD to simultaneously serve multiple I/O requests from various applications with different real-time requirements. We proposed SERICO, a system of scheduling computational I/O requests in CSD. The key idea of SERICO is to perform admission control of real-time computational I/O requests by online schedulability analysis, to avoid wasting the processing resources and memory capacity of CSD in doing meaningless work for those requests deemed to violate the timing constraints. Each admitted computational I/O request is served in a controlled manner with carefully designed parameters, to meet its timing constraint with minimal memory cost. We evaluate SERICO with both synthetic workloads on simulators and representative applications on realistic CSD hardware. Experiment results show that SERICO significantly outperforms the default method used in the CSD device and the standard deadline-driven scheduling approach.

Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices upon the traditional storage system rely on the paging mechanism to migrate pages to the host DRAM, tending to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, an efficient fine-grained read framework, Pipette, is proposed in this article as an extension to the traditional I/O framework. With adaptive design for caching, merging, and scheduling, Pipette explores locality and acceleration for fine-grained read requests to establish an efficient byte-granular read path upon the dedicated byte-addressable interface. When the Pipette prototype on an SSD runs popular workloads, we measured throughput gains by up to 50% and 54% with traffic reduction in the range of$41.3\times $and$56.5\times $.

Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices tend to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, a fine-grained read framework, Pipette, is proposed in this paper, as an extension to the traditional I/O framework. With an adaptive caching design, Pipette framework offers a tremendous reduction in I/O traffic as well as achieves significant performance gain. A Pipette prototype was implemented with Ext4 file system on an SSD for two real-world applications, where the I/O throughput is improved by 31.6% and 33.5%, and the I/O traffic is reduced by 95.6% and 93.6%, respectively.