A computational storage device incorporating a computation unit inside or near its storage unit is a highly promising technology to maximize a storage server’s performance. However, to apply such computational storage devices and take their full potential in virtualized environments, server architects must resolve a fundamental challenge: cost-effective virtualization . This critical challenge can be directly addressed by the following questions: (1) how to virtualize two different hardware units (i.e., computation and storage), and (2) how to integrate them to construct virtual computational storage devices, and (3) how to provide them to users. However, the existing methods for computational storage virtualization severely suffer from their low performance and high costs due to the lack of hardware-assisted virtualization support. In this work, we propose SmartFVM-Engine , an FPGA card designed to maximize the performance and cost-effectiveness of computational storage virtualization. SmartFVM-Engine introduces three key ideas to achieve the design goals. First, it achieves high virtualization performance by applying hardware-assisted virtualization to both computation and storage units. Second, it further improves the performance by applying hardware-assisted resource orchestration for the virtualized units. Third, it achieves high cost-effectiveness by dynamically constructing and scheduling virtual computational storage devices. To the best of our knowledge, this is the first work to implement a hardware-assisted virtualization mechanism for modern computational storage devices.

Modern servers are actively deploying Solid-State Drives (SSDs) thanks to their high throughput and low latency. However, current server architects cannot achieve the full performance potential of commodity SSDs, as SSDs are complex devices designed for specific goals (e.g., latency, throughput, endurance, cost) with their internal mechanisms undisclosed to users. In this article, we propose SSDcheck , a novel SSD performance model to extract various internal mechanisms and predict the latency of next access to commodity black-box SSDs. We identify key performance-critical features (e.g., garbage collection, write buffering) and find their parameters (i.e., size, threshold) from each SSD by using our novel diagnosis code snippets. Then, SSDcheck constructs a performance model for a target SSD and dynamically manages the model to predict the latency of the next access. In addition, SSDcheck extracts and provides other useful internal mechanisms (e.g., fetch unit in multi-queue SSDs, background tasks triggering idle-time interval) for the storage system to fully exploit SSDs. By using those useful features and the performance model, we propose multiple practical use cases. Our evaluations show that SSDcheck’s performance model is highly accurate, and proposed use cases achieve significant performance improvement in various scenarios.

Storage systems play a critical role in modern servers which run highly data-intensive applications. To satisfy the high performance and capacity demands of such applications, storage systems now deploy an array of fast SSDs per server. To reduce the storage cost of employing many SSDs per server, storage systems actively perform inline data reduction (e.g., data deduplication, compression). Existing inline data reduction studies can achieve high performance and scalability by offloading computation-intensive data-reduction operations to dedicated hardware accelerators. However, such existing studies suffer from limited workload support and scalability. For example, they reduce only large data blocks, which incur many IO requests, leading to low data reduction rates, and their offloading overlooks memory-intensive operations, leading to the unoptimal scalability.

It is essential for smart house researchers to have large datasets from actual environments. However, not all researchers have sufficient budgets to build test beds. These researchers need a simulator that can synthesize realistic sensory datasets. To solve this problem, we propose the 'UbiSim' simulator for activity recognition research. UbiSim provides a 3D graphical user interface to enable spatial perception using multiple sensors, including those that detect motion, pressure, vibration, temperature, and contact, along with RFID tags and receivers. The sensors are designed to verify collisions in a virtual space as a means to operate with minimal computational costs. Our proposed methods were tested in a virtual environment. The results show that the smart house simulator achieves real-time performance.

As a practical application, we focus on the systematic genetic algorithm for a multi-head surface mounting machine. The multi-head surface mounting machine is becoming increasingly popular due to its merit that the mounting speed is high though the price is low. Since the scheduling problem for these machines is known to be of a combinatorial nature and NP-hard, no exact algorithm has been proposed. A genetic algorithm (GA) has been recognized as an efficient and useful procedure for solving large combinatorial optimization problems. The paper proposes to use a GA to solve the scheduling problem for the multi-head surface mounting machine. The result of computer simulation shows that the proposed method performs better than the conventional methods.