To manage massive trajectory data, we propose a novel cloud-based trajectory data management technique, Springbok, which leverages cloud storage to balance performance and monetary costs. Unlike existing key-value, relational, and time series databases, Springbok natively models trajectories as first-class data objects via a spatio-temporal series data model, enabling efficient insertion, query processing, and cost-aware storage management in cloud environments. It further employs an optimized indexing scheme that accounts for both the characteristics of trajectory data and the properties of cloud storage, enabling efficient query execution. In addition, Springbok adopts a tiered cloud storage architecture with carefully designed data layouts, flushing, access, and compression policies, guided by cloud storage performance characteristics and pricing models, to jointly optimize performance and cost. We implemented a fully functional prototype of Springbok supporting core trajectory queries, deletion, and crash recovery, and evaluated it using both real-world and synthetic datasets. The results show that Springbok achieves comparable or better performance than state-of-the-art systems in most cases while significantly reducing monetary costs, demonstrating its effectiveness in balancing performance and cost.

In-storage computing (ISC) aims to mitigate the excessive data movement between the host memory and storage by offloading computation to storage devices for in-situ execution. However, ensuring data freshness remains a key challenge for practical ISC. For performance considerations, many data processing systems implement a buffer manager to cache part of the on-disk data in the host memory. While the host applications commit updates to the in-memory cached copies of the data, ISC operators offloaded to the device only have access to the on-disk persistent data. Thus, ISC may miss the most recent updates from the host and produce incorrect results after reading the stale and inconsistent data from the persistent storage. With this limitation, current ISC can only be used in read-only settings where the on-disk data are not subject to concurrent updates. To tackle this problem, we propose a cooperative buffer manager for ISC to transparently provide data freshness guarantees to host applications. Proposed methods allow the device to synchronize with the host buffer manager and decide whether to read the most recent copy of data from host memory or flash memory. We implement our method based on a real hardware platform and perform evaluation with a Bs--tree based key-value store. Experiments show that our method can provide transparent data freshness for host applications with reduced latency.

Modern SSD firmware is continuously optimized for higher parallelism to match the growing frontend PCIe bandwidth with more backend flash channels. Although a multi-core microprocessor is typically adopted to concurrently process independent NVMe requests from multiple NVMe queues, the existing one-to-many thread-request mapping model with each thread serving one or more incoming I/O requests has poor scalability due to severe lock contention problem, especially in cache management.

Massive trajectory data are continuously generated with the rapid development of location-acquisition devices such as vehicles and smartphones. To provide services for applications such as mobility pattern discovery, how to manage such gigantic trajectory data to support queries in an efficient and cost-effective way becomes vitally important. Due to its low cost, large storage capacity, and reliability, cloud storage such as S3 becomes a new paradigm for storing gigantic data and is used in some general-purpose data management systems to strike a balance between performance and monetary costs. However, few systems exploit the inherent features of cloud storage for trajectory data. In this paper, we propose a novel cloud-based trajectory data management technique, called Springbok, to bridge the gap between massive trajectory data and cloud storage. Springbok is designed to address several key issues related to cloud-based trajectory data management. First, Springbok treats trajectories as first-class citizens using a new spatio-temporal series data model that can benefit insertion, query processing, and storage management in the cloud. Second, a holistic indexing scheme that considers features of trajectory data and cloud storage is designed to facilitate efficient queries. Third, based on performance features and billing models of cloud storage, we design effective data layouts for trajectory data and corresponding data flushing and access policies in a tiered cloud storage architecture for performance improvement and cost reduction. We have implemented a fully functional prototype of Springbok and conducted evaluations using both real-world and synthetic datasets, demonstrating its ability to achieve a good tradeoff between performance and monetary costs. Springbok has been open-sourced for public access.

Embedded machine learning applications face challenges related to massive data movement and high computational intensity, exacerbated by the limited performance of mobile devices. Computational storage devices (CSDs) pose huge potential for accelerating both data-intensive and computation-intensive embedded machine learning tasks by effectively reducing data movement and leveraging built-in accelerators. However, existing in-storage-computing (ISC) frameworks either require invasive customization of existing host driver layers or necessitate complex device firmware modifications, hindering the widespread deployment of CSDs. In addition, the lack of file semantics and the constrained internal resources within CSD implicitly compromise system performance and impact normal read/write performance. In this article, we aim to provide a nonintrusive in-storage-computing framework for embedded applications, named NICE. This framework includes an easy-to-use ISC programming interface that bypasses the kernel stack and requires no modification to the host NVMe driver, which is achieved through a novel hyper-addressing-based programming library and a file-aware page data layout within the CSD. In addition, we incorporate a lightweight kernel with coroutine-based command scheduling and several FPGA-based accelerators within the storage device firmware to enhance the performance of embedded machine learning applications while ensuring that the normal I/O performance remains unaffected. NICE is implemented on real CSD hardware integrated with ARM and FPGA. Experimental results demonstrate that our NICE framework can achieve an average latency performance improvement of$43.5\times $($9.32\times $) compared to CPU-(GPU-) based embedded machine learning solutions using the state-of-the-art NVIDIA Jetson NX platform, with$27.5\times $($4.3\times $) higher energy efficiency. NICE also has$34.2\times $less software and I/O performance overheads than state-of-the-art ISC frameworks.

In-storage computing with computational SSDs is emerging as one effective solution for I/O bottlenecks in big data applications such as AI learning model training. Specifically, with in-SSD computing, computation can be pushed down to SSDs and the volume of the output data that will be transferred back to the host can be greatly reduced. However, there are several fundamental issues for applications to fully exploit in-SSD computing with simple and efficient function offloading. In this paper, we present three challenges for in-SSD computing, namely, data model, programming framework, and storage/computing integration, and discuss possible research directions.

Recently, solid-state drives (SSDs) have been used in a wide range of emerging data processing systems. Essentially, an SSD is a complex embedded system that involves both hardware and software design. For the latter, firmware modules such as the flash translation layer (FTL) orchestrate internal operations and flash management, and are crucial to the overall input/output performance of an SSD. Despite the rapid development of new SSD features in the market, the research of flash firmware has been mostly based on simulations due to the lack of a realistic and extensible SSD development platform. In this paper, we propose SoftSSD, a software-oriented SSD development platform for rapid flash firmware prototyping. The core of SoftSSD is a novel framework with an event-driven programming model. With the programming model, new FTL algorithms can be implemented and integrated into a full-featured flash firmware in a straightforward way. The resulting flash firmware can be deployed and evaluated on a hardware development board, which can be connected to a host system via peripheral component interconnect express and serve as a normal non-volatile memory express SSD. Different from existing hardware-oriented development platforms, SoftSSD implements the majority of SSD components (e.g., host interface controller) in software, so that data flows and internal states that were once confined in the hardware can now be examined with a software debugger, providing the observability and extensibility that are critical to the rapid prototyping and research of flash firmware. We describe the programming model and hardware design of SoftSSD. We also perform experiments with real application workloads on a prototype board to demonstrate the performance and usefulness of SoftSSD, and release the open-source code of SoftSSD for public access.

Multidisplay Android systems are emerging and require new display management. The current Android design exposes and offloads the multiscreen display management to app developers. As a consequence, apps cannot utilize multiscreen without redevelopment. This article proposes MSA, a novel Android app development framework for transparent multiscreen support. MSA cooperates with existing Android system services to map the single-screen views provided by apps to multiple screens and maps input events backward correspondingly. Following the new framework, the app development of single-screen Android systems and multiscreen Android systems are unified. Thus, both existing apps and future app development can directly utilize multiple screens with single-screen-based techniques, such as multiwindow and resizable-activity, instead of redevelopment through multiscreen-dedicated new methods such as presentation classes. We have implemented an MSA prototype with real hardware and released the source code for public access. Experimental results show that using MSA, without any modifications, existing apps can directly run and fully exploit multiple screens with better performance and less overhead compared with the state-of-the-art multiscreen Android system.

Solid-state drives (SSDs) have been used in a wide range of emerging data processing systems. To fully utilize the massive internal parallelism delivered by SSDs, manufacturers begin to utilize high-performance multi-core microprocessors in scalable flash firmware to process I/O requests concurrently. Designing scalable multi-core flash firmwares requires simulation tools that can model the features of a multi-core environment. However, existing SSD simulators assume a single-threading execution model and are not capable of modelling overheads incurred by multi-threading firmware execution such as lock contentions. In this paper, we propose MCMQ, a novel framework for simulating scalable multi-core flash firmware. The framework is based on an emulated multi-core RISC processor and supports executing multiple I/O traces in parallel through a multi-queue interface. Experiment results show the effectiveness of the proposed framework. We have released the open-source code of MCMQ for public access.

Recently, solid-state drives (SSDs) have been used in a wide range of emerging data processing systems. Essentially, an SSD is a complex embedded system that involves both hardware and software design. For the latter, firmware modules such as the flash translation layer (FTL) orchestrate internal operations and flash management, and are crucial to the overall I/O performance of an SSD. Despite the rapid development of new features of SSDs in the market, the research of flash firmware has been mostly simulation-based due to the lack of a realistic and extensible SSD development platform. In this paper, we propose SoftSSD, a software-defined SSD development platform for rapid flash firmware prototyping. The core of SoftSSD is a novel framework with an event-driven programming model. With the programming model, new FTL algorithms can be implemented and integrated into a full-featured flash firmware in a straightforward way. The resulting flash firmware can be deployed and evaluated on a hardware development board, which can be connected to a host system via PCIe and serve as a normal NVMe SSD. Different from existing hardware-oriented development platforms, SoftSSD implements the majority of SSD components (e.g., host interface controller) in software so that data flows and internal states that were once confined in the hardware can now be examined with a software debugger, providing the observability and the extensibility that are critical to the rapid prototyping and research of flash firmware. This paper describes the programming model and hardware design of SoftSSD. We also perform experiments with real application workloads on a prototype board to demonstrate the performance and usefulness of SoftSSD and released the open-source code of SoftSSD for public access.

Modern time series databases come with a tag-based query interface that allows users to select time series, which are essentially sequences of timestamped data values, based on a set of specific tags. A tagging index is an important component that can efficiently provide such tag-based services. However, existing methods store tag information in external databases or time-partitioned data structures, which has a negative impact on query performance. In this paper, we present a novel abstraction for efficient queries of tag information in time series databases: a hybrid tagging index that manages all tags in one place. By managing tag information globally in a single disk-based data structure, we can fundamentally relieve memory pressure and eliminate I/O overhead of duplicate metadata from existing methods. Furthermore, the tagging index is internally partitioned by time to support time range based queries and data retention which are essential to time series databases. We implement the proposed tagging index as a standalone module which can be integrated with time series storage engines. Experiments on the TSBS benchmark show our proposed method can significantly speed up queries by on average 84.0% and 87.2% compared to Prometheus (using a time-partitioned segment method) and Graphite (using an external database for tag management), respectively.

Dual-screen foldable Android smartphones such as Microsoft Surface Duo are emerging. However, due to its internal design, the Android framework cannot support combined mode, by which two screens can be integrated into one, without modifications, thus requiring new display management. Android introduces a new dual-screen display method, called presentation class, to handle this problem; however, existing apps need to be redesigned based on the new class. In this paper, we propose Dual-Screen Android (DSA), a semantics-aware display scheme for dual-screen foldable Android smartphones. DSA is transparent to apps, thus requiring no changes for existing apps, and incurs minimum modification to the Android framework. Specifically, inside Android, DSA duplicates and maps single-screen views provided by apps to dual screens and maps input events backward correspondingly, thus being transparent to apps. DSA also opens the door to store other hardware states (e.g., screen brightness, screen-touch events, etc.), which can be utilized to bridge other software/hardware semantic gaps for further system optimization. To demonstrate this, we design an effective Q-learning energy optimization scheme within DSA to control screen brightness based on predicted users' behaviors. We have implemented DSA based on Android 10 with real hardware and conducted a series of experiments. Experimental results show that based on DSA, without any modifications, existing apps can directly run and fully exploit dual screens with negligible time and memory overheads, and our energy optimization scheme can effectively reduce energy. We have released the open-source code of DSA for public access.

Performance-monitoring timeseries systems such as Prometheus and InfluxDB play a critical role in assuring reliability and operationally. These systems commonly adopt a column-oriented storage model, by which timeseries samples from different time-series are separated, and all samples (with both numeric values and timestamps) in one timeseries are grouped into chunks and stored together. As a group of timeseries are often collected from the same source with the same timestamps, managing timestamps and metrics in a group manner provides more opportunities for query and insertion optimization but posts new challenges as well. Besides, for performance monitoring systems, to support better compression and efficient queries for most recent data that are most likely accessed by users, huge volumes of data are first cached in memory and then periodically flushed to disks. Periodic data flushing incurs high IO overhead, and simply discarding flushed data, which can still serve queries, not only is a waste but also brings huge memory reclamation cost. In this paper, we propose Heracles which integrates two techniques - (1) a new storage model, which enables efficient queries on compressed data by utilizing the shared timestamp column to easily locate corresponding metric values; (2) a novel two-level epoch-based memory manager, which allows the system to gradually flush and reclaim in-memory data while unreclaimed data can still serve queries. Heracles is implemented as a standalone module that can be easily integrated into existing performance monitoring timeseries systems. We have implemented a fully functional prototype with Heracles based on Prometheus tsdb, a representative open-source performance monitoring system, and conducted extensive experiments with real and synthetic timeseries data. Experimental results show that, compared with Prometheus, Heracles can improve the insertion throughput by 171%, and reduce the query latency and space usage by 32% and 30%, respectively, on average. Besides, to compare with other state-of-the-art storage techniques, we have integrated LevelDB (for LSM-tree-based structure) and Parquet (for column stores) into Prometheus tsdb, respectively, and experimental results show Heracles outperform these two integrations. We have released the open-source code of Heracles for public access.

The purpose of this study was to explore the assistant function of 3-D information for I125 particle implantation of multineedle intervention under the guidance of ultrasound. The assistant function of 3-D information was verified by a simulation experiment system which consists of an ultrasound probe, an abdominal phantom, the preoperative computed tomography image of a patient, the electromagnetic tracking device, and the self-developed 3-D image navigation software with a practical and friendly graphical user interface. The simulation particle implantation experiments were divided into the two groups. The first group of experiments was performed with the aid of 3-D information. Seven days later, the second group of experiments was carried out with the aid of 2-D information. We made the statistical analysis of the experimental results obtained by nine medical students, nine interventional radiologists, and nine attending physicians. With the assistance of 3-D information, the percentage of tumor coverage increased (p < 0.01), the operation time shortened (p < 0.01), and the number of insertions reduced (p < 0.01). The assistant function of 3-D information for particle implantation of multineedle intervention under the guidance of ultrasound was technically feasible and effective.