Hybrid transactional/analytical processing (HTAP) would overload database systems. To alleviate performance interference between transactions and analytics, recent research pursues the potential of in-storage processing (ISP) using commodity computational storage devices (CSDs). However, in-storage query processing faces technical challenges in HTAP environments. Continuously updated data versions pose two hurdles: (1) data items keep changing, and (2) finding visible data versions incurs excessive data access in CSDs. Such access patterns dominate the cost of query processing, which may hinder the active deployment of CSDs. This paper addresses the core issues by proposing an a nalyt i c offloa d e ngine (AIDE) that transforms engine-specific query execution logic into vendor-neutral computation through a canonical interface. At the core of AIDE are the canonical representation of vendor-specific data and the separate management of data locators. It enables any CSD to execute vendor-neutral operations on canonical tuples with separate indexes, regardless of host databases. To eliminate excessive data access, we prescreen the indexes before offloading; thus, host-side prescreening can obviate the need for running costly version searching in CSDs and boost analytics. We implemented our prototype for PostgreSQL and MyRocks, demonstrating that AIDE supports efficient ISP for two databases using the same FPGA logic. Evaluation results show that AIDE improves query latency up to 42× on PostgreSQL and 34× on MyRocks.

Internet routers/switches are vulnerable to system failures which require a power reset. Tremendous efforts are made to guarantee the high availability of the systems. A recent work shows that a phase change memory (PCM)-based routing lookup table can achieve high availability in the destination lookup of routers/switches. However, a packet buffer in routers/switches cannot benefit from the lookup table approach because it requires a much larger and higher-bandwidth memory system and its memory traffic is equally divided into reads and writes, while routing table accesses are mostly read-dominant. PCM can provide high availability even when the system undergoes a power reset but exhibits unacceptable write bandwidth. In this work, we propose a magnetic RAM (MRAM)/PCM-based hybrid memory packet buffer and a packet mapping method. A small MRAM combined with a large PCM can outperform the dynamic random access memory (DRAM)-based packet buffer by 28.5% or 22.4% on average for internet-mix packet traffic when optimizing only bandwidth or both bandwidth and lifetime. The proposed adaptive packet mapping method maps small packets less than a predetermined packet size threshold to MRAM for maximizing PCM bandwidth as small packets degrade row buffer locality in PCM. In addition, the mapping method dynamically changes the packet size threshold to capture the working set of packet buffering, which significantly improves PCM lifetime.

Performance critical transaction and storage systems require fast persistence of write data. Typically, a non-volatile RAM (NVRAM) is employed on the datapath to the permanent storage, to temporarily and quickly store write data before the system acknowledges the write request. NVRAM is commonly implemented with battery-backed DRAM. Unfortunately, battery-backed DRAM is small and costly, and occupies a precious DIMM slot. In this paper, we make a case for dual, byte- and block-addressable solid-state drive (2B-SSD), a novel NAND flash SSD architecture designed to offer a dual view of byte addressability and traditional block addressability at the same time. Unlike a conventional storage device, 2B-SSD allows accessing the same file with two independent byte- and block-I/O paths. It controls the data transfer between its internal DRAM and NAND flash memory through an intuitive software interface, and manages the mapping of the two address spaces. 2B-SSD realizes a wholly different way and speed of accessing files on a storage device; applications can access them directly using memory-mapped I/O, and moreover write with a DRAM-like latency. To quantify the benefits of 2B-SSD, we modified logging subsystems of major database engines to store log records directly on it without buffering them in the host memory. When running popular workloads, we measured throughput gains in the range of 1.2X and 2.8X with no risk of data loss.

Data-intensive queries are common in business intelligence, data warehousing and analytics applications. Typically, processing a query involves full inspection of large in-storage data sets by CPUs. An intuitive way to speed up such queries is to reduce the volume of data transferred over the storage network to a host system. This can be achieved by filtering out extraneous data within the storage, motivating a form of near-data processing. This work presents Biscuit, a novel near-data processing framework designed for modern solid-state drives. It allows programmers to write a data-intensive application to run on the host system and the storage system in a distributed, yet seamless manner. In order to offer a high-level programming model, Biscuit builds on the concept of data flow. Data processing tasks communicate through typed and data-ordered ports. Biscuit does not distinguish tasks that run on the host system and the storage system. As the result, Biscuit has desirable traits like generality and expressiveness, while promoting code reuse and naturally exposing concurrency. We implement Biscuit on a host system that runs the Linux OS and a high-performance solid-state drive. We demonstrate the effectiveness of our approach and implementation with experimental results. When data filtering is done by hardware in the solid-state drive, the average speed-up obtained for the top five queries of TPC-H is over 15x.

This paper presents YourSQL , a database system that accelerates data-intensive queries with the help of additional in-storage computing capabilities. YourSQL realizes very early filtering of data by offloading data scanning of a query to user-programmable solid-state drives. We implement our system on a recent branch of MariaDB (a variant of MySQL). In order to quantify the performance gains of YourSQL, we evaluate SQL queries with varying complexities. Our result shows that YourSQL reduces the execution time of the whole TPC-H queries by 3.6×, compared to a vanilla system. Moreover, the average speed-up of the five TPC-H queries with the largest performance gains reaches over 15×. Thanks to this significant reduction of execution time, we observe sizable energy savings. Our study demonstrates that the YourSQL approach, combining the power of early filtering with end-to-end datapath optimization, can accelerate large-scale analytic queries with lower energy consumption.

This tutorial overviews flash memory solid-state drive technologies and recent advances in the field.

This paper proposes a scheme to enhance energy efficiency and QoS of real time network applications on smart phone. The scheme reduces energy consumption and increases the successful interaction rate between the client at smart phone and the busy server of real time network application by deploying a surrogate of the client at smart phone in cloud computing environment. All interactions among the client at smart phone, the application server and the surrogate in the cloud are controlled by tokens. The proposed scheme considers security as well as energy waste in the cloud.

Phishing websites impersonate legitimate counterparts to lure users into visiting their websites. Once users visit a phishing website then the phishing website may steal users' private information or cause drive-by downloads. To detect a phishing website, human experts compare the claimed identity of a website with features in the website. For example, human experts often compare the domain name in the URL against the claimed identity. Most legitimate websites have domain names that match their identities, while phishing websites usually have less relevance between their domain names and their claimed (fake) identities. In addition to blacklists, whitelists, heuristics, and classifications used in the state-of-the-art systems, we propose to consider websites' identity claims. Our phishing detection system mimics this human expert behavior. Given a website, our system learns the identity that this website claims, and computes the textual relevance between this claimed identity and other features in the website. Our phishing detection system then uses this textual relevance as one of the features for classification, and our classifiers achieve more than 98% of true positive rate and very low false positive rate between 0.5% and 1%.