ACM Transactions on Computer Systems

This tutorial and reference sets out to instruct users in organizing and managing multiple tasks, and multiple programs, in Windows. It focuses on sharing and transferring data and graphics between Windows applications, either using a single computer, or by sharing across a network.

INSIDE Windows NT

User Profiling for Energy Optimisation in Mobile Cloud Computing

The storage subsystem has undergone tremendous innovation in order to keep up with the ever-increasing demand for throughput Non Volatile Memory Express (NVMe) based solid state devices are the latest development in this domain, delivering unprecedented performance in terms of latency and peak bandwidth NVMe drives are expected to be particularly beneficial for I/O intensive applications, with databases being one of the prominent use-casesThis paper provides the first, in-depth performance analysis of NVMe drives Combining driver instrumentation with system monitoring tools, we present a breakdown of access times for I/O requests throughout the entire system Furthermore, we present a detailed, quantitative analysis of all the factors contributing to the low-latency, high-throughput characteristics of NVMe drives, including the system software stack Lastly, we characterize the performance of multiple cloud databases (both relational and NoSQL) on state-of-the-art NVMe drives, and compare that to their performance on enterprise-class SATA-based SSDs We show that NVMe-backed database applications deliver up to 8× superior client-side performance over enterprise-class, SATA-based SSDs

Performance analysis of NVMe SSDs and their implication on real world databases

We analyze the manycore scalability of five widely-deployed file systems, namely, ext4, XFS, btrfs, F2FS, and tmpfs, by using our open source benchmark suite, FXMARK. FXMARK implements 19 microbenchmarks to stress specific components of each file system and includes three application benchmarks to measure the macroscopic scalability behavior. We observe that file systems are hidden scalability bottlenecks in many I/O-intensive applications even when there is no apparent contention at the application level. We found 25 scalability bottlenecks in file systems, many of which are unexpected or counterintuitive. We draw a set of observations on file system scalability behavior and unveil several core aspects of file system design that systems researchers must address.

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Understanding manycore scalability of file systems

Fast storage devices are an emerging solution to satisfy data-intensive applications. They provide high transaction rates for DBMS, low response times for Web servers, instant on-demand paging for applications with large memory footprints, and many similar advantages for performance-hungry applications. In spite of the benefits promised by fast hardware, modern operating systems are not yet structured to take advantage of the hardware’s full potential. The software overhead caused by an OS, negligible in the past, adversely impacts application performance, lessening the advantage of using such hardware. Our analysis demonstrates that the overheads from the traditional storage-stack design are significant and cannot easily be overcome without modifying the hardware interface and adding new capabilities to the operating system.In this article, we propose six optimizations that enable an OS to fully exploit the performance characteristics of fast storage devices. With the support of new hardware interfaces, our optimizations minimize per-request latency by streamlining the I/O path and amortize per-request latency by maximizing parallelism inside the device. We demonstrate the impact on application performance through well-known storage benchmarks run against a Linux kernel with a customized SSD. We find that eliminating context switches in the I/O path decreases the software overhead of an I/O request from 20 microseconds to 5 microseconds and a new request merge scheme called Temporal Merge enables the OS to achieve 87p to 100p of peak device performance, regardless of request access patterns or types. Although the performance improvement by these optimizations on a standard SATA-based SSD is marginal (because of its limited interface and relatively high response times), our sensitivity analysis suggests that future SSDs with lower response times will benefit from these changes. The effectiveness of our optimizations encourages discussion between the OS community and storage vendors about future device interfaces for fast storage devices.

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Optimizing the Block I/O Subsystem for Fast Storage Devices

Native Command Queueing (NCQ) is an optimization technology to maximize throughput by reordering requests inside a disk drive. It has been so successful that NCQ has become the standard in SATA 2 protocol specification, and the great majority of disk vendors have adopted it for their recent disks. However, there is a possibility that the technology may lead to an information gap between the OS and a disk drive. A NCQ-enabled disk tries to optimize throughput without realizing the intention of an OS, whereas the OS does its best under the assumption that the disk will do as it is told without specific knowledge regarding the details of the disk mechanism. Let us call this expectation discord, which may cause serious problems such as request starvations or performance anomaly. In this article, we (1) confirm that expectation discord actually occurs in real systems; (2) propose software-level approaches to solve them; and (3) evaluate our mechanism. Experimental results show that our solution is simple, cheap (no special hardware required), portable, and effective.

NCQ vs. I/O scheduler: Preventing unexpected misbehaviors

In this work, we propose a new batching scheme called temporal merge, which dispatches discontiguous block requests using a single I/O operation. It overcomes the disadvantages of narrow block interface and enables an OS to exploit peak throughput of a storage device for small random requests as well as a single large request. Temporal merge significantly enhances device and channel utilization regardless of access sequentiality of a workload, which has not been achievable by traditional schemes.

We extended the block I/O interface of a DRAM-based SSD in cooperation with its vendor, and implemented temporal merge into I/O subsystem in Linux 2.6.32. The experimental results show that under multi-threaded random access workload, the proposed solution can achieve 87%-100% of peak throughput of the SSD. We expect that the new temporal merge interface will lead to better design of future host controller interfaces such as NVMHCI for next-generation storage devices.

/pdf/exploiting-peak-device-throughput-from-random-access-139mhkltdf.pdf

Exploiting peak device throughput from random access workload

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.

Enhancing QoS and Energy Efficiency of Realtime Network Application on Smartphone Using Cloud Computing

Emerging non-volatile memory technologies as a disk drive replacement raise some issues of software stack and interfaces, which have not been considered in disk-based storage systems. In this work, we present new cooperative schemes including software and hardware to address performance issues with deploying storage-class memory technologies as a storage device. First, we propose a new polling scheme called dynamic interval polling to avoid the unnecessary polls and reduce the burden on storage system bus. Second, we propose a pipelined execution between storage device and host OS called pipelined post I/O processing. By extending vendor-specific I/O interfaces between software and hardware, we can improve the responsiveness of I/O requests with no sacrifice of throughput.

/pdf/dynamic-interval-polling-and-pipelined-post-i-o-processing-2jzogyep5l.pdf

Young Jin Yu

Papers

Optimizing the Block I/O Subsystem for Fast Storage Devices

NCQ vs. I/O scheduler: Preventing unexpected misbehaviors

Exploiting peak device throughput from random access workload

Enhancing QoS and Energy Efficiency of Realtime Network Application on Smartphone Using Cloud Computing

Dynamic interval polling and pipelined post I/O processing for low-latency storage class memory