Storage Network Performance Analysis
Author | : Huseyin Simitci |
Publisher | : Wiley |
Total Pages | : 432 |
Release | : 2011-02-11 |
Genre | : Computers |
ISBN | : 9781118081044 |
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Author | : Huseyin Simitci |
Publisher | : Wiley |
Total Pages | : 432 |
Release | : 2011-02-11 |
Genre | : Computers |
ISBN | : 9781118081044 |
Author | : Huseyin Simitci |
Publisher | : John Wiley & Sons |
Total Pages | : 436 |
Release | : 2003-05-09 |
Genre | : Computers |
ISBN | : |
Features vendor-neutral coverage applicable to any storage network Includes a special case-study section citing real-world applications and examples The first vendor-neutral volume to cover storage network performance tuning and optimization Exacting performance monitoring and analysis maximizes the efficiency and cost-effectiveness of existing storage networks Meets the needs of network administrators, storage engineers, and IT professionals faced with shrinking budgets and growing data storage demands
Author | : Nagapramod S. Mandagere |
Publisher | : |
Total Pages | : 108 |
Release | : 2005 |
Genre | : Client/server computing |
ISBN | : |
Author | : Epp,Martin |
Publisher | : KIT Scientific Publishing |
Total Pages | : 246 |
Release | : 2018-02-14 |
Genre | : Logistics |
ISBN | : 3731507420 |
Shuttle-based storage and retrieval systems (SBS/RSs) are an important part of today's warehouses. In this work, a new approach is developed that can be applied to model different configurations of SBS/RSs. The approach is based on the modeling of SBS/RSs as discrete-time open queueing networks and yields the complete probability distributions of the performance measures.
Author | : 帥奇奇 |
Publisher | : |
Total Pages | : 0 |
Release | : 2016 |
Genre | : Electronic data processing |
ISBN | : |
Author | : Berk Atikoǧlu |
Publisher | : |
Total Pages | : |
Release | : 2014 |
Genre | : |
ISBN | : |
Data centers are large groups of networked servers -- typically on the order of tens of thousands of servers -- that power Internet services used by billions of people. As the Internet usage keeps growing and the Internet services get more sophisticated, it is evident that the data center performance will play an even bigger role in users' quality of experience in using these services. Thus, it becomes imperative that data center resources are scaled to cope with the projected increase in Internet services traffic. While data center performance can potentially be optimized along many dimensions, our dissertation focuses on analyzing and optimizing the performance of two key components of a typical data center. The first component is the storage systems, which store the data related to the services such as user state, profiles, accounting and authentication. In addition to maintaining the integrity and privacy of such data, storage systems should be extremely responsive in returning the data when queried by the services hosted by the data center. The second component is the network connecting the servers in the data center with each other. Clearly, the communication performance between the servers should be optimized as well to meet strict delay and bandwidth requirements of the services. However, optimizing the storage and the network involves a number of challenges, which should be effectively addressed to meet the critical performance requirements. This dissertation describes our efforts to address some of these challenges, which we detail below. In the first part of this dissertation, we analyze Facebook's Memcached deployment, which is a distributed memory caching system. By carefully studying this deployment, which is arguably the world's largest, we detail many characteristics of the caching workload and also reveal a number of surprising results: i) we find the GET/SET ratio to be 30:1, which is higher than what is assumed in the literature, ii) some applications of Memcached behave more like persistent storage than a cache, iii) strong locality metrics, such as keys accessed many millions of times a day, do not always suffice for a high hit rate, and iv) there is still room for efficiency and hit rate improvements in Memcached's implementation. We believe that the insights revealed by our analysis are critical to understand the performance of distributed memory caching systems and help design schemes to optimize their deployment. In the second part of the dissertation, we focus on optimizing the network performance along both Layer 2 and Layer 3 of the protocol stack. We first present R2D2, a method for rapid and reliable data delivery in data centers. R2D2 exploits the uniformity of data center network topology and latency to collapse individual Layer 3 flows into one meta-flow. Such state-sharing among multiple flows leads to a very simple and cost-effective method for making the inter-connection fabric reliable. We extensively test a prototype Linux implementation of R2D2 in 1 Gbps and 10 Gbps networks with a variety of switches and under different workloads. We find that it significantly improves TCP performance by preventing timeouts. We deploy R2D2 in a production environment with hundreds of servers and real world traffic and show that R2D2 performs at least as good as existing solutions which are much more expensive to implement than R2D2. Finally, we describe the QCN (Quantized Congestion Notification) algorithm and present a mathematical model for understanding its stability. QCN is a Layer 2 congestion control mechanism, which has been developed for the IEEE 802.1Qau standard (a part of the IEEE Data Center Bridging Task Group's efforts).
Author | : Jun Xu |
Publisher | : Apress |
Total Pages | : 279 |
Release | : 2018-11-16 |
Genre | : Computers |
ISBN | : 1484239288 |
Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO pattern in a quantitative way, identifying the storage system bottleneck, and designing the cache policy). In the new era of IoT, big data, and cloud systems, better performance and higher density of storage systems has become crucial. To increase data storage density, new techniques have evolved and hybrid and parallel access techniques—together with specially designed IO scheduling and data migration algorithms—are being deployed to develop high-performance data storage solutions. Among the various storage system performance analysis techniques, IO event trace analysis (block-level trace analysis particularly) is one of the most common approaches for system optimization and design. However, the task of completing a systematic survey is challenging and very few works on this topic exist. Block Trace Analysis and Storage System Optimization brings together theoretical analysis (such as IO qualitative properties and quantitative metrics) and practical tools (such as trace parsing, analysis, and results reporting perspectives). The book provides content on block-level trace analysis techniques, and includes case studies to illustrate how these techniques and tools can be applied in real applications (such as SSHD, RAID, Hadoop, and Ceph systems). What You’ll Learn Understand the fundamental factors of data storage system performance Master an essential analytical skill using block trace via various applications Distinguish how the IO pattern differs in the block level from the file level Know how the sequential HDFS request becomes “fragmented” in final storage devices Perform trace analysis tasks with a tool based on the MATLAB and Python platforms Who This Book Is For IT professionals interested in storage system performance optimization: network administrators, data storage managers, data storage engineers, storage network engineers, systems engineers
Author | : Jon Tate |
Publisher | : IBM Redbooks |
Total Pages | : 46 |
Release | : 2018-12-04 |
Genre | : Computers |
ISBN | : 0738457108 |
This IBM® Redbooks® Product Guide describes the IBM Storage Networking SAN768C-6. IBM Storage Networking SAN768C-6 has the industry's highest port density for a storage area network (SAN) director and features 768 line-rate 32 gigabits per second (Gbps) or 16 Gbps Fibre Channel ports. Designed to support multiprotocol workloads, IBM Storage Networking SAN768C-6 enables SAN consolidation and collapsed-core solutions for large enterprises, which reduces the number of managed switches and leads to easy-to-manage deployments. IBM Storage Networking SAN768C-6 supports the 48-Port 32 Gbps Fibre Channel Switching Module, the 48-Port 16 Gbps Fibre Channel Switching Module, the 48-port 10 Gbps FCoE Switching Module, the 24-port 40 Gbps FCoE switching module, and the 24/10-port SAN Extension Module. By reducing the number of front-panel ports that are used on inter-switch links (ISLs), it also offers room for future growth. IBM Storage Networking SAN768C-6 addresses the mounting storage requirements of today's large virtualized data centers. As a director-class SAN switch, IBM Storage Networking SAN768C-6 uses the same operating system and management interface as other IBM data center switches. It brings intelligent capabilities to a high-performance, protocol-independent switch fabric, and delivers uncompromising availability, security, scalability, simplified management, and the flexibility to integrate new technologies. You can use IBM Storage Networking SAN768C-6 to transparently deploy unified fabrics with Fibre Channel and Fibre Channel over Ethernet (FCoE) connectivity to achieve low total cost of ownership (TCO). For mission-critical enterprise storage networks that require secure, robust, cost-effective business-continuance services, the FCIP extension module is designed to deliver outstanding SAN extension performance, reducing latency for disk and tape operations with FCIP acceleration features, including FCIP write acceleration and FCIP tape write and read acceleration.
Author | : Clint Huffman |
Publisher | : Elsevier |
Total Pages | : 376 |
Release | : 2014-08-14 |
Genre | : Computers |
ISBN | : 0124167047 |
Microsoft Windows 8.1 and Windows Server 2012 R2 are designed to be the best performing operating systems to date, but even the best systems can be overwhelmed with load and/or plagued with poorly performing code. Windows Performance Analysis Field Guide gives you a practical field guide approach to performance monitoring and analysis from experts who do this work every day. Think of this book as your own guide to "What would Microsoft support do?" when you have a Windows performance issue. Author Clint Huffman, a Microsoft veteran of over fifteen years, shows you how to identify and alleviate problems with the computer resources of disk, memory, processor, and network. You will learn to use performance counters as the initial indicators, then use various tools to "dig in" to the problem, as well as how to capture and analyze boot performance problems. This field guide gives you the tools and answers you need to improve Microsoft Windows performance Save money on optimizing Windows performance with deep technical troubleshooting that tells you "What would Microsoft do to solve this?" Includes performance counter templates so you can collect the right data the first time. Learn how to solve performance problems using free tools from Microsoft such as the Windows Sysinternals tools and more. In a rush? Chapter 1 Start Here gets you on the quick path to solving the problem. Also covers earlier versions such as Windows 7 and Windows Server 2008 R2.
Author | : B. W. Stuck |
Publisher | : Prentice Hall |
Total Pages | : 638 |
Release | : 1985 |
Genre | : Computers |
ISBN | : |