Exploring the scalability of RPC with oslo.messaging

Size: px
Start display at page:

Download "Exploring the scalability of RPC with oslo.messaging"

Transcription

1 Exploring the scalability of RPC with oslo.messaging

2 Rpc client Rpc server Request Rpc client Rpc server Worker Response Worker Messaging infrastructure Rpc client Rpc server Worker Request-response Each request goes to one of the servers Worker includes a client and a server Clients make requests concurrently Clients record throughput and latency Test fails if response not received within 2 seconds of request

3 Disclaimer There are lots of aspects of scale and lots of different use cases or variations that could be explored. This is an initial experiment that I hope provides some food for thought. It most certainly should not be considered comprehensive or conclusive.

4 Code for test:

5 2 machines for servers, both with 4 cores, both running Fedora 19 4 machines for clients, 2 with 12 cores, 2 with 16 cores, all running RHEL7 RabbitMQ Qpidd.28 Qpid Dispatch.2 (with patch) Oslo.messaging from gordon/

6 35 This graph shows how the average rate of requests on the vertical axis - varies as we increase the number of workers for different configurations i.e. As we move right along the horizontal axis RPCs per second per 'worker' AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver The cut-off in each case is the point at which the response time of any request is above 2 seconds

7 The next graph just focuses in on a smaller 'slice' of the horizontal axis... RPCs per second per 'worker' AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver

8 The point at which we start on the vertical axis is the maximum request-response rate and is latency sensitive As we increase the workers, the rate of each client drops off. The rate of degradation and the point at it starts are key measures of scalability. RPCs per second per 'worker' AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver qpid driver with extended timeout

9 35 3 For the two configurations using the AMQP 1. driver, there is minimal degradation until we get to about 2 workers. RPCs per second per 'worker' For the rabbit driver, the degradation is more immediate. AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver qpid driver with extended timeout For the qpid driver, degradation is 5 somewhere in between that for the other two drivers, but we start significantly lower. Why?

10 RPCs per second per 'worker' The configuration these two lines represent are using the exact same broker. The only thing that is different is the driver (and the client library it uses) In fact the cutoff first observed for the qpid driver was due to this extra latency. On the same machine as the broker, with reduced latency but also reduced CPU available, the cutoff happens much later. Increasing the timeout marginally shows a more accurate picture of degradation from a bad starting point. (The same increase doesn't affect the cutoff for the other drivers). AMQP 1. driver with qpidd qpid driver qpid driver with extended timeout RPCs per second per 'worker' The qpid driver has very poor latency due to extra (unnecessary) synchronous roundtrips arising from: (a) creating a sender for every message (and querying the node type in each case) (b) using a synchronous send (both for request and response) AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver qpid driver with extended timeout

11 1 9 8 This graph shows the growth in aggregate request rate i.e. The overall rate of requests through the broker from all the clients together 7 Combined RPCs per second Initially the aggregate rate increases as we add workers. Eventually we reach a point beyond which the rate cannot increase. Additional workers then tends to reduce the overall rate. AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver qpid driver with extended timeout 3 2 The maximum aggregate rate and the point at which it is reached are also key aspects of scalability

12 1 9 Again, we will focus in on a smaller 'slice' of the horizontal axis to better see some of the detail Combined RPCs per second AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver qpid driver with extended timeout

13 1 The configurations using the AMQP 1. driver, increase fairly linearly with the number of workers up to about 2 workers, tailing off after 3 workers or so. The maximum aggregate rate is significantly higher than either of the other two drivers Combined RPCs per second The rabbit driver shows a linear increase in aggregate rate up to about 6 or 7 workers, and flattens out at about 1 workers. AMQP 1. driver with dispatch AMQP 1. driver with qpidd rabbit driver qpid driver qpid driver with extended timeout The qpid driver shows much slower growth than the other drivers,but the increase continues to about 3 workers.

14 What are the limits, and can we get round them?

15 RPCs per second per 'worker' limited number of workers we can support at the maximum rate of requests Combined RPCs per second limited achievable aggregate rate RPCs per second per 'worker' limited number of workers we can support while staying within the defined maximum response time

16 RPCs per second per 'worker' Can we delay the point at which the average rate seen by each worker begins to degrade? Combined RPCs per second Can we keep increasing the aggregate rate? RPCs per second per 'worker' Can we extend the scale at which we can keep within a given maximum timeout?

17 Need to go beyond the limits of a single intermediating process

18 Average RPC's per second, per 'worker' Number of 'workers' Rabbit driver, 1 node cluster Rabbit driver, 2 node cluster AMQP 1. driver, 1 Qpid Dispatch router AMQP 1. driver, 2 Qpid Dispatch routers The point of significant degradation was postponed with the AMQP 1. driver and a Qpid Dispatch Router pair, though the line is not quite flat. With the rabbit driver an A RabbitMQ cluster pair the curve was shifted right a little, but there was no alteration in basic shape.

19 Combined RPCs per second Rabbit driver, 1 node cluster Rabbit driver, 2 node cluster AMQP 1. driver, 1 Qpid Dispatch router AMQP 1. driver, 2 Qpid Dispatch routers Number of 'workers' Combined RPCs per second Combined RPCs per second Number of 'workers' Number of 'workers' AMQP 1. driver, 1 Qpid Dispatch router AMQP 1. driver, 2 Qpid Dispatch routers Extended the maximum achievable aggregate rate both with rabbit driver and clustered RabbitMQ and for AMQP 1. driver and Qpid Dispatch Router network. Rabbit driver, 1 node cluster Rabbit driver, 2 node cluster

20 Average RPC's per second, per 'worker' Number of 'workers' Rabbit driver, 1 node cluster Rabbit driver, 2 node cluster AMQP 1. driver, 1 Qpid Dispatch router AMQP 1. driver, 2 Qpid Dispatch routers Extended the number of workers we can support while staying within the maximum allowed response time both with rabbit driver and clustered RabbitMQ and for AMQP 1. driver and Qpid Dispatch Router network.

OpenStack internal messaging at the edge: In-depth evaluation. Ken Giusti Javier Rojas Balderrama Matthieu Simonin

OpenStack internal messaging at the edge: In-depth evaluation. Ken Giusti Javier Rojas Balderrama Matthieu Simonin OpenStack internal messaging at the edge: In-depth evaluation Ken Giusti Javier Rojas Balderrama Matthieu Simonin Who s here? Ken Giusti Javier Rojas Balderrama Matthieu Simonin Fog Edge and Massively

More information

RabbitMQ: Messaging in the Cloud

RabbitMQ: Messaging in the Cloud ADD-01 RabbitMQ: Messaging in the Cloud Matthew Sackman matthew@rabbitmq.com INTRODUCTION AND PLAN COMING UP IN THE NEXT HOUR... INTRODUCTION AND PLAN COMING UP IN THE NEXT HOUR... Messaging, messaging,

More information

Interactive Responsiveness and Concurrent Workflow

Interactive Responsiveness and Concurrent Workflow Middleware-Enhanced Concurrency of Transactions Interactive Responsiveness and Concurrent Workflow Transactional Cascade Technology Paper Ivan Klianev, Managing Director & CTO Published in November 2005

More information

Database Architectures

Database Architectures Database Architectures CPS352: Database Systems Simon Miner Gordon College Last Revised: 11/15/12 Agenda Check-in Centralized and Client-Server Models Parallelism Distributed Databases Homework 6 Check-in

More information

Architecture and terminology

Architecture and terminology Architecture and terminology Guy Carmin RHCE, RHCI, RHCVA, RHCSA Solution Architect IGC, Red Hat Roei Goldenberg RHCE Linux Consultant and Cloud expert, Matrix May 2015 Agenda RHEL-OSP services modules

More information

Storm. Distributed and fault-tolerant realtime computation. Nathan Marz Twitter

Storm. Distributed and fault-tolerant realtime computation. Nathan Marz Twitter Storm Distributed and fault-tolerant realtime computation Nathan Marz Twitter Basic info Open sourced September 19th Implementation is 15,000 lines of code Used by over 25 companies >2700 watchers on Github

More information

Congestion Control in TCP

Congestion Control in TCP Congestion Control in TCP Antonio Carzaniga Faculty of Informatics University of Lugano May 6, 2005 Outline Intro to congestion control Input rate vs. output throughput Congestion window Congestion avoidance

More information

Next-Generation AMQP Messaging Performance, Architectures, and Ecosystems with Red Hat Enterprise MRG. Bryan Che MRG Product Manager Red Hat, Inc.

Next-Generation AMQP Messaging Performance, Architectures, and Ecosystems with Red Hat Enterprise MRG. Bryan Che MRG Product Manager Red Hat, Inc. Next-Generation AMQP Messaging Performance, Architectures, and Ecosystems with Red Hat Enterprise MRG Bryan Che MRG Product Manager Red Hat, Inc. Carl Trieloff Senior Consulting Software Engineer/ Director

More information

CLUSTERING HIVEMQ. Building highly available, horizontally scalable MQTT Broker Clusters

CLUSTERING HIVEMQ. Building highly available, horizontally scalable MQTT Broker Clusters CLUSTERING HIVEMQ Building highly available, horizontally scalable MQTT Broker Clusters 12/2016 About this document MQTT is based on a publish/subscribe architecture that decouples MQTT clients and uses

More information

OS and Hardware Tuning

OS and Hardware Tuning OS and Hardware Tuning Tuning Considerations OS Threads Thread Switching Priorities Virtual Memory DB buffer size File System Disk layout and access Hardware Storage subsystem Configuring the disk array

More information

OS and HW Tuning Considerations!

OS and HW Tuning Considerations! Administração e Optimização de Bases de Dados 2012/2013 Hardware and OS Tuning Bruno Martins DEI@Técnico e DMIR@INESC-ID OS and HW Tuning Considerations OS " Threads Thread Switching Priorities " Virtual

More information

Congestion Control in Datacenters. Ahmed Saeed

Congestion Control in Datacenters. Ahmed Saeed Congestion Control in Datacenters Ahmed Saeed What is a Datacenter? Tens of thousands of machines in the same building (or adjacent buildings) Hundreds of switches connecting all machines What is a Datacenter?

More information

Multiprocessor Support

Multiprocessor Support CSC 256/456: Operating Systems Multiprocessor Support John Criswell University of Rochester 1 Outline Multiprocessor hardware Types of multi-processor workloads Operating system issues Where to run the

More information

Process Scheduling. Copyright : University of Illinois CS 241 Staff

Process Scheduling. Copyright : University of Illinois CS 241 Staff Process Scheduling Copyright : University of Illinois CS 241 Staff 1 Process Scheduling Deciding which process/thread should occupy the resource (CPU, disk, etc) CPU I want to play Whose turn is it? Process

More information

MRG - AMQP trading system in a rack. Carl Trieloff Senior Consulting Software Engineer/ Director MRG Red Hat, Inc.

MRG - AMQP trading system in a rack. Carl Trieloff Senior Consulting Software Engineer/ Director MRG Red Hat, Inc. MRG - AMQP trading system in a rack Carl Trieloff Senior Consulting Software Engineer/ Director MRG Red Hat, Inc. Trading system in a rack... We will cover a generic use case of a trading system in a rack,

More information

Congestion Control in TCP

Congestion Control in TCP Congestion Control in TCP Antonio Carzaniga Faculty of Informatics University of Lugano November 11, 2014 Outline Intro to congestion control Input rate vs. output throughput Congestion window Congestion

More information

Graphical Analysis. Figure 1. Copyright c 1997 by Awi Federgruen. All rights reserved.

Graphical Analysis. Figure 1. Copyright c 1997 by Awi Federgruen. All rights reserved. Graphical Analysis For problems with 2 variables, we can represent each solution as a point in the plane. The Shelby Shelving model (see the readings book or pp.68-69 of the text) is repeated below for

More information

Time and Space. Indirect communication. Time and space uncoupling. indirect communication

Time and Space. Indirect communication. Time and space uncoupling. indirect communication Time and Space Indirect communication Johan Montelius In direct communication sender and receivers exist in the same time and know of each other. KTH In indirect communication we relax these requirements.

More information

Computer Networks Project 4. By Eric Wasserman and Ji Hoon Baik

Computer Networks Project 4. By Eric Wasserman and Ji Hoon Baik Computer Networks Project 4 By Eric Wasserman and Ji Hoon Baik Modifications to the Code, and the Flowcharts UDP transmission is different from TCP transmission in that: 1. UDP transmission is unidirectional;

More information

Storm. Distributed and fault-tolerant realtime computation. Nathan Marz Twitter

Storm. Distributed and fault-tolerant realtime computation. Nathan Marz Twitter Storm Distributed and fault-tolerant realtime computation Nathan Marz Twitter Storm at Twitter Twitter Web Analytics Before Storm Queues Workers Example (simplified) Example Workers schemify tweets and

More information

DATABASE SCALE WITHOUT LIMITS ON AWS

DATABASE SCALE WITHOUT LIMITS ON AWS The move to cloud computing is changing the face of the computer industry, and at the heart of this change is elastic computing. Modern applications now have diverse and demanding requirements that leverage

More information

Sandor Heman, Niels Nes, Peter Boncz. Dynamic Bandwidth Sharing. Cooperative Scans: Marcin Zukowski. CWI, Amsterdam VLDB 2007.

Sandor Heman, Niels Nes, Peter Boncz. Dynamic Bandwidth Sharing. Cooperative Scans: Marcin Zukowski. CWI, Amsterdam VLDB 2007. Cooperative Scans: Dynamic Bandwidth Sharing in a DBMS Marcin Zukowski Sandor Heman, Niels Nes, Peter Boncz CWI, Amsterdam VLDB 2007 Outline Scans in a DBMS Cooperative Scans Benchmarks DSM version VLDB,

More information

Distributed Systems. Characteristics of Distributed Systems. Lecture Notes 1 Basic Concepts. Operating Systems. Anand Tripathi

Distributed Systems. Characteristics of Distributed Systems. Lecture Notes 1 Basic Concepts. Operating Systems. Anand Tripathi 1 Lecture Notes 1 Basic Concepts Anand Tripathi CSci 8980 Operating Systems Anand Tripathi CSci 8980 1 Distributed Systems A set of computers (hosts or nodes) connected through a communication network.

More information

Distributed Systems. Characteristics of Distributed Systems. Characteristics of Distributed Systems. Goals in Distributed System Designs

Distributed Systems. Characteristics of Distributed Systems. Characteristics of Distributed Systems. Goals in Distributed System Designs 1 Anand Tripathi CSci 8980 Operating Systems Lecture Notes 1 Basic Concepts Distributed Systems A set of computers (hosts or nodes) connected through a communication network. Nodes may have different speeds

More information

On BigFix Performance: Disk is King. How to get your infrastructure right the first time! Case Study: IBM Cloud Development - WW IT Services

On BigFix Performance: Disk is King. How to get your infrastructure right the first time! Case Study: IBM Cloud Development - WW IT Services On BigFix Performance: Disk is King How to get your infrastructure right the first time! Case Study: IBM Cloud Development - WW IT Services Authors: Shaun T. Kelley, Mark Leitch Abstract: Rolling out large

More information

Produced by. Design Patterns. MSc Computer Science. Eamonn de Leastar

Produced by. Design Patterns. MSc Computer Science. Eamonn de Leastar Design Patterns MSc Computer Science Produced by Eamonn de Leastar (edeleastar@wit.ie)! Department of Computing, Maths & Physics Waterford Institute of Technology http://www.wit.ie http://elearning.wit.ie

More information

Cluster-Based Scalable Network Services

Cluster-Based Scalable Network Services Cluster-Based Scalable Network Services Suhas Uppalapati INFT 803 Oct 05 1999 (Source : Fox, Gribble, Chawathe, and Brewer, SOSP, 1997) Requirements for SNS Incremental scalability and overflow growth

More information

Congestion Control in TCP

Congestion Control in TCP Congestion Control in CP Antonio Carzaniga Faculty of Informatics University of Lugano Apr 7, 2008 2005 2007 Antonio Carzaniga 1 Intro to congestion control Outline Input rate vs. output throughput Congestion

More information

UV Mapping to avoid texture flaws and enable proper shading

UV Mapping to avoid texture flaws and enable proper shading UV Mapping to avoid texture flaws and enable proper shading Foreword: Throughout this tutorial I am going to be using Maya s built in UV Mapping utility, which I am going to base my projections on individual

More information

Web Serving Architectures

Web Serving Architectures Web Serving Architectures Paul Dantzig IBM Global Services 2000 without the express written consent of the IBM Corporation is prohibited Contents Defining the Problem e-business Solutions e-business Architectures

More information

JVM and application bottlenecks troubleshooting

JVM and application bottlenecks troubleshooting JVM and application bottlenecks troubleshooting How to find problems without using sophisticated tools Daniel Witkowski, EMEA Technical Manager, Azul Systems Daniel Witkowski - About me IT consultant and

More information

Future-ready IT Systems with Performance Prediction using Analytical Models

Future-ready IT Systems with Performance Prediction using Analytical Models Future-ready IT Systems with Performance Prediction using Analytical Models Madhu Tanikella Infosys Abstract Large and complex distributed software systems can impact overall software cost and risk for

More information

VMware vcloud Director Configuration Maximums vcloud Director 9.1 and 9.5 October 2018

VMware vcloud Director Configuration Maximums vcloud Director 9.1 and 9.5 October 2018 VMware vcloud Director Configuration Maximums vcloud Director 9.1 and 9.5 October 2018 This document supports the version of each product listed and supports all subsequent versions until the document

More information

Indirect Communication

Indirect Communication Indirect Communication Vladimir Vlassov and Johan Montelius KTH ROYAL INSTITUTE OF TECHNOLOGY Time and Space In direct communication sender and receivers exist in the same time and know of each other.

More information

Database Architectures

Database Architectures Database Architectures CPS352: Database Systems Simon Miner Gordon College Last Revised: 4/15/15 Agenda Check-in Parallelism and Distributed Databases Technology Research Project Introduction to NoSQL

More information

A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols. Broch et al Presented by Brian Card

A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols. Broch et al Presented by Brian Card A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols Broch et al Presented by Brian Card 1 Outline Introduction NS enhancements Protocols: DSDV TORA DRS AODV Evaluation Conclusions

More information

CS 5520/ECE 5590NA: Network Architecture I Spring Lecture 13: UDP and TCP

CS 5520/ECE 5590NA: Network Architecture I Spring Lecture 13: UDP and TCP CS 5520/ECE 5590NA: Network Architecture I Spring 2008 Lecture 13: UDP and TCP Most recent lectures discussed mechanisms to make better use of the IP address space, Internet control messages, and layering

More information

B.H.GARDI COLLEGE OF ENGINEERING & TECHNOLOGY (MCA Dept.) Parallel Database Database Management System - 2

B.H.GARDI COLLEGE OF ENGINEERING & TECHNOLOGY (MCA Dept.) Parallel Database Database Management System - 2 Introduction :- Today single CPU based architecture is not capable enough for the modern database that are required to handle more demanding and complex requirements of the users, for example, high performance,

More information

Microservice-Based Agile Architectures:

Microservice-Based Agile Architectures: Microservice-Based Agile Architectures: An Opportunity for Specialized Niche Technologies Stefano Munari, Sebastiano Valle, Tullio Vardanega University of Padua, Department of Mathematics Ada-Europe 2018

More information

An Implementation of the Homa Transport Protocol in RAMCloud. Yilong Li, Behnam Montazeri, John Ousterhout

An Implementation of the Homa Transport Protocol in RAMCloud. Yilong Li, Behnam Montazeri, John Ousterhout An Implementation of the Homa Transport Protocol in RAMCloud Yilong Li, Behnam Montazeri, John Ousterhout Introduction Homa: receiver-driven low-latency transport protocol using network priorities HomaTransport

More information

Data Mining, Parallelism, Data Mining, Parallelism, and Grids. Queen s University, Kingston David Skillicorn

Data Mining, Parallelism, Data Mining, Parallelism, and Grids. Queen s University, Kingston David Skillicorn Data Mining, Parallelism, Data Mining, Parallelism, and Grids David Skillicorn Queen s University, Kingston skill@cs.queensu.ca Data mining builds models from data in the hope that these models reveal

More information

Chapter 2: Understanding Data Distributions with Tables and Graphs

Chapter 2: Understanding Data Distributions with Tables and Graphs Test Bank Chapter 2: Understanding Data with Tables and Graphs Multiple Choice 1. Which of the following would best depict nominal level data? a. pie chart b. line graph c. histogram d. polygon Ans: A

More information

Distributed Systems Exam 1 Review Paul Krzyzanowski. Rutgers University. Fall 2016

Distributed Systems Exam 1 Review Paul Krzyzanowski. Rutgers University. Fall 2016 Distributed Systems 2015 Exam 1 Review Paul Krzyzanowski Rutgers University Fall 2016 1 Question 1 Why did the use of reference counting for remote objects prove to be impractical? Explain. It s not fault

More information

Listening with TSDE (Transport Segment Delay Estimator) Kathleen Nichols Pollere, Inc.

Listening with TSDE (Transport Segment Delay Estimator) Kathleen Nichols Pollere, Inc. Listening with TSDE (Transport Segment Delay Estimator) Kathleen Nichols Pollere, Inc. Basic Information Pollere has been working on TSDE under an SBIR grant from the Department of Energy. In the process

More information

Introduction to Internet of Things Prof. Sudip Misra Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur

Introduction to Internet of Things Prof. Sudip Misra Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Introduction to Internet of Things Prof. Sudip Misra Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture - 08 Basics of IoT Networking- Part- IV So, we continue

More information

ECEN Final Exam Fall Instructor: Srinivas Shakkottai

ECEN Final Exam Fall Instructor: Srinivas Shakkottai ECEN 424 - Final Exam Fall 2013 Instructor: Srinivas Shakkottai NAME: Problem maximum points your points Problem 1 10 Problem 2 10 Problem 3 20 Problem 4 20 Problem 5 20 Problem 6 20 total 100 1 2 Midterm

More information

Networking Strategy and Optimization Services (NSOS) 2010 IBM Corporation

Networking Strategy and Optimization Services (NSOS) 2010 IBM Corporation Networking Strategy and Optimization Services (NSOS) Agenda Network Strategy and Optimization Services (NSOS) Overview IBM NSOS NAO Offerings Model IBM NSOS NIO Offerings Model Why IBM Lot of specialist

More information

Building High Performance Apps using NoSQL. Swami Sivasubramanian General Manager, AWS NoSQL

Building High Performance Apps using NoSQL. Swami Sivasubramanian General Manager, AWS NoSQL Building High Performance Apps using NoSQL Swami Sivasubramanian General Manager, AWS NoSQL Building high performance apps There is a lot to building high performance apps Scalability Performance at high

More information

Efficient On-Demand Operations in Distributed Infrastructures

Efficient On-Demand Operations in Distributed Infrastructures Efficient On-Demand Operations in Distributed Infrastructures Steve Ko and Indranil Gupta Distributed Protocols Research Group University of Illinois at Urbana-Champaign 2 One-Line Summary We need to design

More information

Cloud Scale IoT Messaging

Cloud Scale IoT Messaging Cloud Scale IoT Messaging EclipseCon France 2018 Dejan Bosanac, Red Hat Jens Reimann, Red Hat IoT : communication patterns Cloud Telemetry 2 Inquiries Commands Notifications optimized for throughput scale-out

More information

CS 856 Latency in Communication Systems

CS 856 Latency in Communication Systems CS 856 Latency in Communication Systems Winter 2010 Latency Challenges CS 856, Winter 2010, Latency Challenges 1 Overview Sources of Latency low-level mechanisms services Application Requirements Latency

More information

Profile of CopperEye Indexing Technology. A CopperEye Technical White Paper

Profile of CopperEye Indexing Technology. A CopperEye Technical White Paper Profile of CopperEye Indexing Technology A CopperEye Technical White Paper September 2004 Introduction CopperEye s has developed a new general-purpose data indexing technology that out-performs conventional

More information

Performance Evaluation of Virtualization Technologies

Performance Evaluation of Virtualization Technologies Performance Evaluation of Virtualization Technologies Saad Arif Dept. of Electrical Engineering and Computer Science University of Central Florida - Orlando, FL September 19, 2013 1 Introduction 1 Introduction

More information

File System Performance (and Abstractions) Kevin Webb Swarthmore College April 5, 2018

File System Performance (and Abstractions) Kevin Webb Swarthmore College April 5, 2018 File System Performance (and Abstractions) Kevin Webb Swarthmore College April 5, 2018 Today s Goals Supporting multiple file systems in one name space. Schedulers not just for CPUs, but disks too! Caching

More information

Congestion control in TCP

Congestion control in TCP Congestion control in TCP If the transport entities on many machines send too many packets into the network too quickly, the network will become congested, with performance degraded as packets are delayed

More information

Distributing OpenStack on top of a Key/Value store

Distributing OpenStack on top of a Key/Value store Distributing OpenStack on top of a Key/Value store Jonathan Pastor (Inria, Ascola, Ecole des Mines de Nantes) PhD candidate, under the supervision of Adrien Lebre and Frédéric Desprez Journée Cloud 2015

More information

TCP Strategies. Keepalive Timer. implementations do not have it as it is occasionally regarded as controversial. between source and destination

TCP Strategies. Keepalive Timer. implementations do not have it as it is occasionally regarded as controversial. between source and destination Keepalive Timer! Yet another timer in TCP is the keepalive! This one is not required, and some implementations do not have it as it is occasionally regarded as controversial! When a TCP connection is idle

More information

Chapter 17: Parallel Databases

Chapter 17: Parallel Databases Chapter 17: Parallel Databases Introduction I/O Parallelism Interquery Parallelism Intraquery Parallelism Intraoperation Parallelism Interoperation Parallelism Design of Parallel Systems Database Systems

More information

Lixia Zhang M. I. T. Laboratory for Computer Science December 1985

Lixia Zhang M. I. T. Laboratory for Computer Science December 1985 Network Working Group Request for Comments: 969 David D. Clark Mark L. Lambert Lixia Zhang M. I. T. Laboratory for Computer Science December 1985 1. STATUS OF THIS MEMO This RFC suggests a proposed protocol

More information

Graphs of Exponential

Graphs of Exponential Graphs of Exponential Functions By: OpenStaxCollege As we discussed in the previous section, exponential functions are used for many realworld applications such as finance, forensics, computer science,

More information

Contents Overview of the Performance and Sizing Guide... 5 Architecture Overview... 7 Performance and Scalability Considerations...

Contents Overview of the Performance and Sizing Guide... 5 Architecture Overview... 7 Performance and Scalability Considerations... Unifier Performance and Sizing Guide for On-Premises Version 17 July 2017 Contents Overview of the Performance and Sizing Guide... 5 Architecture Overview... 7 Performance and Scalability Considerations...

More information

Scalable Enterprise Networks with Inexpensive Switches

Scalable Enterprise Networks with Inexpensive Switches Scalable Enterprise Networks with Inexpensive Switches Minlan Yu minlanyu@cs.princeton.edu Princeton University Joint work with Alex Fabrikant, Mike Freedman, Jennifer Rexford and Jia Wang 1 Enterprises

More information

Studying Fairness of TCP Variants and UDP Traffic

Studying Fairness of TCP Variants and UDP Traffic Studying Fairness of TCP Variants and UDP Traffic Election Reddy B.Krishna Chaitanya Problem Definition: To study the fairness of TCP variants and UDP, when sharing a common link. To do so we conduct various

More information

Outline. Internet. Router. Network Model. Internet Protocol (IP) Design Principles

Outline. Internet. Router. Network Model. Internet Protocol (IP) Design Principles Outline Internet model Design principles Internet Protocol (IP) Transmission Control Protocol (TCP) Tze Sing Eugene Ng Department of Computer Science Carnegie Mellon University Tze Sing Eugene Ng eugeneng@cs.cmu.edu

More information

3.7. Vertex and tangent

3.7. Vertex and tangent 3.7. Vertex and tangent Example 1. At the right we have drawn the graph of the cubic polynomial f(x) = x 2 (3 x). Notice how the structure of the graph matches the form of the algebraic expression. The

More information

COSC243 Part 2: Operating Systems

COSC243 Part 2: Operating Systems COSC243 Part 2: Operating Systems Lecture 17: CPU Scheduling Zhiyi Huang Dept. of Computer Science, University of Otago Zhiyi Huang (Otago) COSC243 Lecture 17 1 / 30 Overview Last lecture: Cooperating

More information

Introduction to Ethernet Latency

Introduction to Ethernet Latency Introduction to Ethernet Latency An Explanation of Latency and Latency Measurement The primary difference in the various methods of latency measurement is the point in the software stack at which the latency

More information

Most real programs operate somewhere between task and data parallelism. Our solution also lies in this set.

Most real programs operate somewhere between task and data parallelism. Our solution also lies in this set. for Windows Azure and HPC Cluster 1. Introduction In parallel computing systems computations are executed simultaneously, wholly or in part. This approach is based on the partitioning of a big task into

More information

Network Processors and their memory

Network Processors and their memory Network Processors and their memory Network Processor Workshop, Madrid 2004 Nick McKeown Departments of Electrical Engineering and Computer Science, Stanford University nickm@stanford.edu http://www.stanford.edu/~nickm

More information

Short-Cut MCMC: An Alternative to Adaptation

Short-Cut MCMC: An Alternative to Adaptation Short-Cut MCMC: An Alternative to Adaptation Radford M. Neal Dept. of Statistics and Dept. of Computer Science University of Toronto http://www.cs.utoronto.ca/ radford/ Third Workshop on Monte Carlo Methods,

More information

How Much Logic Should Go in an FPGA Logic Block?

How Much Logic Should Go in an FPGA Logic Block? How Much Logic Should Go in an FPGA Logic Block? Vaughn Betz and Jonathan Rose Department of Electrical and Computer Engineering, University of Toronto Toronto, Ontario, Canada M5S 3G4 {vaughn, jayar}@eecgutorontoca

More information

QoS on Low Bandwidth High Delay Links. Prakash Shende Planning & Engg. Team Data Network Reliance Infocomm

QoS on Low Bandwidth High Delay Links. Prakash Shende Planning & Engg. Team Data Network Reliance Infocomm QoS on Low Bandwidth High Delay Links Prakash Shende Planning & Engg. Team Data Network Reliance Infocomm Agenda QoS Some Basics What are the characteristics of High Delay Low Bandwidth link What factors

More information

Lightstreamer. The Streaming-Ajax Revolution. Product Insight

Lightstreamer. The Streaming-Ajax Revolution. Product Insight Lightstreamer The Streaming-Ajax Revolution Product Insight 1 Agenda Paradigms for the Real-Time Web (four models explained) Requirements for a Good Comet Solution Introduction to Lightstreamer Lightstreamer

More information

Lecture 24: Board Notes: Cache Coherency

Lecture 24: Board Notes: Cache Coherency Lecture 24: Board Notes: Cache Coherency Part A: What makes a memory system coherent? Generally, 3 qualities that must be preserved (SUGGESTIONS?) (1) Preserve program order: - A read of A by P 1 will

More information

Instant Integration into the AMQP Cloud with Apache Qpid Messenger. Rafael Schloming Principle Software Red Hat

Instant Integration into the AMQP Cloud with Apache Qpid Messenger. Rafael Schloming Principle Software Red Hat Instant Integration into the AMQP Cloud with Apache Qpid Messenger Rafael Schloming Principle Software Engineer @ Red Hat rhs@apache.org Overview Introduction Messaging AMQP Proton Demo Summary Introduction

More information

Distributed Data Infrastructures, Fall 2017, Chapter 2. Jussi Kangasharju

Distributed Data Infrastructures, Fall 2017, Chapter 2. Jussi Kangasharju Distributed Data Infrastructures, Fall 2017, Chapter 2 Jussi Kangasharju Chapter Outline Warehouse-scale computing overview Workloads and software infrastructure Failures and repairs Note: Term Warehouse-scale

More information

Parallel Programming Principle and Practice. Lecture 10 Big Data Processing with MapReduce

Parallel Programming Principle and Practice. Lecture 10 Big Data Processing with MapReduce Parallel Programming Principle and Practice Lecture 10 Big Data Processing with MapReduce Outline MapReduce Programming Model MapReduce Examples Hadoop 2 Incredible Things That Happen Every Minute On The

More information

Ms Nurazrin Jupri. Frequency Distributions

Ms Nurazrin Jupri. Frequency Distributions Frequency Distributions Frequency Distributions After collecting data, the first task for a researcher is to organize and simplify the data so that it is possible to get a general overview of the results.

More information

Performance Benchmarking an Enterprise Message Bus. Anurag Sharma Pramod Sharma Sumant Vashisth

Performance Benchmarking an Enterprise Message Bus. Anurag Sharma Pramod Sharma Sumant Vashisth Performance Benchmarking an Enterprise Message Bus Anurag Sharma Pramod Sharma Sumant Vashisth About the Authors Sumant Vashisth is Director of Engineering, Security Management Business Unit at McAfee.

More information

Impact of transmission errors on TCP performance. Outline. Random Errors

Impact of transmission errors on TCP performance. Outline. Random Errors Impact of transmission errors on TCP performance 1 Outline Impact of transmission errors on TCP performance Approaches to improve TCP performance Classification Discussion of selected approaches 2 Random

More information

A Performance Study of Locking Granularity in Shared-Nothing Parallel Database Systems

A Performance Study of Locking Granularity in Shared-Nothing Parallel Database Systems A Performance Study of Locking Granularity in Shared-Nothing Parallel Database Systems S. Dandamudi, S. L. Au, and C. Y. Chow School of Computer Science, Carleton University Ottawa, Ontario K1S 5B6, Canada

More information

EqualLogic Storage and Non-Stacking Switches. Sizing and Configuration

EqualLogic Storage and Non-Stacking Switches. Sizing and Configuration EqualLogic Storage and Non-Stacking Switches Sizing and Configuration THIS WHITE PAPER IS FOR INFORMATIONAL PURPOSES ONLY, AND MAY CONTAIN TYPOGRAPHICAL ERRORS AND TECHNICAL INACCURACIES. THE CONTENT IS

More information

COSC 311: ALGORITHMS HW1: SORTING

COSC 311: ALGORITHMS HW1: SORTING COSC 311: ALGORITHMS HW1: SORTIG Solutions 1) Theoretical predictions. Solution: On randomly ordered data, we expect the following ordering: Heapsort = Mergesort = Quicksort (deterministic or randomized)

More information

Deploying VSaaS and Hosted Solutions using CompleteView

Deploying VSaaS and Hosted Solutions using CompleteView SALIENT SYSTEMS WHITE PAPER Deploying VSaaS and Hosted Solutions using CompleteView Understanding the benefits of CompleteView for hosted solutions and successful deployment architectures. Salient Systems

More information

NIC TEAMING IEEE 802.3ad

NIC TEAMING IEEE 802.3ad WHITE PAPER NIC TEAMING IEEE 802.3ad NIC Teaming IEEE 802.3ad Summary This tech note describes the NIC (Network Interface Card) teaming capabilities of VMware ESX Server 2 including its benefits, performance

More information

2 Test Setup Server Details Database Client Details Details of test plan... 5

2 Test Setup Server Details Database Client Details Details of test plan... 5 Contents 1 Introduction 3 2 Test Setup 4 2.1 Server Details.................................. 4 2.1.1 Database................................ 4 2.2 Client Details.................................. 5 2.3

More information

Corda Performance To infinity and beyond! James Carlyle Chief Engineer, R3 7 March 2018

Corda Performance To infinity and beyond! James Carlyle Chief Engineer, R3 7 March 2018 Corda Performance To infinity and beyond! James Carlyle Chief Engineer, R3 7 March 2018 Performance Matters! Why does it matter? Ultimate capacity of network Efficiency and costs of infrastructure But.

More information

CS-736 Midterm: Beyond Compare (Spring 2008)

CS-736 Midterm: Beyond Compare (Spring 2008) CS-736 Midterm: Beyond Compare (Spring 2008) An Arpaci-Dusseau Exam Please Read All Questions Carefully! There are eight (8) total numbered pages Please put your NAME ONLY on this page, and your STUDENT

More information

Diagnosing the cause of poor application performance

Diagnosing the cause of poor application performance Diagnosing the cause of poor application performance When it comes to troubleshooting application performance issues, there are two steps you can take to make diagnosis easier, faster and more accurate.

More information

z/os Heuristic Conversion of CF Operations from Synchronous to Asynchronous Execution (for z/os 1.2 and higher) V2

z/os Heuristic Conversion of CF Operations from Synchronous to Asynchronous Execution (for z/os 1.2 and higher) V2 z/os Heuristic Conversion of CF Operations from Synchronous to Asynchronous Execution (for z/os 1.2 and higher) V2 z/os 1.2 introduced a new heuristic for determining whether it is more efficient in terms

More information

Routing Metric. ARPANET Routing Algorithms. Problem with D-SPF. Advanced Computer Networks

Routing Metric. ARPANET Routing Algorithms. Problem with D-SPF. Advanced Computer Networks Advanced Computer Networks Khanna and Zinky, The Revised ARPANET Routing Metric, Proc. of ACM SIGCOMM '89, 19(4):45 46, Sep. 1989 Routing Metric Distributed route computation is a function of link cost

More information

Distributed Systems 27. Process Migration & Allocation

Distributed Systems 27. Process Migration & Allocation Distributed Systems 27. Process Migration & Allocation Paul Krzyzanowski pxk@cs.rutgers.edu 12/16/2011 1 Processor allocation Easy with multiprocessor systems Every processor has access to the same memory

More information

Low Latency Data Grids in Finance

Low Latency Data Grids in Finance Low Latency Data Grids in Finance Jags Ramnarayan Chief Architect GemStone Systems jags.ramnarayan@gemstone.com Copyright 2006, GemStone Systems Inc. All Rights Reserved. Background on GemStone Systems

More information

1588v2 Performance Validation for Mobile Backhaul May Executive Summary. Case Study

1588v2 Performance Validation for Mobile Backhaul May Executive Summary. Case Study Case Study 1588v2 Performance Validation for Mobile Backhaul May 2011 Executive Summary Many mobile operators are actively transforming their backhaul networks to a cost-effective IP-over- Ethernet paradigm.

More information

New features in JustCGM 5.1

New features in JustCGM 5.1 New features in JustCGM 5.1 This document gives a general overview of the most important new features within JustCGM 5.1. The major enhancements are there is now a new Export to PowerPoint module layers

More information

Healthcare IT A Monitoring Primer

Healthcare IT A Monitoring Primer Healthcare IT A Monitoring Primer Published: February 2019 PAGE 1 OF 13 Contents Introduction... 3 The Healthcare IT Environment.... 4 Traditional IT... 4 Healthcare Systems.... 4 Healthcare Data Format

More information

IBM BigFix Lifecycle 9.5

IBM BigFix Lifecycle 9.5 Software Product Compatibility Reports Product IBM BigFix Lifecycle 9.5 Contents Included in this report Operating systems (Section intentionally removed by the report author) Hypervisors (Section intentionally

More information

CIS 632 / EEC 687 Mobile Computing

CIS 632 / EEC 687 Mobile Computing CIS 632 / EEC 687 Mobile Computing TCP in Mobile Networks Prof. Chansu Yu Contents Physical layer issues Communication frequency Signal propagation Modulation and Demodulation Channel access issues Multiple

More information

Can "scale" cloud applications "on the edge" by adding server instances. (So far, haven't considered scaling the interior of the cloud).

Can scale cloud applications on the edge by adding server instances. (So far, haven't considered scaling the interior of the cloud). Recall: where we are Wednesday, February 17, 2010 11:12 AM Recall: where we are Can "scale" cloud applications "on the edge" by adding server instances. (So far, haven't considered scaling the interior

More information

How To Construct A Keyword Strategy?

How To Construct A Keyword Strategy? Introduction The moment you think about marketing these days the first thing that pops up in your mind is to go online. Why is there a heck about marketing your business online? Why is it so drastically

More information