An Integrated Experimental Environment for Distributed Systems and Networks B. White, J. Lepreau, L. Stoller, R. Ricci, S. Guruprasad, M. Newbold, M. Hibler,

Slides:



Advertisements
Similar presentations
Ethernet Switch Features Important to EtherNet/IP
Advertisements

A Novel 3D Layer-Multiplexed On-Chip Network
CloudStack Scalability Testing, Development, Results, and Futures Anthony Xu Apache CloudStack contributor.
High Performance Cluster Computing Architectures and Systems Hai Jin Internet and Cluster Computing Center.
Design Deployment and Use of the DETER Testbed Terry Benzel, Robert Braden, Dongho Kim, Clifford Informatino Sciences Institute
Virtualization in HPC Minesh Joshi CSC 469 Dr. Box Feb 1, 2012.
Bilal Gonen University of Alaska Anchorage Murat Yuksel University of Nevada, Reno.
Using DSVM to Implement a Distributed File System Ramon Lawrence Dept. of Computer Science
Transparent Checkpoint of Closed Distributed Systems in Emulab Anton Burtsev, Prashanth Radhakrishnan, Mike Hibler, and Jay Lepreau University of Utah,
Lecture 8: Testbeds Anish Arora CIS788.11J Introduction to Wireless Sensor Networks Material uses slides from Larry Peterson, Jay Lapreau, and GENI.net.
1 Modeling and Emulation of Internet Paths Pramod Sanaga, Jonathon Duerig, Robert Ricci, Jay Lepreau University of Utah.
Emulab.net: An Emulation Testbed for Networks and Distributed Systems Jay Lepreau and many others University of Utah Intel IXA University Workshop June.
Katz, Stoica F04 EECS 122 Introduction to Computer Networks (Fall 2003) Network simulator 2 (ns-2) Department of Electrical Engineering and Computer Sciences.
Contiki A Lightweight and Flexible Operating System for Tiny Networked Sensors Presented by: Jeremy Schiff.
Lowering the Barrier to Wireless and Mobile Experimentation Brian White, Jay Lepreau, Shashi Guruprasad University of Utah HotNets-I October.
1 Cluster or Network? An Emulation Facility for Research Jay Lepreau Chris Alfeld David Andersen (MIT) Mac Newbold Rob Place Kristin Wright Dept. of Computer.
1 Fast, Scalable Disk Imaging with Frisbee University of Utah Mike Hibler, Leigh Stoller, Jay Lepreau, Robert Ricci, Chad Barb.
Integrated Scientific Workflow Management for the Emulab Network Testbed Eric Eide, Leigh Stoller, Tim Stack, Juliana Freire, and Jay Lepreau and Jay Lepreau.
Scalability and Accuracy in a Large- Scale Network Emulator Amin Vahdat, Ken Yocum, Kevin Walsh, Priya Mahadevan, Dejan Kostic, Jeff Chase, and David Becker.
Student Projects in Computer Networking: Simulation versus Coding Leann M. Christianson Kevin A. Brown Cal State East Bay.
1 A Large-Scale Network Testbed Jay Lepreau Chris Alfeld David Andersen Kevin Van Maren University of Utah September.
An Integrated Experimental Environment for Distributed Systems and Networks B. White, J. Lepreau, L. Stoller, R. Ricci, S. Guruprasad, M. Newbold, M. Hibler,
How To Use It...  Submit ns script via web form  Relax while emulab …  Generates config from script & stores in DB  Maps specified virtual topology.
Emulab Federation Preliminary Design Robert Ricci with Jay Lepreau, Leigh Stoller, Mike Hibler University of Utah USC/ISI Federation Workshop December.
1 Exploring Data Reliability Tradeoffs in Replicated Storage Systems NetSysLab The University of British Columbia Abdullah Gharaibeh Matei Ripeanu.
Emulab.net Current and Future: An Emulation Testbed for Networks and Distributed Systems Jay Lepreau University of Utah December 12, 2001.
Module – 7 network-attached storage (NAS)
EstiNet Network Simulator & Emulator 2014/06/ 尉遲仲涵.
Edge Based Cloud Computing as a Feasible Network Paradigm(1/27) Edge-Based Cloud Computing as a Feasible Network Paradigm Joe Elizondo and Sam Palmer.
1 Exploring Data Reliability Tradeoffs in Replicated Storage Systems NetSysLab The University of British Columbia Abdullah Gharaibeh Advisor: Professor.
CRON: Cyber-infrastructure for Reconfigurable Optical Networks PI: Seung-Jong Park, co-PI: Rajgopal Kannan GRA: Cheng Cui, Lin Xue, Praveenkumar Kondikoppa,
Computer System Architectures Computer System Software
B. White, J. Lepreau, L. Stoller, R. Ricci, S. Guruprasad, M. Newbold, M. Hibler, C. Barb, A. Joglekar Presented by Sunjun Kim Jonathan di Costanzo 2009/04/13.
PrimoGENI Tutorial Miguel Erazo, Neil Goldman, Nathanael Van Vorst, and Jason Liu Florida International University Other project participants: Julio Ibarra.
1 Enabling Large Scale Network Simulation with 100 Million Nodes using Grid Infrastructure Hiroyuki Ohsaki Graduate School of Information Sci. & Tech.
Introduction and Overview Questions answered in this lecture: What is an operating system? How have operating systems evolved? Why study operating systems?
Eric Keller, Evan Green Princeton University PRESTO /22/08 Virtualizing the Data Plane Through Source Code Merging.
MPLS and Traffic Engineering Ji-Hoon Yun Computer Communications and Switching Systems Lab.
Presented by: Sanketh Beerabbi University of Central Florida COP Cloud Computing.
Overlay Network Physical LayerR : router Overlay Layer N R R R R R N.
A Virtual Honeypot Framework Author: Niels Provos Published in: CITI Report 03-1 Presenter: Tao Li.
1 COMPSCI 110 Operating Systems Who - Introductions How - Policies and Administrative Details Why - Objectives and Expectations What - Our Topic: Operating.
Floodless in SEATTLE : A Scalable Ethernet ArchiTecTure for Large Enterprises. Changhoon Kim, Matthew Caesar and Jenifer Rexford. Princeton University.
Emulab and its lessons and value for A Distributed Testbed Jay Lepreau University of Utah March 18, 2002.
Access Control for Federation of Emulab-based Network Testbeds Ted Faber, John Wroclawski 28 July 2008
Issues Autonomic operation (fault tolerance) Minimize interference to applications Hardware support for new operating systems Resource management (global.
Operating Systems David Goldschmidt, Ph.D. Computer Science The College of Saint Rose CIS 432.
1 Testbeds Breakout Tom Anderson Jeff Chase Doug Comer Brett Fleisch Frans Kaashoek Jay Lepreau Hank Levy Larry Peterson Mothy Roscoe Mehul Shah Ion Stoica.
Emulation in Data Grid eXplorer. Emulation problematic Distributed applicationTarget environment Simulation Emulation App. Model Env. model Formal analysis.
Large-scale Virtualization in the Emulab Network Testbed Mike Hibler, Robert Ricci, Leigh Stoller Jonathon Duerig Shashi Guruprasad, Tim Stack, Kirk Webb,
Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1 Based upon slides from Jay Lepreau, Utah Emulab Introduction Shiv Kalyanaraman
1 Wide Area Network Emulation on the Millennium Bhaskaran Raman Yan Chen Weidong Cui Randy Katz {bhaskar, yanchen, wdc, Millennium.
1 Isolating Web Programs in Modern Browser Architectures CS6204: Cloud Environment Spring 2011.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved DISTRIBUTED SYSTEMS.
Globus and PlanetLab Resource Management Solutions Compared M. Ripeanu, M. Bowman, J. Chase, I. Foster, M. Milenkovic Presented by Dionysis Logothetis.
Computer Simulation of Networks ECE/CSC 777: Telecommunications Network Design Fall, 2013, Rudra Dutta.
Internet Protocol Storage Area Networks (IP SAN)
CS 283Computer Networks Spring 2013 Instructor: Yuan Xue.
@Yuan Xue CS 283Computer Networks Spring 2011 Instructor: Yuan Xue.
Deterlab Tutorial CS 285 Network Security. What is Deterlab? Deterlab is a security-enhanced experimental infrastructure (based on Emulab) that supports.
BDTS and Its Evaluation on IGTMD link C. Chen, S. Soudan, M. Pasin, B. Chen, D. Divakaran, P. Primet CC-IN2P3, LIP ENS-Lyon
1 Scalability and Accuracy in a Large-Scale Network Emulator Nov. 12, 2003 Byung-Gon Chun.
Working at a Small-to-Medium Business or ISP – Chapter 6
Ahmed Saeed†, Mohamed Ibrahim†, Khaled A. Harras‡, Moustafa Youssef†
Architecture and Algorithms for an IEEE 802
Oracle Solaris Zones Study Purpose Only
Support for ”interactive batch”
CLUSTER COMPUTING.
Working at a Small-to-Medium Business or ISP – Chapter 6
L. Glimcher, R. Jin, G. Agrawal Presented by: Leo Glimcher
Presentation transcript:

An Integrated Experimental Environment for Distributed Systems and Networks B. White, J. Lepreau, L. Stoller, R. Ricci, S. Guruprasad, M. Newbold, M. Hibler, C. Barb, A. Joglekar University of Utah Presented by Rachel Rubin CS /12/03 (slides borrowed heavily from OSDI ’02)

2 Approaches for testing Needs cover a spectrum –Ease of use –Control –Realism Simulation –Presents controlled, repeatable environment –Loses accuracy due to abstraction –e.g., ns, GloMoSim, x-sim [Brakmo’96] Live-network experimentation –Achieves realism –Surrenders repeatability –e.g., MIT “RON” testbed, PlanetLab Emulation –Introduces controlled packet loss and delay –Requires tedious manual configuration –e.g., Dummynet, nse [Fall’99], Trace Modulation [Noble’97], ModelNet [Vahdat’02]

3 Netbed Integrated access to: –Emulated, … Allocated from a dedicated cluster –Simulated, … –Wide-area nodes and links Selected from ~40 geographically-distributed nodes at ~30 sites Universal, remote access: 365 users 2176 “experiments” in 12 month period Time- and space-shared platform Enables qualitatively new research methods in networks, OSes, distributed systems, smart storage, …

4 Key Ideas “Emulab Classic” –Brings simulation’s efficiency and automation to emulation –2 orders of magnitude improvement in configuration time over a manual approach Virtual machine for network experimentation –Lifecycle & process analogy –Integrates simulation, emulation, and live- network experimentation

5 Two Emulation Goals 1. Accurate: Provide artifact-free environment 2. Universal: Run arbitrary workload: any OS, any code on “routers”, any program, for any user Default resource allocation policy is conservative: –Allocate full real node and link: no multiplexing –Assume maximum possible traffic

6 A Virtual Machine for Network Experimentation Maps common abstractions …To diverse mechanisms NodesCluster nodes, VMs on wide-area nodes, ns LinksVLANs, tunnels, Internet paths AddressesIPv4, ns node identifiers Eventsdistributed event system, ns event system Program Objectsremote execution, ns applications Queuing Disciplineson simulated and emulated nodes Projects, Users, ExperimentsIndependent of experimental technique Topology GenerationConfigure real or simulated nodes Topology VisualizationView hybrid topologies Traffic Generationns models, TG

7 Architecture Supports –Distributed Nodes Specify resources available –Isolation between experiments –Integrates simulation through nse

8 Netbed Virtual Machine Achieved through OS techniques: –Virtualization/abstraction –Single namespace –Conservative resource allocation, scheduling, preemption –Hard/soft state management Benefits: –Facilitates interaction, comparison, and validation –Leverages existing tools (e.g., traffic generation) –Brings capabilities of one technique to another (e.g., nse emulation of wireless links)

9 Outline Background and Related Work Experiment Life Cycle Efficiency and Utilization New Experimental Techniques Summary

10 Experiment Acts as central operational entity Represents … –Network configuration, including nodes and links –Node state, including OS images –Database entries, including event lists Lasts minutes to days, to weeks, to … forever!

11 Experiment Life Cycle Specification Parsing Global resource allocation Node self-configuration Experiment control Preemption and swapping

12 B Experiment Life Cycle $ns duplex-link $A $B 1.5Mbps 20ms BA DB AB A B SpecificationGlobal Resource AllocationNode Self-ConfigurationExperiment ControlSwap OutParsingSwap In AB

13 ns Specification ns: de-facto standard in network simulation, built on Tcl Important features: –Graceful transition for ns users –Power of general-purpose programming language Other means of specification: –Java GUI –Standard topology generators

14 Parsing Translate the ns/Tcl front end

15 Global Resource Allocation Binding abstractions to physical entities –NP-Hard Problem Combinatorial optimization techniques to provide resourse allocation –Map target configuration stored in the DB onto available physical resources Provide the dynamic addition or removal of nodes mid-experiment Virtualizes physical names so nodes can be moved

16 assign: Mapping Local Cluster Resources Maps virtual resources to local nodes and VLANs General combinatorial optimization approach to NP- complete problem Based on simulated annealing Minimizes inter-switch links & number of switches & other constraints … All experiments mapped in less than 3 secs [100 nodes]

17 wanassign: Mapping Distributed Resources Constrained differently than local mapping: –Treats physical nodes as fully-connected (by Internet) –Characterizes node types by “last-mile” link Implements a genetic algorithm

18 Mapping by Node Type set src [$ns node] set router [$ns node] set dest [$ns node] tb-set-hardware $src pc-internet tb-set-hardware $router pc-internet2 tb-set-hardware $dest pc-cable

19 Mapping by Link Characteristics set src [$ns node] set router [$ns node] set dest [$ns node] $ns duplex-link $src $router 10Mb 20ms DropTail $ns duplex-link $router $dest 5Mb 100ms DropTail

20 Node Self-Configuration Driven by nodes Controlled by centrally-stored state –Like linking and loading Allows nodes to be swapped out Allows clean images to be restored

21 Experiment Control Uses system of events Low-level open access to resources Restricted root privledges Manually confirm idle nodes before swapping them out

22 Security Provided Unique Login IDs Cookies required SSL for access SSH for login to nodes Current Addresses for identification purposes Random Passwords Firewalls on Netbed machines Unix secure group/project Accounts

23 Outline Background and Related Work Experiment Life Cycle Efficiency and Utilization New Experimental Techniques Summary

24 Disk Loading Loads full disk images Performance techniques: –Overlaps block decompression and device I/O –Uses a domain-specific algorithm to skip unused blocks –Delivers images via a custom reliable multicast protocol

25 “Frisbee” Disk Loader Scaling

26 Experiment Creation Scaling

27 Configuration Efficiency Emulation experiment configuration –Compared to manual approach using a 6-node “dumbbell” network –Improved efficiency (3.5 hrs vs 3 mins)

28 Utilization Serving last 12 months’ load, requires: –1064 nodes without time-sharing, But only 168 nodes with time-sharing. –19.1 years without space-sharing, But only 1 year with space-sharing.

29 Outline Background and Related Work Experiment Life Cycle Efficiency and Utilization New Experimental Techniques Summary

30 Parameter-Space Case Study Armada (Grid File System) Evaluation [Oldfield & Kotz’02] Run using batch experiments 7 bandwidths x 5 latencies x 3 application settings x 4 configs of 20 nodes 420 tests in 30 hrs (4.3 min apiece)

31 TCP Dynamics Case Study Runs ns regression tests on real kernels Compares empirical results vs. vetted simulation results Exploits simulation/emulation transparency to … –Check accuracy of simulation models, and … –Spot bugs in network stack implementations Infers packet loss from simulation output Injects failures into links via event system

32 TCP New Reno One Drop Test nsFreeBSD 4.3FreeBSD 4.5

33 Outline Background and Related Work Experiment Life Cycle Efficiency and Utilization New Experimental Techniques Summary

34 Summary Two orders of magnitude speedup in emulation setup and configuration time Provides a virtual machine for network experimentation Enables qualitatively new experimental techniques