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Virtuoso: Distributed Computing Using Virtual Machines

Virtuoso: Distributed Computing Using Virtual Machines. Peter A. Dinda Prescience Lab Department of Computer Science Northwestern University http://plab.cs.northwestern.edu. People and Acknowledgements. Students

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Virtuoso: Distributed Computing Using Virtual Machines

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  1. Virtuoso: Distributed Computing Using Virtual Machines Peter A. Dinda Prescience Lab Department of Computer Science Northwestern University http://plab.cs.northwestern.edu

  2. People and Acknowledgements • Students • Ashish Gupta, Ananth Sundararaj, Dong Lu, Bin Lin, Jason Skicewicz, Billy Davidson, Andrew Weinrich, Jack Lange, Alex Shoykhet • Collaborators • In-Vigo project at University of Florida • Renato Figueiredo, Jose Fortes • http://invigo.acis.ufl.edu • Funder • NSF through several awards

  3. Outline • Motivation • Virtuoso Model • Virtual networking and remote devices • Information services • Resource measurement and prediction • Resource control • Related work • Conclusions R. Figueiredo, P. Dinda, J. Fortes, A Case For Grid Computing on Virtual Machines, ICDCS 2003

  4. How do we deliver arbitrary amounts of computational power to ordinary people?

  5. How do we deliver arbitrary amounts of computational power to ordinary people? Distributed and Parallel Computing Interactive Applications

  6. How do we deliver arbitrary amounts of computational power to ordinary people? Distributed and Parallel Computing Interactive Applications

  7. Internet IBM xSeries virtual cluster (64 CPUs), 1 TB RAID Interactivity Environment Cluster, CAVE (~90 CPUs), 8 TB RAID 2 Distributed Optical Testbed Clusters IBM xSeries (14-28 CPUs), 1 TB RAID DOT clusters with optical connectivity IBM xSeries (14-28 CPUs), 1 TB RAID: Argonne, U.Chicago, IIT, NCSA, others Nortel Optera Metro Edge Optical Router Distributed Optical Testbed (DOT) Private Optical Network Northwestern

  8. Grid Computing • “Flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources” • I. Foster, C. Kesselman, S. Tuecke, The Anatomy of the Grid: Enabling Scalable Virtual Organizations, International J. Supercomputer Applications, 15(3), 2001 • Globus, Condor/G, Avaki, EU DataGrid SW, …

  9. Complexity from User’s Perspective • Process or job model • Lots of complex state: connections, special shared libraries, licenses, file descriptors • Operating system specificity • Perhaps even version-specific • Symbolic supercomputer example • Need to buy into some “Grid API” • Install and learn complex Grid software

  10. Users already know how to deal with this complexity at another level

  11. Complexity from Resource Owner’s Perspective • Install and learn complex Grid software • Deal with local accounts and privileges • Associated with global accounts or certificates • Protection • Support users with different OS, library, license, etc, needs.

  12. Virtual Machines • Language-oriented VMs • Abstract interpreted machine, JIT Compiler, large library • Examples: UCSD p-system, Java VM, .NET VM • Application-oriented VMs • Redirect library calls to appropriate place • Examples: Entropia VM • Virtual servers • Kernel makes it appear that a group of processes are running on a separate instance of the kernel • Examples: Ensim, Virtuozzo, SODA, … • Virtual machine monitors (VMMs) • Raw machine is the abstraction • VM represented by a single image • Examples: IBM’s VM, VMWare, Virtual PC/Server, Plex/86, SIMICS, Hypervisor, DesQView/TaskView. VM/386

  13. VMWare GSX VM

  14. Isn’t It Going to Be Too Slow? Small relative virtualization overhead; compute-intensive Experimental setup: physical: dual Pentium III 933MHz, 512MB memory, RedHat 7.1, 30GB disk; virtual: Vmware Workstation 3.0a, 128MB memory, 2GB virtual disk, RedHat 2.0 NFS-based grid virtual file system between UFL (client) and NWU (server)

  15. Isn’t It Going To Be Too Slow? Synthetic benchmark: exponentially arrivals of compute bound tasks, background load provided by playback of traces from PSC Relative overheads < 10%

  16. Isn’t It Going To Be Too Slow? • Virtualized NICs have very similar bandwidth, slightly higher latencies • J. Sugerman, G. Venkitachalam, B-H Lim, “Virtualizing I/O Devices on VMware Workstation’s Hosted Virtual Machine Monitor”, USENIX 2001 • Disk-intensive workloads (kernel build, web service): 30% slowdown • S. King, G. Dunlap, P. Chen, “OS support for Virtual Machines”, USENIX 2003

  17. Virtuoso • Approach: Lower level of abstraction • Raw machines, not processes • Mechanism: Virtual machine monitors • Our Focus: Middleware support to hide complexity • Ordering, instantiation, migration of machines • Virtual networking and remote devices • Connectivity to remote files, machines • Information services • Monitoring and prediction • Resource control

  18. The Virtuoso Model • User orders raw machine(s) • Specifies hardware and performance • Basic software installation available • OS, libraries, licenses, etc. • Virtuoso creates raw imageand returns reference • Image contains disk, memory, configuration, etc. • User “powers up” machine • Virtuoso chooses provider • Information service • Virtuoso migrates image to provider • Efficient network transfer • rsync, demand paging, versioned filesystems

  19. The Virtuoso Model • Provider instantiates machine • Virtual networking ties machine back to user’s home network • Remote device support makes user’s desktop’s devices available on remote VM • Remote display support gives user the console of the machine (VNC) • Resource control to give user expected performance • User goes to his network admin to get address, routing for his new machine • User customizes machine • Feeds in CDs, floppies, ftp, up2date, etc.

  20. The Virtuoso Model • User uses machine • Shutdown, hibernate, power-off, throw away • Virtuoso continuously monitors and adapts • Various mechanisms, all invisible to user • Migrating the machine • Routing traffic between machines • Virtual network topology • Predictive scheduling versus reservations • Various goals • Price • Interactivity • Information service • Resource monitoring and prediction

  21. Outline • Motivation • Virtuoso Model • Virtual networking and remote devices • Information services • Resource measurement and prediction • Resource control • Related work • Conclusions R. Figueiredo, P. Dinda, J. Fortes, A Case For Grid Computing on Virtual Machines, ICDCS 2003

  22. Why Virtual Networking? • A machine is suddenly plugged into your network. What happens? • Does it get an IP address? • Is it a routeable address? • Does firewall let its traffic through? • To any port? How do we make virtual machine hostileenvironments as friendly as the user’s LAN?

  23. A Layer 2 Virtual Network (VLAN) for the User’s Virtual Machines • Why Layer 2? • Protocol agnostic • Mobility • Simple to understand • Ubiquity of Ethernet on end-systems • What about scaling? • Number of VMs limited • Hierarchical routing possible because MAC addresses can be assigned hierarchically

  24. A Simple Layer 2 Virtual Network Client Server VM monitor SSH Remote VM Virtual NIC Physical NIC Physical NIC Friendly Local Network Hostile Remote Network

  25. A Simple Layer 2 Virtual Network Client Server VM monitor SSH Remote VM Virtual NIC Physical NIC Physical NIC Friendly Local Network Hostile Remote Network

  26. A Simple Layer 2 Virtual Network Client Server SSH Tunnel Or SSL TCP Bridged Bridged VM monitor Remote VM Virtual NIC Physical NIC Physical NIC Friendly Local Network Hostile Remote Network

  27. An Overlay Network • Bridgeds and connections form an overlay network for routing traffic among virtual machines and the user’s home network • Links can trivially be added or removed

  28. Bootstrapping the Virtual Network • Star topology always possible • TCP session from client must have been possible • Better topology may be possible • Depends on security at each site • Topology may change • Virtual machines can migrate • Bootstrap to higher layers • Virtual filesystems

  29. Remote Devices Client Server SSH Tunnel Or SSL TCP nbd-server nbd-client VM monitor Remote VM Virtual CDROM Physical CDROM Linux Network Block Device Driver /dev/cdrom <-> /dev/nb0 <-> VMWare CD Image

  30. Extending a Grid Information Service (GIS) to Support Virtual Machines • A GIS contains information about the available resources in a grid • Hosts, routers, switches, software, etc. • URGIS project at Northwestern • GIS based on the relational data model • Compositional queries (joins) to find collections of resources. • “Find physical machines which can instantiate a virtual machine with 1 GB of memory” • “Find sets of four different virtual machines on the same network with a total memory between 512 MB and 1 GB” • Nondeterministic query extension for scalability • http://www.cs.northwestern.edu/~urgis

  31. The RGIS Design (Per Site)

  32. Motivation for Non-deterministic Queries • Queries for compositions of resources easily expressed in SQL: • But such queries can be very expensive to execute • However, we typically don’t need the entire result set, just some rows, and not always the same ones • And we need them in a bounded amount of time • Approach: return random sample of result set select h1.insertid, h2.insertid from hosts h1, hosts h2 where h1.os=‘LINUX’ and h2.os=‘LINUX’ and h1.mem_mb+h2.mem_mb>=3072 “Find 2 hosts with Linux that together have 3 GB of RAM”

  33. Implementing non-deterministic queries select nondeterministically h1.insertid, h2.insertid from hosts h1, hosts h2 where h1.os=‘LINUX’ and h2.os=‘LINUX’ and h1.mem_mb+h2.mem_mb>=3072 within 2 seconds SELECT H1.INSERTID, H2.INSERTID FROM HOSTS H1, HOSTS H2 , INSERTIDS TEMP_H1 , INSERTIDS TEMP_H2 WHERE (H1.OS='LINUX' AND H2.OS='LINUX' AND H1.MEM_MB+H2.MEM_MB>=3072) AND(H1.INSERTID=TEMP_H1.INSERTID AND TEMP_H1.rand > 982663452.975047 AND TEMP_H1.rand <= 1025613125.93505) AND (H2.INSERTID=TEMP_H2.INSERTID AND TEMP_H2.rand > 1877769069.94039 AND TEMP_H2.rand <= 1920718742.90039) Query Manager and Rewriter Random sample ofinput tablesProbability of inclusiondetermined by time constraintand server load

  34. Deadlines

  35. P. Dinda, D. Lu, Nondeterministic Queries in a Relational Grid Information Service, SC 2003 D. Lu, P. Dinda, Synthesizing Realistic Computational Grids, SC 2003 D. Lu, P. Dinda, J. Skicewicz, Scoped and Approximate Queries in a Relational Grid Information Service, Grid 2003

  36. Extending a Grid Information Service (GIS) to Support Virtual Machines • Virtual indirection • Each RGIS object has a unique id • Virtualization table associates unique id of virtual resources with unique ids of their constituent physical resources • Virtual nature of resource is hidden unless query explicitly requests it • Futures • An RGIS object that does not exist yet • Futures table of unique ids • Future nature of resource hidden unless query explicitly requests it

  37. Extending a Resource Monitoring and Prediction System to Support Virtual Machines • Measuring and predicting dynamic resource availability to support adaptation • Virtual machine migration • Routing on the virtual network • Application-level adaptation • RPS System at Northwestern • Host and network measurements for Unix and Windows • Emphasis on prediction (wide range of linear and nonlinear models) and communication (wide range of transports) P. Dinda, Online Prediction of the Running Time of Tasks, Journal of Cluster Computing, 2002 J. Skicewicz, P. Dinda, J. Schopf, Multiresolution Resource Behavior Queries using Wavelets, HPDC 2001 P. Dinda, A Prediction-based Real-time Scheduling Advisor, IPDPS 2002

  38. RPS Toolkit • Extensible toolkit for implementing resource signal prediction systems [CMU-CS-99-138] • Growing: RTA, RTSA, Wavelets, GUI, etc • Easy “buy-in” for users • C++ and sockets (no threads) • Prebuilt prediction components • Libraries (sensors, time series, communication) • http://www.cs.northwestern.edu/~RPS

  39. Example: Multiscale Network Prediction • Large, recent study of predictability • Hundreds of NLANR and other traces • Mostly WANs • Different resolutions • Binning and low-pass via wavelets • Sweet Spot • Predictability often maximized at particular resolution Y. Qiao, J. Skicewicz, P. Dinda, Multiscale Predictability of Network Traffic, NWU-CS-02-13

  40. Multiresolution Network Prediction

  41. Extending a Resource Prediction System to Support Virtual Machines • Goal: monitor physical machine and infer behavior inside of virtual machine • Current approach: /proc on physical machine to slowdown on resource rate in virtual machine • ARX models • Causality problem

  42. Resource Control • Owner has an interest in controlling how much and when compute time is given to a virtual machine • Our approach: A language for expressing these constraints, and compilation to real-time schedules, proportional share, etc. • Very early stages. Trying to avoid kernel modifications.

  43. How to Control: User Irritation Project • Measure interactive user tolerance to resource stealing • Conversely, what service must be provided to interactive users? • “Irritation@Home”

  44. Outline • Motivation • Virtuoso Model • Virtual networking and remote devices • Information services • Resource measurement and prediction • Resource control • Related work • Conclusions R. Figueiredo, P. Dinda, J. Fortes, A Case For Grid Computing on Virtual Machines, ICDCS 2003

  45. Related Work • Collective / Capsule Computing (Stanford) • VMM, Migration/caching, Hierarchical image files • Denali (U. Washington) • Highly scalable VMMs (1000s of VMMs per node) • CoVirt (U. Michigan) • Xenoserver (Cambridge) • SODA (Purdue) • Virtual Server, fast deployment of services • Internet Suspend/Resume (Intel Labs Pittsburgh) • Ensim • Virtual Server, widely used for web site hosting • WFQ-based resource control released into open-source Linux kernel • Virtouzzo (SWSoft) • Ensim competitor • Available VMMs: IBM’s VM, VMWare, Virtual PC/Server, Plex/86, SIMICS, Hypervisor, DesQView/TaskView. VM/386

  46. Current Status (At Northwestern) • Bridged components done • Mechanism for virtual networking • No policy yet • Very preliminary system for acquiring and instantiating VMs done • RGIS schema extensions done • Work In Progress • Remote devices (management) • Virtual networking (policy + adaptation) • VM Monitoring using RPS • User Irritation

  47. For MoreInformation • Prescience Lab (Northwestern University) • http://plab.cs.northwestern.edu • ACIS (University of Florida) • http://acis.ufl.edu R. Figueiredo, P. Dinda, J. Fortes, A Case For Grid Computing on Virtual Machines, ICDCS 2003

  48. Nondeterministic query performance Meaningful tradeoff between query processing time and result set size is possible Select two hosts that together have >3GB of RAM 500,000 host grid generated by GridG Memory distribution according to Smith study of MDS contents Dual Xeon 1 GHz, 2 GB, 240 GB RAID, RGIS2, Oracle 9i Enterprise Average of five trials

  49. Nondeterministic query performance Can use tradeoff to controlquery time independent of query complexity Select n hosts that together have >3GB of RAM 500,000 host grid generated by GridG Memory distribution according to Smith study of MDS contents Dual Xeon 1 GHz, 2 GB, 240 GB RAID, RGIS2, Oracle 9i Enterprise Average of five trials

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