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IEEE International Conference on Autonomic Computing (ICAC’06)

IEEE International Conference on Autonomic Computing (ICAC’06).

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IEEE International Conference on Autonomic Computing (ICAC’06)

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  1. IEEE International Conference on Autonomic Computing (ICAC’06) Autonomic Live Adaptation of Virtual Computational Environments in a Multi-Domain InfrastructurePaul Ruth, Junghwan Rhee, Dongyan XuDepartment of Computer Science and Center for Education and Research in Information Assurance and Security (CERIAS)Rick Kennell, Sebastien GoasguenRosen Center for Advanced ComputingPurdue UniversityWest Lafayette, Indiana, USA

  2. Outline of Talk • Motivations • Overall architecture • Design and implementation • Real-world deployment in nanoHUB • Related work • Conclusion • Demo

  3. Motivations • Formation of shared distributed cyberinfrastructure (CI) • Spanning multiple domains • Serving users/user communities with diverse computation needs • Exhibiting dynamic resource availability and workload • Need for virtual distributed environments (VIOLINs), each with • Customizability and legacy application compatibility • Administrative privileges • Isolation, security, and accountability • Autonomic adaptation capability - A unique opportunity brought by virtualization (VMs and VNs)

  4. Duke U. U. Florida Internet Adaptive VIOLINs Virtual clusters (VIOLINs) Physical cluster nanoHUB infrastructure@Purdue

  5. Autonomic VIOLIN Adaptation • Adaptation triggers: • Dynamic availability of infrastructural resources • Dynamic resource needs of applications running inside • Adaptation actions: • Resource re-allocation • Scale adjustment (adding/deleting virtual machines) • Re-location (migrating virtual machines) • Adaptation goals: • Improving application performance • Increasing infrastructural resource utilization • Maintaining user/application transparency • Minimizing infrastructure administrator attention

  6. Research Challenges • Autonomic live adaptation mechanisms • VM Resource monitoring and scaling • Application profiling and non-intrusive sensing of application needs • Live VIOLIN re-location across domains • Adaptation policies • VIOLIN adaptation model • Infrastructure resource availability and topology • Application resource needs • Application configuration and topology • Optimal VIOLIN adaptation decision-making • Goals (cost vs. gains)? • When to adapt? • How and how much to adapt? • Where to migrate?

  7. Overall Architecture VMs VMs VIOLIN Switch VIOLIN Switch VIOLIN Switch VIOLIN Switch Monitoring Daemon Monitoring Daemon Dom0 Dom0 VMM VMM VMs VMs Physical Network VIOLIN Switch VIOLIN Switch VIOLIN Switch CPU Update Adaptation Manager Monitoring Daemon Monitoring Daemon Dom0 Dom0 Scale Up Migrate VMM VMM

  8. VIOLIN Adaptation Policies • Maintain desirable resource utilization level • Reclaim resource if under-utilized over a period • Add resource if over-utilized over a period • Scale up local resource share • Migrate to other host(s) • Balance host workload • Intra-domain migration first • Minimize migration • Re-adjust resource according to application needs

  9. Implementation and Deployment • Extension to non-adaptive VIOLIN • Based on Xen 3.0 (w/ VM Live migration capability) • Enabling live VIOLIN migration across domains • IP addresses of VMs • Root file systems of VMs • Leveraging Xen libraries for VM resource monitoring (xenstat, xentop) • Extending VIOLIN switch for inter-VM bandwidth monitoring • Deployment in nanoHUB • On-line, on-demand simulation service for nanotechnology community • Web interface for regular users • “My workspace” interface for advanced users • Local infrastructure: two clusters in two subnets

  10. nanoHUB Deployment Overview Local Virtual Machines Migratable Isolated from Local infrastructure VIOLIN Virtual Cluster Delegated trust Virtual Infrastructure over WAN

  11. VIOLIN in nanoHUB In the backround: VIOLIN Simulation job

  12. Autonomic property: Users focus on simulation semantics and results, unaware of VIOLIN creation, setup, and adaptation. VIOLIN in nanoHUB

  13. Impact of Migration on App. Execution End-to-end execution time of NEMO3D w/ and w/o live VIOLIN migration

  14. Domain 1 Domain 1 Domain 2 Domain 2 VIOLIN 1 VIOLIN 3 VIOLIN 5 VIOLIN 2 VIOLIN 4 VIOLIN Adaptation Scenario 1. Initially VIOLIN 1, 2, 3 are computing, VIOLIN 2 is about to be finished. 2. After VIOLIN 2 is finished, before adaptation Without Adaptation With Adaptation

  15. Domain 1 Domain 1 Domain 2 Domain 2 VIOLIN 1 VIOLIN 3 VIOLIN 5 VIOLIN 2 VIOLIN 4 VIOLIN Adaptation Scenario 2. After VIOLIN 2 is finished, before adaptation 3. After adaptation Without Adaptation With Adaptation

  16. Domain 1 Domain 1 Domain 2 Domain 2 VIOLIN 1 VIOLIN 3 VIOLIN 5 VIOLIN 2 VIOLIN 4 VIOLIN Adaptation Scenario 4. After VIOLIN 4, 5 are created 3. After adaptation Without Adaptation With Adaptation

  17. Domain 1 Domain 1 Domain 2 Domain 2 VIOLIN 1 VIOLIN 3 VIOLIN 5 VIOLIN 2 VIOLIN 4 VIOLIN Adaptation Scenario 4. After VIOLIN 4, 5 are created 5. After VIOLIN 1, 3 are finished Without Adaptation With Adaptation

  18. Domain 1 Domain 1 Domain 2 Domain 2 VIOLIN 1 VIOLIN 3 VIOLIN 5 VIOLIN 2 VIOLIN 4 VIOLIN Adaptation Scenario 6. ALLVIOLINs are finished 5. After VIOLIN 1, 3 are finished Without Adaptation With Adaptation

  19. Limitations and Future Work • Simple, heuristic adaptation policy • Application of machine learning and data mining techniques • Centralized adaptation manager • Hierarchical or peer-to-peer adaptation managers • Imprecise application resource demand inference • Multi-dimensional, fine-grain resource demand profiling • Campus-wide infrastructure • Evaluation and deployment in wide-area infrastructure

  20. Related Work • VNET (Northwestern U.) • Cluster-on-Demand (COD) (Duke U.) • Virtual Workspaces on Grid (Argonne National Lab) • SoftUDC (HP Labs) • WOW and IPOP (U. Florida)

  21. Conclusions • Autonomically adaptive virtual infrastructures (VIOLINs) • A new opportunity brought by virtualization technologies • Decoupled from underlying shared infrastructure • Intelligent, first-class entities with user-transparent resource provisioning • Key benefits • Application performance improvement • Infrastructure resource utilization • Management convenience (at both virtual and physical levels) “The Cray motto is: adapt the system to the application - not the application to the system.” - Steve Scott, CTO, Cray Inc. on “adaptive supercomputing”, March 2006

  22. Thank you. For more information: Email:dxu@cs.purdue.edu URL:http://www.cs.purdue.edu/~dxu Google:“Purdue VIOLIN FRIENDS”

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