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Resource Provisioning based on Lease Preemption in InterGrid

Resource Provisioning based on Lease Preemption in InterGrid. Mohsen Amini Salehi , Bahman Javadi , Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia

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Resource Provisioning based on Lease Preemption in InterGrid

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  1. Resource Provisioning based on Lease Preemption in InterGrid MohsenAminiSalehi , BahmanJavadi, RajkumarBuyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia Mohsena,bahmanj,raj@csse.unimelb.edu.au

  2. Introduction • Providing computational resources for users is one of the challenges in the high performance computing. • Resource Providers (RP)? • Grid 5000, DAS-2,Amazon EC2,etc.

  3. InterGrid • provides an architecture and policies for inter-connecting different Grids. • Computational resources in each RP are shared between grid users and local users. • Provisioning rights of the resources in a Grid are delegated to the InterGrid Gateway (IGG). • Local users vs Grid (External) users.

  4. Lease based Resource Provisioning in InterGrid • A lease is an agreement between resource provider and resource consumer whereby the provider agrees to allocate resources to the consumer according to the lease terms presented. • Virtual Machine (VM) technology is a way to implement lease-based resource provisioning. • VMs are able to get suspended, resumed, stopped, or even migrated. • InterGridmakes one lease for each user request.

  5. InterGrid

  6. Problem Statement • How to provision resources for local requests when existing resources have been allocated to grid requests? • Partitioning • Preempting.

  7. Challenges of Preempting • Is that really useful?! • Originally, it is not allowed to preempt leases without permission. • How to do that? • What to do with preempted leases? • lease preemption has some side-effects: • imposes time overhead • can potentially affect other reservations

  8. Challenges of Preempting… • In an RP, usually several leases have to be preemptedto make sufficient resources. • there are also several choices for preemption! (Candidate Sets). • candidate sets have various amount of imposed overhead. Different number of grid users get affected. • How to choose the optimal candidate set for preemption?

  9. Which one is optimal Candidate set? 2 2 4 6 1 1 4 2 2 2

  10. Related Work • Haizea:a lease scheduler for advanced reservation and best effort leases. For preemting it just considers the preemptability of the lease. • Sotomayor et al. estimated the overhead time imposed by preempting a lease (suspending and resuming a VM) • Walters et al. used preemption to give precedence to interactive jobs inside a cluster. But they focus on how to checkpoint the preempted job, and how to resume the preemptedjob. • Kettimuthu et al. applied preemption policy to decrease waiting time.

  11. Proposed Solution(1): make the preemption possible • We introduce different request types (lease type) in InterGrid. • At the moment, a user request in InterGrid is composed of: • Virtual Machine (VM) name needed by the user. • Number of VMs needed. • Ready time: the time that requested VMs should be ready. • Wall time: duration of the lease. • Deadline: the time that serving the request must be finished. • Based on the lease types, it is determined how to schedule the lease and what to do with a preempted lease.

  12. Proposed Solution(1): Introducing Different Lease Types • Best Effort-Cancelable: • neither guarantee the deadline nor the wall time. • impose the minimum overhead time in preemption. • Best Effort-Suspendable: • guarantees the wall time but not in a specific deadline. • overhead is the time to suspend a VM, reschedule , and resume it. • Deadline Constraint-Migratable: • guarantee both the wall time and deadline of the lease. • Deadline Constraint-Non-Preemptable: • guarantees both deadline and wall time .

  13. Proposed Solution(2): Preemption Policy-1 • Minimum Overhead Policy (MOV) • aims at maximizing resource utilization. • tries to minimize the time overhead imposed to the underlying system • preemptsa candidate set that leads to the minimum overhead. • It works out the total overhead imposed to the system by each candidate set and the set with minimum overhead is selected.

  14. Proposed Solution(2): Preemption Policy-2 • Minimum Leases Involved Policy(MLIP) • Users do not like that their leases get affected by preemption. • As a user centric policy, MLIP tries to satisfy more users by preempting less number of leases. • In this policy a candidate set that contains minimum number of leases is selected from all the candidate sets. • MLIP disregards the type of leases involved in a candidate set.

  15. Proposed Solution(2): Preemption Policy-3 • Minimum Overhead Minimum Lease Policy (MOML) • MOML is a balance between MOV

  16. Minimum Overhead Minimum Lease Policy (MOML)

  17. Performance Evaluation-Metrics • Local and Grid Request Rejection Rate • Resource Utilization • Number of Lease Preemption

  18. Experiment configuration: • We use Lublin99 workload model. • We experiment an RP with 32 nodes.

  19. Experimental Results:Local and Grid Request Rejection Rate

  20. Resource Utilization

  21. Number of Lease Preemptions

  22. Conclusion • we leveraged preempting grid leases in favour of local requests. • We proposed different typesof leases for lease based resource providers. • We proposed three policies for lease preemption: • MOV as a policy that improves system utilization, • MLIP that results in less number of preemptionand increasing user satisfaction, • MOML which makes a trade-off between resource utilization and user satisfaction.

  23. Future Work • Scheduling policies in IGG that makes less preemption. • we are interested in optimal sequence of grid leases in a site.

  24. THANK YOUAny Question?

  25. References • Chase, J. S., Irwin, D. E., Grit, L. E., Moore, J. D. &Sprenkle, S. E. (2003), Dynamic virtual clusters in a grid site manager, in `Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing', Washington, DC,USA, pp. 90-98. • De Assunc~ao, M., Buyya, R. & Venugopal, S. (2008), `InterGrid: A case for internetworking islands of Grids', Concurrency and Computation: Practice and Experience 20(8), 997-1024. • Lublin, U. & Feitelson, D. G. (2001), `The workload on parallel supercomputers: Modeling the characteristics of rigid jobs', Journal of Parallel and DistributedComputing 63, 2003. • Sotomayor, B., Keahey, K. & Foster, I. (2008), Combining batch execution and leasing using virtual machines, in `Proceedings of the 17th International Symposium on High Performance Distributed Computing', ACM, New York, NY, USA,pp. 87-96.

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