Resource Provisioning based on Lease Preemption in InterGrid

Mohsen Amini Salehi , Bahman Javadi, Rajkumar BuyyaCloud 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

• 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.

Introduction

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.

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.

• InterGrid makes one lease for each user request.

InterGrid

Problem Statement

• How to provision resources for local requests when existing resources have been allocated

to grid requests?

• Partitioning• Preempting.

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

Challenges of Preempting…

• In an RP, usually several leases have to be preempted to 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?

Which one is optimal Candidate set?

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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 preempted job.

• Kettimuthu et al. applied preemption policy to decrease waiting time.

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.

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 .

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– preempts a 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.

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.

Proposed Solution(2): Preemption Policy-3

• Minimum Overhead Minimum Lease Policy (MOML)– MOML is a balance between MOV

Minimum Overhead Minimum Lease Policy (MOML)

Performance Evaluation-Metrics

• Local and Grid Request Rejection Rate• Resource Utilization• Number of Lease Preemption

Experiment configuration:

• We use Lublin99 workload model.• We experiment an RP with 32 nodes.

Experimental Results:Local and Grid Request Rejection Rate

Resource Utilization

Number of Lease Preemptions

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 preemption and

increasing user satisfaction, – MOML which makes a trade-off between resource

utilization and user satisfaction.

Future Work

• Scheduling policies in IGG that makes less preemption.

• we are interested in optimal sequence of grid leases in a site.

THANK YOUAny Question?

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 Distributed Computing 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.