Resource Provisioning based on Lease Preemption in InterGrid
description
Transcript of Resource Provisioning based on Lease Preemption in InterGrid
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,[email protected]
• 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?
42
1
2 62
42
1
2
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.