FG-76

Project Details

Project Lead

Paul Marshall 

Project Manager

Paul Marshall 

Institution

University of Colorado at Boulder, Computer Science  

Discipline

Computer Science (401) 

Abstract

A common problem in on-demand IaaS clouds is utilization: in order to ensure on-demand availability providers have to ensure that there are available resources waiting for a request to come. To do that, they either have to significantly overprovision resources (in which case they experience low utilization) or reject a large proportion of requests (in which case the cloud is not really on-demand). The question arises: how can we combine best of both worlds?

Intellectual Merit

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Broader Impacts

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Scale of Use

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Results

Problem: A common problem in on-demand IaaS clouds is utilization: in order to ensure on-demand availability providers have to ensure that there are available resources waiting for a request to come. To do that, they either have to significantly overprovision resources (in which case they experience low utilization) or reject a large proportion of requests (in which case the cloud is not really on-demand). The question arises: how can we combine best of both worlds?

Approach: Paul Marshall from the University of Colorado at Boulder approached this problem by deploying always-on preemptible VMs on all nodes of an IaaS cloud. When an on-demand request comes, the preemptible VMs are terminated in order to release resources for the on-demand request; when the nodes again become available the preemptible VMs are redeployed. Using this method, Paul was able to solve the utilization problem described above and demonstrate cloud utilization of up to 100%. Since sudden preemption is typical in volunteer computing systems such as SETI@home or various Condor installations, this solution was therefore evaluated in the context of a Condor system measure its efficiency for the volnteer computing execution which was shown to be over 90%.

In order to evaluate his system experimentally, Paul first modified the open source Nimbus toolkit to extend its functionality to supports the backfill approach. He then had to deploy the augmented implementation on a sizable testbed that gave him enough privilege (root) to install and configure the augmented Nimbus implementation -- Such requirements are typically hard to find on platforms other than dedicated local resources. In this case however, this testbed was provided by the FutureGrid hotel resource (specifically we used 19 8-core FG nodes on hotel).

Cloud utilization comparison for the same on-demand request trace.

Figure: Cloud utilization comparison for the same on-demand request trace: backfill VMs are disabled and the cloud is very little utilized ("cold"); 

Figure: Backfill VMs are enabled and the cloud achieves close to 100% utilization over the period of observation.

References:

• Improving Utilization of Infrastructure Clouds, Paul Marshall, Kate Keahey, Tim Freeman, submitted to CCGrid 2011.