Abstract
Driven by the benefits of elasticity and pay-as-you-go model, cloud computing is emerging as an attractive alternative and addition to in-house clusters and supercomputers for some High Performance Computing (HPC) applications. However, poorinterconnect performance, heterogeneous and dynamic environment, and interference by other virtual machines (VMs) are some bottlenecks for efficient HPC in cloud. For tightly-coupled iterative applications, one slow processor slows down the entire application, resulting in poor CPU utilization. In this paper, we present a dynamic load balancer for tightly-coupled iterative HPC applications in cloud. It infers the static hardware heterogeneity in virtualized environments, and also adapts to the dynamic heterogeneity caused by the interference arising due to multi-tenancy. Through continuous live monitoring, instrumentation, and periodic refinement of task distribution to VMs, our load balancer adapts to the dynamic variations in cloud resources. Through experimental evaluation on a private cloud with 64 VMs using benchmarks and a real science application, we demonstrate performance benefits up to 45%. Finally, we analyze the effect of load balancing frequency, problem size, and computational granularity (problem decomposition) on the performance and scalability of our techniques.
| Original language | English (US) |
|---|---|
| Pages | 402-409 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Aug 14 2013 |
| Event | 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013 - Delft, Netherlands Duration: May 13 2013 → May 16 2013 |
Other
| Other | 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013 |
|---|---|
| Country | Netherlands |
| City | Delft |
| Period | 5/13/13 → 5/16/13 |
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Keywords
- Cloud
- High Performance Computing
- Load-balance
ASJC Scopus subject areas
- Software
Cite this
Improving HPC application performance in cloud through dynamic load balancing. / Gupta, Abhishek; Sarood, Osman; Kale, Laxmikant V; Milojicic, Dejan.
2013. 402-409 Paper presented at 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, Delft, Netherlands.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Improving HPC application performance in cloud through dynamic load balancing
AU - Gupta, Abhishek
AU - Sarood, Osman
AU - Kale, Laxmikant V
AU - Milojicic, Dejan
PY - 2013/8/14
Y1 - 2013/8/14
N2 - Driven by the benefits of elasticity and pay-as-you-go model, cloud computing is emerging as an attractive alternative and addition to in-house clusters and supercomputers for some High Performance Computing (HPC) applications. However, poorinterconnect performance, heterogeneous and dynamic environment, and interference by other virtual machines (VMs) are some bottlenecks for efficient HPC in cloud. For tightly-coupled iterative applications, one slow processor slows down the entire application, resulting in poor CPU utilization. In this paper, we present a dynamic load balancer for tightly-coupled iterative HPC applications in cloud. It infers the static hardware heterogeneity in virtualized environments, and also adapts to the dynamic heterogeneity caused by the interference arising due to multi-tenancy. Through continuous live monitoring, instrumentation, and periodic refinement of task distribution to VMs, our load balancer adapts to the dynamic variations in cloud resources. Through experimental evaluation on a private cloud with 64 VMs using benchmarks and a real science application, we demonstrate performance benefits up to 45%. Finally, we analyze the effect of load balancing frequency, problem size, and computational granularity (problem decomposition) on the performance and scalability of our techniques.
AB - Driven by the benefits of elasticity and pay-as-you-go model, cloud computing is emerging as an attractive alternative and addition to in-house clusters and supercomputers for some High Performance Computing (HPC) applications. However, poorinterconnect performance, heterogeneous and dynamic environment, and interference by other virtual machines (VMs) are some bottlenecks for efficient HPC in cloud. For tightly-coupled iterative applications, one slow processor slows down the entire application, resulting in poor CPU utilization. In this paper, we present a dynamic load balancer for tightly-coupled iterative HPC applications in cloud. It infers the static hardware heterogeneity in virtualized environments, and also adapts to the dynamic heterogeneity caused by the interference arising due to multi-tenancy. Through continuous live monitoring, instrumentation, and periodic refinement of task distribution to VMs, our load balancer adapts to the dynamic variations in cloud resources. Through experimental evaluation on a private cloud with 64 VMs using benchmarks and a real science application, we demonstrate performance benefits up to 45%. Finally, we analyze the effect of load balancing frequency, problem size, and computational granularity (problem decomposition) on the performance and scalability of our techniques.
KW - Cloud
KW - High Performance Computing
KW - Load-balance
UR - http://www.scopus.com/inward/record.url?scp=84881307424&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881307424&partnerID=8YFLogxK
U2 - 10.1109/CCGrid.2013.65
DO - 10.1109/CCGrid.2013.65
M3 - Paper
AN - SCOPUS:84881307424
SP - 402
EP - 409
ER -