Name
Abstract | ||
|---|---|---|
Performance of applications executed on large parallel systems suffer due to load imbalance. Load balancing is required to scale such applications to large systems. However, performing load balancing incurs a cost which may not be known a priori. In addition, application characteristics may change due to its dynamic nature and the parallel system used for execution. As a result, deciding when to balance the load to obtain the best performance is challenging. Existing approaches put this burden on the users, who rely on educated guess and extrapolation techniques to decide on a reasonable load balancing period, which may not be feasible and efficient. In this paper, we propose the Meta-Balancer framework which relieves the application programmers of deciding when to balance load. By continuously monitoring the application characteristics and using a set of guiding principles, Meta-Balancer invokes load balancing on its own without any prior application knowledge. We demonstrate that Meta-Balancer improves or matches the best performance that can be obtained by fine tuning periodic load balancing. We also show that in some cases Meta-Balancer improves performance by 18% whereas periodic load balancing gives only a 1.5% benefit. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CLUSTER.2012.61 | Cluster Computing |
Keywords | Field | DocType |
cases meta-balancer,application programmer,fine tuning periodic load,load balancing,prior application knowledge,automated load,application characteristic,meta-balancer framework,periodic load balancing,application characteristics,best performance,reasonable load,mathematical model,parallel,computational modeling,resource allocation,simulation,parallel processing,force,automated | Load management,Network Load Balancing Services,Load modeling,Computer science,Load balancing (computing),Fine-tuning,Parallel computing,A priori and a posteriori,Real-time computing,Extrapolation,Resource allocation,Distributed computing | Conference |
ISSN | ISBN | Citations |
1552-5244 | 978-1-4673-2422-9 | 12 |
PageRank | References | Authors |
0.69 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harshitha Menon | 1 | 49 | 6.89 |
| Nikhil Jain | 2 | 321 | 24.01 |
| Gengbin Zheng | 3 | 829 | 55.03 |
| Laxmikant V. Kale | 4 | 2871 | 248.18 |