Energy-Efficient Scheduling on Milliclusters with Performance Constraints
GREENCOM '11 Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications
Combining analytic kernel models for energy-efficient data modeling and classification
The Journal of Supercomputing
Hierarchical genetic-based grid scheduling with energy optimization
Cluster Computing
Security, energy, and performance-aware resource allocation mechanisms for computational grids
Future Generation Computer Systems
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Contention on shared resources such as cache and main memory slows down the execution of the applications affecting not only application performance but also induces inefficient use of energy. Therefore, in this paper we deal with the contention problem and energy optimization on shared resources multicore-based machines. Our main contribution is a memory-aware resource allocation algorithm that minimize energy consumption by reducing contention conflicts and maximizing performance. We design a heuristic that includes in its objective function the impact of the contention on the application performance. Experimental results emphasize the interest of the provided solution.