Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
ETAHM: an energy-aware task allocation algorithm for heterogeneous multiprocessor
Proceedings of the 45th annual Design Automation Conference
IEEE Transactions on Parallel and Distributed Systems
A Heuristic Energy-aware Scheduling Algorithm for Heterogeneous Clusters
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
A genetic algorithm for energy efficient device scheduling in real-time systems
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A new evolutionary algorithm using shadow price guided operators
Applied Soft Computing
Solving the Stock Reduction Problem with the Genetic Linear Programming Algorithm
ICCIS '10 Proceedings of the 2010 International Conference on Computational and Information Sciences
Green Task Scheduling Algorithms with Speeds Optimization on Heterogeneous Cloud Servers
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
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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Minimizing computing energy consumption has many benefits, such as environment protection, cost savings, etc. An important research problem is the energy aware task scheduling for cloud computing. For many diverse computers in a typical cloud computing system, great energy reduction can be achieved by smart optimization methods. The objective of energy aware task scheduling is to efficiently complete all assigned tasks to minimize energy consumption with various constraints. Genetic Algorithm (GA) is a popular and effective optimization algorithm. However, it is much slower than other traditional search algorithms such as heuristic algorithm. In this paper, we propose a shadow price guided algorithm (SGA) to improve the performance of energy aware task scheduling. Experiment results have shown that our energy aware task scheduling algorithm using the new SGA is more effective and faster than the standard GA.