Power optimization of real-time embedded systems on variable speed processors
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Communications of the ACM
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
A shadow price guided genetic algorithm for energy aware task scheduling on cloud computers
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
GREENCOM '11 Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications
Leveraging Heterogeneity for Energy Minimization in Data Centers
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
On minimizing the resource consumption of cloud applications using process migrations
Journal of Parallel and Distributed Computing
Hi-index | 0.00 |
Currently, a large number of cloud computing servers waste a tremendous amount of energy and emit a considerable amount of carbon dioxide. Thus, it is necessary to significantly reduce pollution and substantially lower energy usage. This paper seeks to implement six innovative green task scheduling algorithms that have two main steps: assigning as many tasks as possible to a cloud server with lowest energy, and setting the same optimal speed for all tasks assigned to each cloud server. A newly proven theorem can determine the optimal speed for all tasks assigned to a computer. These novel green algorithms are developed for heterogeneous cloud servers with adjustable speeds and parameters to effectively reduce energy consumption and finish all tasks before a deadline. Based on sufficient simulations, three green algorithms that allocate a task to a cloud server with minimum energy are more effective than three others that assign a task to a randomly selected cloud server. Sufficient simulation results indicate that the best algorithm among the six algorithms is Shortest Task First for Computer with Minimum Energy algorithm.