A task scheduling strategy with energy optimization for cloud rendering systems

被引：1
|
作者：
Li Q. [1 ]
Wu W. [1 ]
Cao Y. [1 ]
Wang L. [1 ]
机构：
[1] School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an
来源：
Wu, Weiguo | 1600年 / Xi'an Jiaotong University卷 / 50期
关键词：
Cloud rendering; Energy consumption model; Task scheduling;
D O I：
10.7652/xjtuxb201602001
中图分类号：
学科分类号：
摘要：
A task scheduling strategy with optimized energy consumption for cloud rendering systems is proposed to solve the problem that the mismatching task scheduling on render nodes causes a great waste of energy consumption. A rendering task energy consumption model is presented to describe formally the overall energy consumption of a system and takes both the idle and the task running energy consumptions of each node into account. The optimization object is to reduce the overall energy consumption of the system, and the strategy divides the task scheduling sequence into subsequences based on the non-dependence characteristic among rendering tasks. The simulated annealing ideology is used to optimize the scheduling of the subsequence tasks, to improve the utilization ratio of the nodes and to reduce the idle energy consumption of nodes so that the energy consumption for the overall system is reduced. Moreover, the strategy adopts a way of space in time to reduce the time complexity by using a matrix to store the energy of subsequence tasks. Experimental results and comparisons with the FIFO algorithm and EMRSA (energy-aware MapReduce scheduling) algorithm in a multi jobs measurement show that the energy optimization performance of the proposed strategy has improved about 43.4% and 6.7%, respectively, that is, the proposed strategy effectively reduces the overall energy consumption for cloud rendering systems. Moreover, the proposed strategy possesses better expansibility. It can be concluded that the proposed strategy can improve the energy efficiency and overall performance of cloud rendering systems. © 2016, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
引用
收藏
页码：1 / 6
页数：5
相关论文
共 13 条
  • [1] Baharon M.R., Shi Q., Llewellyn-Jones D., Et al., Secure rendering process in cloud computing, Proceedings of the 2013 11th Annual International Conference on Privacy, Security and Trust, pp. 82-87, (2013)
  • [2] Luo L., Wu W., Zhang F., Energy modeling based on cloud data center, Journal of Software, 25, 7, pp. 1371-1387, (2014)
  • [3] Li C., Zhou R., Li T., Enabling distributed generation powered sustainable high-performance data center, Proceedings of the 19th IEEE International Symposium on High Performance Computer Architecture, pp. 35-46, (2013)
  • [4] Li K., Energy efficient scheduling of parallel tasks on multiprocessor computers, Journal of Supercomputing, 60, 2, pp. 223-247, (2012)
  • [5] Lee Y.C., Zomaya A.Y., Energy efficient utilization of resources in cloud computing system, Journal of Supercomputing, 60, 2, pp. 268-280, (2012)
  • [6] Zong Z., Manzanares A., Ruan X., Et al., EAD and PEBD: two energy-aware duplication scheduling algorithms for parallel tasks on homogeneous clusters, IEEE Transactions on Computers, 60, 3, pp. 360-374, (2011)
  • [7] Maheshwari N., Nansuri R., Varma V., Dynamic energy efficient data placement and cluster reconfiguration algorithm for MapReduce framework, Future Generation Computer System, 28, 1, pp. 119-127, (2012)
  • [8] Pietri I., Sakellariou R., Energy-aware workflow scheduling using frequency scaling, Proceedings of the 2014 43rd International Conference on Parallel Processing Workshops, pp. 104-113, (2014)
  • [9] Zhu X., He C., Wang J., Et al., An elastic energy-aware scheduling strategy for heterogeneous computing systems, Chinese Journal of Computers, 35, 6, pp. 1313-1326, (2012)
  • [10] Tan Y., Zeng G., Wang W., Policy of energy optimal management for cloud computing platform with stochastic tasks, Journal of Software, 23, 2, pp. 266-278, (2012)
12