面向能耗优化的云渲染系统任务调度策略

李谦; 伍卫国; 曹莹方; 王蕾

doi:10.7652/xjtuxb201602001

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面向能耗优化的云渲染系统任务调度策略

更新时间：2025-07-09

- 面向能耗优化的云渲染系统任务调度策略
- A Task Scheduling Strategy With Energy Optimization for Cloud Rendering Systems
- 西安交通大学学报 2016年50卷第2期页码：1-6
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2015-10-12。作者简介: 李谦(1986—),男,博士生;伍卫国(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61202041,91330117);国家高技术研究发展计划资助项目(2012AA01A306,2014AA01A302)
- DOI：10.7652/xjtuxb201602001
  中图分类号： TP399
- 网络首发：2016-02-10，
  
  纸质出版：2016
- 稿件说明：
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李谦, 伍卫国, 曹莹方, 等. 面向能耗优化的云渲染系统任务调度策略[J]. 西安交通大学学报, 2016,50(2):1-6.

A Task Scheduling Strategy With Energy Optimization for Cloud Rendering Systems[J]. 2016, 50(2): 1-6.
李谦, 伍卫国, 曹莹方, 等. 面向能耗优化的云渲染系统任务调度策略[J]. 西安交通大学学报, 2016,50(2):1-6. DOI： 10.7652/xjtuxb201602001.

A Task Scheduling Strategy With Energy Optimization for Cloud Rendering Systems[J]. 2016, 50(2): 1-6. DOI： 10.7652/xjtuxb201602001.

摘要

针对云渲染系统中由于渲染节点与任务不匹配调度而带来的能耗浪费问题

提出一种通过任务调度方式来优化系统能耗的策略。为了形式化描述系统的整体能耗

综合考虑节点空闲能耗和任务运行能耗

建立渲染任务能耗模型; 以降低系统总体能耗为优化目标

根据渲染任务之间无依赖性的特点

将任务调度序列拆分成子序列

利用模拟退火思想

通过优化子序列任务调度提高节点利用率、减少节点空闲能耗

以此降低系统全局任务的能耗; 采用矩阵存储子序列任务的能耗

以空间换时间的方式降低策略的时间复杂度。实验结果表明:该策略在多渲染作业环境中能耗优化效果比先进先出算法提升了43.4%

比能耗感知的调度算法提升了6.7%

能够有效降低云渲染系统的总体能耗

同时具有良好的扩展性

使云渲染系统的能耗效率和整体性能得到提升。

Abstract

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.

关键词

Keywords

references

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