科技通报
科技通報
과기통보
BULLETIN OF SCIENCE AND TECHNOLOGY
2014年
10期
52-54
,共3页
任务调度%云平台%特征尺度分解
任務調度%雲平檯%特徵呎度分解
임무조도%운평태%특정척도분해
task scheduling%cloud platform%characteristic scale decomposition
云平台多处理器的任务调度是解决庞大用户群中庞大任务量和数据量的关键,云平台中任务调度的计算性能影响整个系统的运行效率。提出一种基于任务信息流特征尺度谱分析的开销折减算法,采用可分解特征下的云平台任务同步开销折减算法,通过构建复杂通道下多处理器运行环境下的云平台任务调度整合基础模型,进行任务节点信息表征,使用GSM、TD-SCDMA、TD-LTE和WLAN,实现多处理器和多通道任务调度,计算各任务匹配资源的效率,得到资源相似度,基于任务信息流特征尺度谱分析方法计算每个任务的特征尺度,得到尺度优化的开销折减目标函数。仿真结果表明,采用该算法进行任务调度,具有较高的执行效率,CPU利用率高,网络开销折减幅度较高,提高了数据通信效率。
雲平檯多處理器的任務調度是解決龐大用戶群中龐大任務量和數據量的關鍵,雲平檯中任務調度的計算性能影響整箇繫統的運行效率。提齣一種基于任務信息流特徵呎度譜分析的開銷摺減算法,採用可分解特徵下的雲平檯任務同步開銷摺減算法,通過構建複雜通道下多處理器運行環境下的雲平檯任務調度整閤基礎模型,進行任務節點信息錶徵,使用GSM、TD-SCDMA、TD-LTE和WLAN,實現多處理器和多通道任務調度,計算各任務匹配資源的效率,得到資源相似度,基于任務信息流特徵呎度譜分析方法計算每箇任務的特徵呎度,得到呎度優化的開銷摺減目標函數。倣真結果錶明,採用該算法進行任務調度,具有較高的執行效率,CPU利用率高,網絡開銷摺減幅度較高,提高瞭數據通信效率。
운평태다처리기적임무조도시해결방대용호군중방대임무량화수거량적관건,운평태중임무조도적계산성능영향정개계통적운행효솔。제출일충기우임무신식류특정척도보분석적개소절감산법,채용가분해특정하적운평태임무동보개소절감산법,통과구건복잡통도하다처리기운행배경하적운평태임무조도정합기출모형,진행임무절점신식표정,사용GSM、TD-SCDMA、TD-LTE화WLAN,실현다처리기화다통도임무조도,계산각임무필배자원적효솔,득도자원상사도,기우임무신식류특정척도보분석방법계산매개임무적특정척도,득도척도우화적개소절감목표함수。방진결과표명,채용해산법진행임무조도,구유교고적집행효솔,CPU이용솔고,망락개소절감폭도교고,제고료수거통신효솔。
Multi-processors task scheduling in cloud platform is the key to solve the huge user group in the huge task and data quantity, it influences the running efficiency of the whole system and computing performance of task scheduling in cloud platform. An overhead reduction algorithm based on task information flow characteristic spectrum analysis is pro-posed. And the task scheduling integration synchronization algorithm of the cloud platform multiprocessor is proposed based on feature scale decomposition, The decomposition characteristics of the cloud platform task integration and synchro-nization algorithm, through the cloud platform task scheduling environment construction of complex multi processor chan-nel integration under the base model, task node information representation, using GSM, TD-SCDMA, TD-LTE and WLAN, realizes the multi processor and multi channel scheduling, each task efficiency calculation of matching resources, to obtain the resource similarity, the characteristic scale task information flow characteristic scale is calculated for each task analysis method based on spectrum, obtained the scale optimization overhead reduction target function. The simulation results show that, it has high efficiency and high utilization rate of CPU, the network overhead is less, the overall performance is better than the traditional algorithm.