面向绿色云计算的节能型资源整合和任务调度关键技术的研究

项目名称： 面向绿色云计算的节能型资源整合和任务调度关键技术的研究

项目编号： No.61472192

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 徐小龙

作者单位： 南京邮电大学

项目金额： 85万元

中文摘要： 云数据中心的高能耗及其引起的环境问题引起了广泛的关注。云数据中心中能量的不合理组织、资源的低效管理与任务的无序执行是造成其高能耗、低能效的主要原因。本项目将在细致分析相关工作以及目前云数据中心运作模式导致的能耗问题的基础上，构建科学的云数据中心能耗模型与绿色云计算模型，然后结合自身的研究优势，从资源整合和任务调度的角度针对面向绿色云计算的资源配置算法、任务调度策略及数据部署与紧凑机制等关键技术展开深入研究，在保证系统QoS和SLA的前提下，使系统的各个环节运行有序化，有效、合理地降低云数据中心的能耗。本项目还针对绿色云计算的科学评价机制展开研究，并构建逼真的绿色云计算实验测试平台、原型系统及典型应用示范，以有效验证和优化本项目提出的关键技术的性能、可行性与实际应用价值。针对绿色云计算模型及面向绿色云计算的资源整合和任务调度关键技术展开深入细致的研究，对于节能减排具有重要意义和价值。

中文关键词： 云计算；绿色计算；资源整合；任务调度；数据部署

英文摘要： High-energy consumption in cloud data centres and its effects on the environment raised widespread concerns around the world. The reasons for the high-energy consumption are because of the unorganised distrition of energy, low efficinet managements of resources, and less organised tasks executions. The objectives of this project are to establish an energy-consumption model and a green cloud computing model based on detailed analysis of operations of data centres and energy consumption owing to the operation, and to develop algorithms to alloate resources, schedule tasks execution, and distribute and aggregate data towards green cloud computing, thus makeing the components of cloud system operate orderly, and reducing of energy consumption efficiently and reasonably under the guarantee of system's Quality of Service (QoS) and Service Level Agreement (SLA). In addition, an assessment method will be studied in this project to evaluate the green cloud computing, and an experiment plaftorm will be developed to test the performance and feasibility of green cloud computing technologies proposed in this project. Pilot applications will also be implemented in this platform to test its practical applications. The exclusive research efforts in modelling green cloud computing and resource allocation and tasks execution scheduling will play an important role in reducing energy consumption and emission of CO2 to the globe.

英文关键词： cloud computing;green computing;resource integration;task scheduling;data deployment