大数据与信息工程学院学术报告:Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing
发布时间:2017-06-06 浏览次数:

    时值初夏,大数据之都贵阳的大数据产业发展正盛,贵州大学大数据与信息工程学院迎来了湖南大学信息科学与工程学院国家“千人计划”特聘教授李克勤教授到校讲学。

    李克勤现为湖南大学信息科学与工程学院国家“千人计划”特聘教授、博士生导师、院学术委员会主席、超级计算与云计算研究所所长。他1985年毕业于清华大学,获计算机科学学士学位;1990年毕业于休斯顿大学,获计算机科学博士学位。之后受聘于纽约州立大学,历任助理教授、副教授、正教授;2009年晋升为纽约州立大学讲席教授;2011年被聘请为清华大学高智讲座教授;2012年受聘为中组部国家“千人计划”特聘教授。目前研究课题集中在并行计算与高性能计算、分布式计算、高能效计算和通信、异构计算系统、云计算、大数据计算、CPU-GPU混合协同计算、多核计算、存储和文件系统、无线通讯网络、传感器网络、对等文件共享系统、移动计算、服务计算、物联网和信息物理系统等。在学术著作、研究期刊、和国际会议上共发表论文490篇以上(其中IEEE/ACM Transactions/Journals论文近100篇,SCI期刊论文260篇)。他在网格网络的处理器分配和作业调度以及光网络上的并行计算方面的开创性研究具有广泛和深远的影响,为他带来了崇高的学术声誉。他曾多次荣获国际学术会议最佳论文奖。他多次担任国际学术会议主席,并且担任2017年召开的第7届IEEE国际云计算和服务计算年会和第13届国际自然计算及模糊系统和知识发现会议的主席。他主持国家自然科学基金委员会的重点项目“面向激光聚变模拟的大规模异构众核系统可扩展并行算法与优化方法”, 主持国家自然科学基金委和新加坡国家研究基金会数据科学合作研究项目“面向列车故障检测的深度学习模型及其异构并行处理技术”,并且承担科技部863重大项目“高性能计算环境应用服务优化关键技术”和“基于内存计算的并行处理系统研究与开发”,同时在“大规模异构并行系统的高效能调度理论与方法”领域带领湖南省自然科学基金创新研究群体。他具有重大国际声誉和影响,在世界范围内有50个以上合作研究团队和超过450位合作研究人员。他是 IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, IEEE Transactions on Services Computing, IEEE Transactions on Sustainable Computing 等著名和顶级学术刊物的编委。他是IEEE Fellow以及IEEE计算机学会Fellow遴选委员会委员。

本次学术报告题目:

       Quantitative Modeling and Analytical Calculation of
                  Elasticity in Cloud Computing

                        时间:2017年6月8日9:30-11:00

                        地点:贵州大学新校区崇理楼607会议室

Abstract:
    Elasticity is a fundamental feature of cloud computing and can be considered as a great advantage and a key benefit of cloud computing. One key challenge in cloud elasticity is lack of consensus on a quantifiable, measurable, observable, and calculable definition of elasticity and systematic approaches to modeling, quantifying, analyzing, and predicting elasticity. Another key challenge in cloud computing is lack of effective ways for prediction and optimization of performance and cost in an elastic cloud platform. Our research makes the following significant contributions. First,we present a new, quantitative, and formal definition of elasticity in cloud computing, i.e., the
probability that the computing resources provided by a cloud platform match the current workload. Our definition is applicable to any cloud platform and can be easily measured and monitored.Furthermore, we develop an analytical model to study elasticity by treating a cloud platform as a queueing system, and use a continuous-time Markov chain (CTMC) model to precisely calculate the elasticity value of a cloud platform by using an analytical and numerical method based on just a few parameters, namely, the task arrival rate, the service rate, the virtual machine start-up and shut-down rates. In addition, we formally define auto-scaling schemes and point out that our model and method can be easily extended to handle arbitrarily sophisticated scaling schemes. Second, we apply our model and method to predict many other important properties of an elastic cloud computing system, such as average task response time, throughput, quality of service, average number of VMs, average number of busy VMs, utilization, cost, cost-performance ratio, productivity, and scalability. In fact, from a cloud consumer’s point of view, these performance and cost metrics are even more important than the elasticity metric. Our study in this talk has two significance.On one hand, a cloud service provider can predict its performance and cost guarantee using the results developed in this talk. On the other hand, a cloud service provider can optimize its elastic scaling scheme to deliver the best cost-performance ratio. To the best of our knowledge, this is the first work that analytically and comprehensively studies elasticity, performance, and cost in cloud computing. Our model and method significantly contribute to the understanding of cloud elasticity and management of elastic cloud computing systems.