吕智慧
网络多媒体技术、流媒体内容分发技术、系统管理技术,云计算和服务计算技术。
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- 姓名:吕智慧
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学科领域:
计算机应用
- 研究兴趣:网络多媒体技术、流媒体内容分发技术、系统管理技术,云计算和服务计算技术。
吕智慧,男,1975年生,内蒙古人,博士,复旦大学计算机科学技术学院副教授,硕士生导师,复旦大学软件学院软件工程硕士指导老师,上海开放大学专聘教师。2004年7月复旦大学计算机应用技术专业博士毕业后留校工作,IEEE会员,国际服务计算学会青年科学家论坛2015中国副主席,中国计算机学会服务计算专委会委员,上海计算机学会会员,上海通信学会会员,NEINE2004-2008,ICCS2008,ICPADS2012,SCC2013-2014,BigData2014,CloudCom2014,DataCloud2014,CSE2014,UIC2015,IEEE BigData 2015, UIC2016,SCC2016, IEEE ICIoT2017国际会议程序委员会委员,IET Communication,计算机学报,电子学报等期刊审稿人. 中国信息技术标准委员会SOA标准工作组和云计算标准工作组专家成员, DMTF国际标准化组织大学联盟成员,上海内容分发网络工程技术研究中心技术委员会专家,上海计算机软件技术开发中心平台专家成员,复旦-日立创新软件技术国际合作联合实验室主要负责人,网络信息安全审计与监控教育部工程研究中心——分布式系统监控与管理研究室负责人。2009年到2010年在美国耶鲁大学计算机系作为访问学者工作。主要研究领域包括:网络多媒体技术、流媒体内容分发技术、IT系统管理技术,云架构和服务计算技术,目前主要着重于攻关支撑大数据处理的云架构平台技术,多云架构支撑的内容分发技术,云数据中心资源监控调度与安全审计技术研究和多云架构下服务部署调度技术研究。在从事科研工作近20年的时间里,积极组织和参加多项国家和省部级科研项目,在项目中积极参与组织研究与开发,攻克学术和技术难题,做出了很多创新性的成果。由于在网络多媒体领域成绩突出成果丰富,2009年12月作为第二完成人获得上海市科技进步二等奖一项“可运营管理的大规模流媒体内容分发网络研究与应用”。2015年作为第二完成人与网宿科技公司合作攻关的项目“基于网宿全球混合云架构的CDN及P2P内容优化分发平台关键技术研究及应用”荣获2015上海市科技进步奖一等奖。
在从事科研近20年时间里,先后参加若干项国家级重点项目和国际重大合作项目的研发。2002-2005年作为项目开发小组主要负责人,参加了国家“十五”重大科技攻关计划“网络教育关键技术及示范工程”中复旦大学负责的“网络教学管理系统”课题的攻关开发,顺利完成攻关;2004年作为主要负责人参加国家自然科学基金课题:“基于网格架构的丰富媒体内容分发网络研究”,已经顺利结题。2005年作为项目组技术组长参加了国家863计划软件重大专项应用类课题“Linux多媒体网络教学软件”的研发,顺利完成攻关。2006年-2007年作为副组长组织研发了上海市科学技术委员会2005重点攻关项目:面向IPv6环境的可管理运营的P2P流媒体内容分发服务系统与关键技术研究,课题顺利验收,评价为优。2009年-2011年组织研发国家发改委2008年下一代互联网CNGI应用示范项目-中国学术会议在线学术视频资源共享工程,项目顺利上线运行: 。2008年-2010年主持教育部高等学校博士学科点专向科研基金(新教师基金)项目“基于SOA和Multi-agent的复杂组合服务监控管理技术研究”。2010年-2012年作为负责人主持国家自然基金青年基金项目(新一代可控可信、网络友好的CDN-P2P混合内容分发模型与算法研究,编号60903164)。2004.4-2006.4,作为研究组长负责复旦大学与日立公司的重大国际合作项目——基于Web Service的宽带多媒体内容分发和服务系统,到2006.4完成了二期项目,两年项目经费200万。2006年4月开始了新一轮的合作,成立了复旦-日立创新软件技术国际合作联合实验室——InSTech,作为研究团队研发负责人,开展Web服务资源管理前沿课题研究。2009年3月三年任务顺利验收,作为第二负责人开始新一轮的三年合作,从事云资源监控与管理技术研发。目前复旦-日立创新软件技术联合实验室从2012年开始了新一轮合作,着重于云数据中心运营与风险管理技术研究,2015年起着重于攻关支撑大数据处理的云架构平台技术,项目累计投入经费已经超过1500万. 2013年起参与组织研发复旦-银联重点合作项目:银联云安全审计与监控系统。2011-2013年负责工信部SOA标准公益项目,作为第二完成人完成一项国家标准"基于Web服务的IT资源管理规范",已经在2014年正式出版。在参加科研项目攻关的同时,积极总结项目的科研成果,发表多篇创新性的论文,先后在IEEE、计算机研究与发展、小型微型计算机系统、LNCS等国内外权威核心期刊和重要国际会议发表了一系列论文90多篇,其中40余篇被SCI/EI核心索引,并先后参加了多次高水平的国际会议学术交流。参与申请十项专利,五项软件著作权,参与编写著作"数据库百科全书",2009年上海交通大学出版社出版。
目前主讲本科课程:程序设计,网络多媒体技术,多媒体信息技术,多媒体技术基础,Internet网络概论,网络应用系统设计,网络安全概论;主讲博士和硕士研究生课程:网络多媒体内容分发技术,网络与多媒体技术,分布式系统管理技术与方法。
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主页访问
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964
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成果数
17
【会议论文】InSTechAH: An Autoscaling Scheme for Hadoop in the Private Cloud
吕智慧, Xueying Wang, Zhihui Luv, Jie Wu, Tong Zhao, Patrick Hung
IEEE SCC 2015.:,-0001:
-1年11月30日
As a key type of applications running in cloud environments, big data analytics are being generously hosted in virtualized platforms. However, in many cloud data centers, such data analytics clusters are not used efficiently and thereby cost extra overhead. The cost-effectiveness of such data analytics clusters in cloud data centers is the key concern of this paper. In this paper, we design, implement and evaluate the InSTechAH, an autoscale schemascheme for a Hadoop system in a private cloud, which attempts to improve the resource utilization in cloud data centers as well as to maintain required quality of services. In this system, we design the multilayer node model to reduce interference from other services by automatically scaling the clusters according to the autoscale algorithm we introduced. We then build the resource scheduling model which use prediction based scheduling method to reduce the cost brought by scaling. We evaluate our schemascheme partly on a real data trace and partly on simulation, with Hadoop as the parallel data analytics frameworks and OpenStack as the cloud management architecture, to show the efficiency of InSTechAH system.
autoscale, cost effectiveness, Hadoop, Cloud computing, Private Cloud, Openstack, data center
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131浏览
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【期刊论文】Web Services Standard-based System Resource Management Middleware Model, Scheme and Test
吕智慧, Zhihui Lu, Jie Wu and Patrick Hung
International Journal of Services Computing,-0001,():
-1年11月30日
Traditionally, system resource management software is tightly tied with managed IT resources through their specific manageability interfaces. Applying Web Services technology to the field of system resource management is a reasonable way to loosen this tie. In this paper, we discuss the implementation of SOA principals to the system resource management field, and then use WSDM standard and WS-Management standards to design and realize the resource management middleware model. We illustrate how Web Services management standards are mapped and integrated with existing management interfaces, such as JMX, WMI, and so on. As part of our experimental work, we discuss MUSE-JMX and Wiseman-WMI based system management implementation schemes, including Manager Layer, Gateway Layer, and Resource Agent Layer, and then we analyze the experiments results. After that, from our research experiences and related surveys, we analyze the gap between WS-Management and real management fields and design some feasible solution for these gaps. Finally, we discuss the prospective research direction and challenges in this field.
System Resource Management,, Web Services,, SOA,, WSDM,, WS-Management,, Wiseman,, JMX,, WMI
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110浏览
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50下载
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【期刊论文】An Analysis and Comparison of cloud Data Center Energy-efficient Resource Management Technolog
吕智慧, Zhihui Lu, Soichi Takashige, Yumiko Sugita, Tomohiro Morimura, and Yutaka Kudo
International Journal of Services Computing,-0001,():
-1年11月30日
Nowadays, cloud data centers began to support more and more popular online services such as web search, ecommerce, social networking, video on demand and software as a service (SaaS), so the massive scale of data centers brings the challenges of energy efficiency. Therefore, the concept of an energy-efficient Green data center has been proposed. To build an energy-efficient cloud data center, cloud data center resource dynamic provisioning and consolidation technology is involved. In this paper, we fi rst provide a survey of current indus try and academic efforts on cloud data center energy-efficient management technology, focusing on the cloud data center resource dynamic provisioning technology and resource consolidation technology. We first focus on an analysis and comparison of cloud resource predictive dynamic provisioning technology. We analyze and discuss the mai n resource prediction methods and models, including basic models, feedback based models and multi ple time series models. We describe the relationship between these categories as well as the characteristics of the models. After that, we analyze and compare Cloud resource reactive dynamic provisioning technology. And then we analyze cloud resource consolidation technology. Furthermore, we also give a prospect on cloud resource management technology standardization trends. Lastly, we analyze the prospective research direction.
Cloud Computing,, IDC,, Cloud D ata Center,, Vi rtual Machine-VM,, Resource Consolidation,, Energy-efficient management technology,, Resource Prediction,, Resource Management,, VM Migration
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54浏览
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吕智慧, Jie Bao, Zhihui Lu, Jie Wu, Shiyong Zhang, Yiping Zhong
IEEE/IFIP Network Operations and Management Symposium, NOMS 2014.:,-0001:
-1年11月30日
Resources dynamical allocation and management is always an important feature in cloud computing. Auto Scale allows users to scale their cloud resources capacity according to elastic loads timely, which has been widely used in mature public cloud. For private cloud, there are some different features from public cloud. It is more flexible to use Auto Scale technique to provide QoS guarantees and ensure system health. In this paper, we design a novel Auto Load-aware Scale scheme for private cloud environment. We describe scale in and scale out strategy based on prediction algorithm. We implement our scheme on OpenStack platform. Both simulation and experiments are carried out to evaluate our work. The experiments show that our scheme has better performance in resource utilization while providing high SLA levels.
Cloud computing,, Auto scale,, Prediction,, Dynamic scalability,, OpenStack,, Resource management
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65浏览
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【会议论文】VMRaS: A Novel Virtual Machine Risk Assessment Scheme in the Cloud Environment
吕智慧, SiFan Liu, Jie Wu, ZhiHui Lu, Hui Xiong
2013 IEEE 10th International Conference on Services Computing.:,-0001:
-1年11月30日
Security issues of cloud computing are always being concerned by customers. Research on a virtual machine’s quantitative or qualitative value of risk will be a good start to know the security status of a cloud data center. Risk assessment is a solution for really understanding security procedures of the network and information system, analyzing where security threats come from and how much loss the risk can cause. By means of the combination of risk assessment with cloud computing, we can assess the risk value of virtual machines, and the security of data center can be ensured by administrator who has ability to quickly locate the risk points and easily control and reduce the risks. In this paper, we present VMRaS (a novel virtual machine risk assessment scheme in the cloud environment), a scheme that can assess the risk of a virtual machine. First, we introduce the process, criteria and algorithms of risk assessment. And then we present the design and implementation of VMRaS. We evaluate a prototype of VMRaS which is deployed on an OpenStack-based cloud computing resource management platform. The result shows that VMRaS works well in the OpenStack-based cloud environment.
security, risk assessment, cloud computing, virtual machine, cloud data center
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38浏览
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83下载
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【会议论文】PSRPS: A Workload Pattern Sensitive Resource Provisioning Scheme for Cloud
吕智慧, Feifei Zhang, Jie Wu, ZhiHui Lu
2013 IEEE 10th International Conference on Services Computing.:,-0001:
-1年11月30日
On-demand resource provisioning is with great challenge in cloud systems. The key problem is how to learn about the future workload in advance to help determine resource allocation. There are various prediction models developed to predict the future workload. The major problem of pre vious researches is that they assume that application workload has static pattern. In practice, so many application workloads have hybrid dynamic pattern overtime. To achieve high prediction accuracy, we find that it’s essential to detect both workload pattern stage and the changes in the model parameters. In this paper, we present a Pattern Sensitive Resource Provisioning Scheme, named PSRPS. It can recognize application workload patterns and choose suitable prediction models for prediction online. Besides, when there is maladjustment in prediction models, PSRPS can switch prediction models or adjust the parameters of the model by itself to adaptively to guarantee prediction accuracy.
resource management, resource provisioning scheme, cloud, prediction model, periodic eries, non-periodic series, error correction
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【会议论文】CloudStreamMedia: A Cloud Assistant Global Video on Demand Leasing Scheme
吕智慧, Da Deng, Zhihui Lu*, Wei Fang, Jie Wu
2013 IEEE 10th International Conference on Services Computing.:,-0001:
-1年11月30日
Cloud computing is a new computing paradigm that takes all resources as services, and it is not only agile, but also scalable. With the development of cloud computing, video on demand has become one of the most popular applications over the Internet. Currently, there is a trend of using cloud data centers and virtualization technologies to expand large-scale video streaming services with higher quality and lower expense. In this paper, we present CSM (Cloud Stream Media), a scheme that books the minimum resources from global data centers to match its demand and dynamically adjusts all resources to effectively meet the users’ requests and guarantee a certain kind of quality of service, thus enhances the utilization and decreases the cost. CSM first predicts the stream media’s future demand and data center’s workload by using ARIMA model, and then performs a locality-aware resource booking (LARB) algorithm to lease the necessary resource from globalized cloud service providers in a long time. In order to handle prediction inaccuracy and the short-term demand peeks, CSM also introduces an inaccurate prediction handle strategy and performs auto scaling. We evaluate our scheme by combining both real world data and simulation. The results show good accuracy of our prediction and about 20% cut of total cost.
streaming media, video on demand, cloud resource prediction, resource leasing, cloud computing
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42浏览
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【期刊论文】An Analysis and Comparison of CDN-P2Phybrid Content Delivery System and Model
吕智慧
,-0001,():
-1年11月30日
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47浏览
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255下载
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【期刊论文】RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center
吕智慧
,-0001,():
-1年11月30日
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69浏览
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493下载
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【期刊论文】CPDID: A Novel CDN-P2P Dynamic Interactive Delivery Scheme for Live Streaming
吕智慧
,-0001,():
-1年11月30日
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71浏览
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234下载
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