介绍了一种基于P2P的智能资源发现机制(PIRD)，该机制通过局部敏感哈希将所有属性编织成一组索引，并将其映射到结构化的P2P覆盖网络中。此外，还采用了Lempel-Ziv-Welch算法压缩属性信息以提高效率，并借助层次P2P结构搜索请求者附近的资源。
导读：
第一书店诚招秋季教材班级征订员报酬优厚
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6月10号计算机系学术报告通知，地点F楼213 ※ 来源: 同济网论坛 BBS.TONGJI.NET
美国University of Arkansas计算机系的 Shen Haiying博士于10号下午来访我系并作学术报告，顺便进行招生宣传。
****************************************************************************************************
报告题目：P2P-based Intelligent Resource Discovery in Internet-based Distributed Systems
报告人：Dr. Haiying Shen
报告时间：10号下午2：30-3：30
报告地点：F楼213
****************************************************************************************************
欢迎同学们参加，下为其报告主要内容和个人简历：
P2P-based Intelligent Resource Discovery in Internet-based Distributed Systems
Abstract
Internet-based distributed systems enable globally scattered resources to be collectively pooled and used in a cooperative manner to achieve unprecedented petascale supercomputing capabilities. Numerous resource discovery approaches have been proposed to help achieve this goal. To report or discover a multi-attribute resource, most approaches use multiple messages with one message for each attribute, leading to high overhead. Anther approach can reduce multi-attribute to one index, but is not practically effective in an environment with a large number of different resource attributes. Furthermore, few approaches are able to locate resources geographically close to the requesters, which is critical to system performance. This talk will present a P2P-based intelligent resource discovery mechanism (PIRD). It weaves all attributes into a set of indices using locality sensitive hashing, and then maps the indices to a structured P2P overlay. It further incorporates Lempel-Ziv-Welch algorithm to compress attribute information for higher efficiency. In addition, it helps to search resources geographically close to requesters by relying on a hierarchical P2P structure. PIRD significantly reduces overhead and improves the efficiency and effectiveness of resource discovery in Internet-based distributed systems. Theoretical analysis and simulation results demonstrate the efficiency and effectiveness of PIRD in comparison with other approaches. It dramatically reduces overhead and yields significant improvements on the efficiency and effectiveness of resource discovery.
Bio:
Haiying Shen received the BS degree in Computer Science and Engineering from Tongji University, China in 2000, and the MS and Ph.D. degrees in Computer Engineering from Wayne State University in 2004 and 2006, respectively. She is currently an Assistant Professor in the Department of Computer Science and Computer Engineering of the University of Arkansas. Her research interests include distributed and parallel computer systems and computer networks, with an emphasis on peer-to-peer and content delivery networks, wireless networks, resource management in cluster and grid computing, and data searching. Her research work has been published in top journals and conferences in these areas. She was a co-Chair of the RFID track in IEEE-CASE 2008, a PC member of many conferences such as ICPP and EUC. She is a member of IEEE and ACM.
[本帖最后由 xuyanwei83 于 2008-6-10 10:12 编辑 ]
个人空间加为好友PM TOP 第一书店诚招秋季教材班级征订员报酬优厚
本文转自
http://bbs.tongji.net/redirect.php?tid=571244&goto=lastpost
第一书店诚招秋季教材班级征订员报酬优厚
0顶它
6月10号计算机系学术报告通知，地点F楼213 ※ 来源: 同济网论坛 BBS.TONGJI.NET
美国University of Arkansas计算机系的 Shen Haiying博士于10号下午来访我系并作学术报告，顺便进行招生宣传。
****************************************************************************************************
报告题目：P2P-based Intelligent Resource Discovery in Internet-based Distributed Systems
报告人：Dr. Haiying Shen
报告时间：10号下午2：30-3：30
报告地点：F楼213
****************************************************************************************************
欢迎同学们参加，下为其报告主要内容和个人简历：
P2P-based Intelligent Resource Discovery in Internet-based Distributed Systems
Abstract
Internet-based distributed systems enable globally scattered resources to be collectively pooled and used in a cooperative manner to achieve unprecedented petascale supercomputing capabilities. Numerous resource discovery approaches have been proposed to help achieve this goal. To report or discover a multi-attribute resource, most approaches use multiple messages with one message for each attribute, leading to high overhead. Anther approach can reduce multi-attribute to one index, but is not practically effective in an environment with a large number of different resource attributes. Furthermore, few approaches are able to locate resources geographically close to the requesters, which is critical to system performance. This talk will present a P2P-based intelligent resource discovery mechanism (PIRD). It weaves all attributes into a set of indices using locality sensitive hashing, and then maps the indices to a structured P2P overlay. It further incorporates Lempel-Ziv-Welch algorithm to compress attribute information for higher efficiency. In addition, it helps to search resources geographically close to requesters by relying on a hierarchical P2P structure. PIRD significantly reduces overhead and improves the efficiency and effectiveness of resource discovery in Internet-based distributed systems. Theoretical analysis and simulation results demonstrate the efficiency and effectiveness of PIRD in comparison with other approaches. It dramatically reduces overhead and yields significant improvements on the efficiency and effectiveness of resource discovery.
Bio:
Haiying Shen received the BS degree in Computer Science and Engineering from Tongji University, China in 2000, and the MS and Ph.D. degrees in Computer Engineering from Wayne State University in 2004 and 2006, respectively. She is currently an Assistant Professor in the Department of Computer Science and Computer Engineering of the University of Arkansas. Her research interests include distributed and parallel computer systems and computer networks, with an emphasis on peer-to-peer and content delivery networks, wireless networks, resource management in cluster and grid computing, and data searching. Her research work has been published in top journals and conferences in these areas. She was a co-Chair of the RFID track in IEEE-CASE 2008, a PC member of many conferences such as ICPP and EUC. She is a member of IEEE and ACM.
[本帖最后由 xuyanwei83 于 2008-6-10 10:12 编辑 ]
个人空间加为好友PM TOP 第一书店诚招秋季教材班级征订员报酬优厚
本文转自
http://bbs.tongji.net/redirect.php?tid=571244&goto=lastpost
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