Faster Web Page Allocation with Neural Networks

To maintain quality of service, some heavily trafficked Web sites use multiple servers, which share information through a shared file system or data space. The Andrews file system (AFS) and distributed file system (DFS), for example, can facilitate this sharing. In other sites, each server might have its own independent file system. Although scheduling algorithms for traditional distributed systems do not address the special needs of Web server clusters well, a significant evolution in the computational approach to artificial intelligence and cognitive engineering shows promise for Web request scheduling. Not only is this transformation - from discrete symbolic reasoning to massively parallel and connectionist neural modeling - of compelling scientific interest, but also of considerable practical value. Our novel application of connectionist neural modeling to map Web page requests to Web server caches maximizes hit ratio while load balancing among caches. In particular, we have developed a new learning algorithm for fast Web page allocation on a server using the self-organizing properties of the neural network (NN).

[1]  Matthias Grossglauser,et al.  On the relevance of long-range dependence in network traffic , 1996, SIGCOMM '96.

[2]  Micah Beck,et al.  The Internet2 Distributed Storage Infrastructure Project: An Architecture for Internet Content Channels , 1998, Comput. Networks.

[3]  Vir V. Phoha,et al.  Image recovery and segmentation using competitive learning in a layered network , 1996, IEEE Trans. Neural Networks.

[4]  Sally Floyd,et al.  Wide area traffic: the failure of Poisson modeling , 1995, TNET.

[5]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[6]  Takao Enkawa,et al.  A self‐organizing neural network approach for multiple traveling salesman and vehicle routing problems , 1999 .

[7]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[8]  Philip S. Yu,et al.  Dynamic Load Balancing on Web-Server Systems , 1999, IEEE Internet Comput..

[9]  Kwan Lawrence Yeung,et al.  Node placement optimization in ShuffleNets , 1998, TNET.

[10]  Arun Iyengar,et al.  Improving Web Server Performance by Caching Dynamic Data , 1997, USENIX Symposium on Internet Technologies and Systems.

[11]  Philip S. Yu,et al.  DNS dispatching algorithms with state estimators for scalable Web‐server clusters , 1999, World Wide Web.

[12]  A. Odlyzko,et al.  Growth of the Internet , 2002 .

[13]  Walter Willinger,et al.  On the self-similar nature of Ethernet traffic , 1993, SIGCOMM '93.

[14]  Vir Virander Phoha IMAGE RECOVERY AND SEGMENTATION USING COMPETITIVE LEARNING IN A COMPUTATIONAL NETWORK by , 1992 .

[15]  Steffen Rothkugel,et al.  Enhancing the Web's Infrastructure: From Caching to Replication , 1997, IEEE Internet Comput..

[16]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[17]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1997, TNET.

[18]  Matthias Grossglauser,et al.  On the relevance of long-range dependence in network traffic , 1999, TNET.

[19]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.