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<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008>Anirudh Badam will present his research seminar/general
exam on Thursday </SPAN></FONT></DIV>
<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008>January 24 at 1PM in Room 402. The members of his
committee are: </SPAN></FONT></DIV>
<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008>Vivek Pai (advisor), Jennifer Rexford, and Larry
Peterson. Everyone is </SPAN></FONT></DIV>
<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008>invited to attend his talk, and those faculty wishing
to remain for the oral </SPAN></FONT></DIV>
<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008>exam following are welcome to do so. His abstract
and reading list </SPAN></FONT></DIV>
<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008>follow below.</SPAN></FONT></DIV>
<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008></SPAN></FONT> </DIV>
<DIV dir=ltr align=left><FONT face=Arial color=#0000ff size=2><SPAN
class=931363521-16012008><FONT face="Times New Roman" color=#000000
size=3>Abstract:<BR>Web Caching is the caching of web documents in order to
reduce bandwidth usage, server load and perceived download time for the web
object download. A web cache stores copies of web objects when they are accessed
through cache. The stored objects can be used to server subsequent requests. Web
caching has been extensive studied and a plethora of techniques exist today.
Solutions are based on developing good cache replacement algorithms which are
used to figure out storing what objects can provide more savings, improving file
systems which usually involves optimizing file systems for frequent additions,
deletions and accesses, etc. However, the existing solutions have scalability
problems with respect to memory. Some techniques impose lower bounds on the
amount of memory needed for a web cache. Other techniques have huge virtual
memory requirements leading to frequent swapping in low memory systems. Cache
replacement policies need metadata for better functionality and file systems
need huge lists or trees (preferably in memory) for any access to files that it
stores. This requires the physical memory to scale as an increasing function of
the number of web objects that the cache can store. This restricts not only the
number of objects that can be stored in a web cache but also the economical
affordability of the system. Hence, we propose a web caching mechanism, the
HashCache, a file system optimization approach, which uses hashing to reduce the
physical memory requirements. HashCache uses hashing techniques to map files to
the disk locations. Hashing makes the cache maintenance and usage to be mostly
memory less. Hashing mechanism also allows more flexible mechanisms for metadata
maintenance and does not impose lower bounds on the requirements of memory to
provide performance guarantees. HashCache exploits the distribution of sizes and
contents of web objects to enable an efficient usage of the secondary storage
(optimal bucket sizes). Also, with the help of experiments on an implementation
of the HashCache architecture we show the usefulness of
HashCache.</FONT><BR><BR></SPAN></FONT></DIV>1. Computer Networks by Peterson
and Davie<BR>2. Operating System Concepts by Silberschatz, Galvin and
Gagne<BR>3. P. Danzig, R. Hall, and M. Schwartz, A Case for Caching File Objects
inside Internetworks, Tech. Report CU-CS-642-93, Dept. of Computer Science,
Univ. of Colorado, Boulder, Colo., 1993. <BR>4. S. Acharya and S.B. Zdonik, An
Efficient Scheme for Dynamic Data Replication, Tech. Report CS-93-43, Dept. of
Computer Science, Brown Univ., Providence, R.I., 1993.<BR>5. M.M. Recker and
J.E. Pitkow, Predicting Document Access in Large, Multimedia Repositories, Tech.
Report VU-GIT-94-35, Graphics, Visualization, and Usability Center, Georgia
Tech, Atlanta, 1994. <BR>6. D.D. Sleator, R.E. Trajan, Amortized efficiency of
list update and paging rules, Commun. ACM, Vol. 2, p 202-208, 1985.<BR>7. D.
Wessels, K. Claffy, ICP and the Squid Web Cache, IEEE JSAC, Vol. 16, No. 3.
1998.<BR>8. H. Bahn, S.H. Noh, L. Min, K. Koh, Using Full Reference History For
Efficient Document Replacement In Web Caches, In Proc. USITS, Colorado, Boulder,
1999.<BR>9. E.P. Markatos, M.G.H. Katevenis, D. Pnevmatikatos, M. Flouris,
Secondary Storage Management For Web Proxies, In Proc Usits 99, Boulder,
Colorado, 1999. <BR>10. A. Sweeney, Scalability in the XFS File System, In Proc
USENIX 96, San Diego, California, 1996.<BR>11. L. Breslau, P. Cao, L. Fan, G.
Phillips, Scott Shenker, <SPAN class=m>Web Caching and Zipf-like Distributions:
Evidence and Implications </SPAN>, In Proc. IEEE Infocom, 1999. <BR>12 C.
Agarwal, J. L. Wolf, P. S. Yu, Caching on the World Wide Web, IEEE Tran.
Knowledge and Data Engineering, Vol. 11, No. 1, January, 1999.<BR></BODY></HTML>