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Hi Greg,
Thanks a lot for providing this information.
I will pick up this technical report and read it soon, and will
probably adopt some good features of their algorithms and add them
into lvs.
Thanks,
Wensong
"Burd, Greg" wrote:
>
> I happened across the following doing some research on LB algorithms for Web
> and Application Servers. I believe this algorithm is the foundation of the
> IBM WebSphere Application Server (via second hand information). You may
> find it interesting.
>
> http://liinwww.ira.uka.de/cgi-bin/bibshow?e=Ufdisfqpsut0ODTUSM0VUFYBT%60DT/v
> ojrvf%7d217252:&r=bibtex
>
> regards,
>
> -greg
>
> ---------------------------------------------
> @TechReport{UTEXAS_CS//CS-TR-98-20,
> type = "Technical Report",
> number = "CS-TR-98-20",
> institution = "University of Texas, Austin",
> title = "Interpreting Stale Load Information",
> month = oct # " 1,",
> year = "1998",
> bibdate = "November 24, 98",
> url = "ftp://ftp.cs.utexas.edu/pub/techreports/tr98-20.ps.Z";,
> author = "Michael Dahlin",
> abstract = "In this paper we examine the problem of balancing load
> in a large-scale distributed system when information
> about server loads may be stale. It is well known that
> sending each request to the machine with the apparent
> lowest load can behave badly in such systems, yet this
> technique is common in practice. Other systems use
> round-robin or random selection algorithms that
> entirely ignore load information or that only use a
> small subset of the load information. Rather than risk
> extremely bad performance on one hand or ignore the
> chance to use load information to improve performance
> on the other, we develop strategies that interpret load
> information based on its age. Through simulation, we
> examine several simple algorithms that use such load
> interpretation strategies under a range of workloads.
> Our experiments suggest that by properly interpreting
> load information, systems can (1) match the performance
> of the most aggressive algorithms when load information
> is fresh, (2) outperform current algorithms by as much
> as 60\% when information is moderately old, (3)
> significantly outperform random load distribution when
> information is older still, and (4) avoid pathological
> behavior even when information is extremely old.",
> }
>
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