Scalable Server Performance and Capacity Planning

Scalable Server Performance and Capacity Planning

UCLA Extension Course 819.328

Instructor: Dr. Neil J. Gunther

Performance Dynamics Consulting
Castro Valley, California, USA

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Where? When? What? How?
UCLA Extension March 18-22, 2002 Performance analysis Register online
Room G-33 West Monday-Friday Scalability modeling at the UCLA web site
10995 Le Conte Ave. 9am-5pm 3.0 CEU credits Use number: M4429U

UCLA Extension is adjacent to the Medical Center and UCLA campus in Los Angeles, California.
Both Course Content and Registration are available via the UCLA website, or call the Program Office at (310) 825-3344 for more information.

1  Course Highlights for 2002

Below are some special topics that will be addressed in detail during the course.

1.1  Gnutella and P2P Scalability

Criticism of Gnutella network scalability has rested on the bandwidth attributes of the original interconnection topology: a Cayley tree. Trees, in general, are known to have lower aggregate bandwidth than higher dimensional topologies e.g., hypercubes, meshes and tori. Gnutella was intended to support thousands to millions of peers. Studies of interconnection topologies in the literature, however, have focused on hardware implementations which are limited by cost to a few thousand nodes. Since the Gnutella network is virtual, hyper-topologies are relatively unfettered by such constraints. We will analyze the comparative performance of several plausible hyper-topologies and compare their throughput up to millions of peers.

1.2  Threaded Applications

I will show you how to build a model of a threaded server in PDQ.
This relies on constructing what is known (in the business) as a Flow-Equivalent sub-model.
This is necessary when you are trying to determine how many threads should be available to service an application.
A comparison with commercial web application servers, such as Web Logic, will also be given.

1.3  Expressing Parallelism

There are some tricks to capturing the concept of parallelism in PDQ.
I will demonstrate how by examining some examples in detail.

1.4  Mr. Erlang and Friends

We will take a detailed look at how to model multiprocessor servers using the following queueing algorithms:

1.5  Caching Models

Models from Chapter 7 of The Practical Performance Analyst textbook on Multiprocessor Systems will be discussed in detail.

1.6  PDQ Walk-Thru

The plan is to spend time late Thursday and early Friday going through the process of model construction in PDQ.
The software itself can be downloaded at anytime.
Since this is open source, you do not need to have purchased a copy of the textbook to use PDQ.
To participate effectively in the UCLA class, you should bring a laptop computer with your favorite C compiler installed.
Better yet, download and compile the PDQ source prior to attending the class!


Footnotes:

1 But do you know why not? Come and find out.


File translated from TEX by TTH, version 2.25.
On 19 Feb 2002, 15:08.