The Millennium Performance Problems

The Millennium Performance Problems are:

1. Performance Visualization:
The idea here is to find ways of representing performance data that are a better impedance match for our cognitive computer (our brain). One role model is the techniques used in so-called Scientific Visualization where physicists and biologists have learned to use things like special GUIs and animation to represent complex data in ways that help them solve problems. Why should they have all the fun?

2. Self-instrumented Applications:
Object-oriented programming has been promoted as a good thing primarily for reasons of reusability. If people are going to re-use objects, how about they come with their own instrumentation? This really should be part of the object library so that a programmer need nevre be concerned with adding such code. Then I, as the performance analyst would have the ability to turn objects on selectively and thereby trace paths through application code to find bottlenecks, even on production systems.

3. The Von Neummann Bottleneck:
An efficient way to compute something on a machine is to do more than one thing at once. The technical term is parallelism. Unfortunately, despite a lot of intense effort over the last two decades, general-purpose parallelism remains a holy grail of performance analysis. One reason for this barrier seems to stem from the influential success of early electronic computer designers such as Alan Turing and John von Neumann. The fundamental paradigm of sequential programming and operation seem to be almost impossible to break away from in the modern digital computer. But there is an obvious role model for a non-von computer architecture: our brain. This has led to the idea neural networks as way of achieving a higher degree of parallelism. Quantum computers are another.

4. Performance Analysis of the Internet:
The results of analyzing Internet packet traces 15 years ago at Bellcore (now Lucent) showed that long-term correlations can persist over several orders of magnitude in time. Packet arrivals are not Poisson, and service times are not constant. In other words, all the conventional queueing theory techniques near and dear to our hearts as performance analysts, are no longer valid at the packet level. How are we to model the Internet? Perhaps we need to think big. The climatologists use things like the Earth Simulator to address complex questions about global warming. Maybe we need an Internet Simulator of similar scale?

5. Performance Analysis of Quantum Computers:
Quantum Computers are probably a long way off, but quantum communication devices are already here. The only reason you are not aware of them is because they are expensive and therefore a specialty item for institutions like banks. In the next 3 to 5 years, I believe these things will reach commodity prices and will therefore become more ubiquitous. I am also working on these technologies now. The question for us is, How will they affect performance?

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