You might have noticed that when you do a transaction in a bank, you usually see a single line being served by 2 or more tellers. You must have perceived that this technique of lining up customers is better than when each teller has its own line. This will lessen the chance of you being stuck for a long time when the guy before you takes a long time to complete transaction.
In computer systems, you can view the customers as tasks to be executed by 2 or more processors. Each processor will pick from a common line, a task that it will execute. In this article, we will analyze the performance of symmetric multiprocessing using continuous markov chain analysis. We will also be using the tool available from www.extremecomputing.org to help us compute the probabilities.
Continue reading Computing Performance of Symmetric Multi-Processing Scheduling
In the last article, we computed the probabilities associated with the states in a continuous markov chain model using balance equations. The computation, although elementary, was tedious and distracts us from the real problem, which is understanding the performance of a queueing model. Fortunately, there is an online tool we can use in order to speed up the computation of continuous markov chains. This tool can be found in the site http://www.extremecomputing.org. This site contains numerous solvers that can be of interest to the computational scientists. However, the tool we are interested in is the Continuous Markov Chain solver which can be accessed in this link.
Continue reading Computing Continuous Markov Chains Using Extreme Optimal Solver