Teaching Performance Modeling in the Era of 140characters Information

It is not easy to state the birthdate of Performance Modeling (PM). On April 1971, a workshop on System Performance Evaluation was held at Harvard University. Richard Muntz was the chairman of the session “Queueing Theoretic Models”. In that session, Jeffrey Buzen presented “Analysis of system bottlenecks using a queueing network model”. In the 70s, some groups were founded to work on the computer performance modeling. The National Bureau of Standards organized several task groups and the Computer Performance Evaluation Users Group collected people “from many United States Governmental agencies involved in various phases of this field … a number of academicians as well as analysts from business and industry working in this area, and this gave rise to the formation within the ACM of SIGME [Special Interest Group in Measurement and Evaluation] which is currently known as SIGMETRICS.” In 1973 the International Federation for Information Processing founded the Working Group 7.3 Computer System Modelling and its International Symposium on Computer Performance Modeling, Measurement, and Evaluation started to take place. More difficult is to go back to the first courses in general Performance modeling and prediction. Definitely, in the 80s the PM area reached its peak and relative courses were taught in some universities for some decades. In the first years of 2000, some of these general PM courses started to disappear while specific contents still remained in courses relative to applications as “tools” for that particular area. A question naturally arises: is it no more time to teach the modelling principles and basic methodologies? Is it time to just use the techniques in specific domains? The author has not sure answers, but some doubts. Starting from a close examination of the state of the art of PM courses in the main Universities, we try to give some food for thought about the role of the education, the meaning of knowledge and information, their difference and the importance of criticism to face with incoming changing challenges

An approximate mean value analysis approach for system management and overload control

Blocking is the phenomenon where a service request is momentarily stopped, but not lost, until the service becomes available again. Despite its importance, blocking is a difficult phenomenon to model analytically, because it creates strong inter-dependencies in the systems components. Mean Value Analysis (MVA) is one of the most appealing evaluation methodology since its low computational cost and easy of use. In this paper, an approximate MVA for Bloking After Service is presented that greatly outperforms previous results. The new algorithm is obtained by analyzing the inter-dependencies due to the blocking mechanism and by consequently modifying the MVA equations. The proposed algorithm is tested and then applied to a capacity planning and admission control study of a web server system

QRF: An Optimization-Based Framework for Evaluating Complex Stochastic Networks

The Quadratic Reduction Framework (QRF) is a numerical modeling framework to evaluate complex stochastic networks composed of resources featuring queueing, blocking, state-dependent behavior, service variability, temporal dependence, or a subset thereof. Systems of this kind are abstracted as network of queues for which QRF supports two common blocking mechanisms: blocking-after-service and repetitive-service random-destination. State-dependence is supported for both routing probabilities and service processes. To evaluate these models, we develop a novel mapping, called Blocking-Aware Quadratic Reduction (BQR), which can describe an intractably large Markov process by a large set of linear inequalities. Each model is then analyzed for bounds or approximate values of performance metrics using optimization programs that provide different levels of accuracy and error guarantees. Numerical results demonstrate that QRF offers very good accuracy and much greater scalability than exact analysis methods

Analysis of blocking networks with temporal dependence

In this paper we estend the class of MAP queueing networks to include blocking models. We consider two different blocking mechanisms: Repetitive Service-Random Destination and Blocking After Service. We analyze the Markov process underlying the MAP queueing network and propose a methodology based on a partition of the state space into “marginal state spaces”. By using this partition, we prove a set of “partial” balance equations that relates blocking performance indexes. The proposed methodology can be a sound framework to define approximate solution methods for MAP queueing networks with blocking