Probabilistic Reasoning and Markov Chains as Means to Improve Performance of Tuning Decisions under Uncertainty

The variable environmental conditions and runtime phenomena require the developers of complex business information systems to expose the configuration parameters to the system administrators. This allows them to intervene by tuning the bottleneck configuration parameters in response to the current changes or in anticipation of the future changes in order to maintain the system performance at an optimum level. However, these manual performance tuning interventions are prone to error and lack of standards due to fatigue, varying levels of expertise, and over-reliance on inaccurate predictions of future states of a business information system. The purpose of this research work is to investigate that how the capacity of probabilistic reasoning to handle uncertainty can be combined with the capacity of Markov chains to map the stochastic environmental phenomena to ideal self-optimization actions. This is done using a comparative experimental research design that involves quantitative data collection through simulations of different algorithm variants. This provided compelling results, which indicate that applying the algorithm to a distributed database system improves the performance of tuning decisions under uncertainty. The improvement is measured quantitatively by a response-time latency 27% lower than the average and a transaction throughput 17% higher than the average.