Document Type : Original/Review Paper


1 Faculty of Information Technology, Strathmore University, Nairobi, Kenya.

2 Faculty of Information Technology, Strathmore University, Nairobi, Kenya


Variable environmental conditions and runtime phenomena require developers of complex business information systems to expose configuration parameters to system administrators. This allows system administrators to intervene by tuning the bottleneck configuration parameters in response to current changes or in anticipation of future changes in order to maintain the system’s 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. As a result, the purpose of this research is to investigate on how the capacity of probabilistic reasoning to handle uncertainty can be combined with the capacity of Markov chains to map stochastic environmental phenomena to ideal self-optimization actions. This was done using a comparative experimental research design that involved quantitative data collection through simulations of different algorithm variants. This provided compelling results that indicate that applying the algorithm in a distributed database system improves performance of tuning decisions under uncertainty. The improvement was quantitatively measured by a response-time latency that was 27% lower than average and a transaction throughput that was 17% higher than average.


[1] D. G. Sullivan, “Using probabilistic reasoning to University, automate software tuning (Doctoral Dissertation) ”, Harvard Cambridge, Massachusetts, 2003.
[2] T. Vodopive, S. Samothrakis and B. Šter, “On Monte Carlo Tree Search and Reinforcement Learning, Journal of Artificial Intelligence Research”, vol. 60, pp. 881–936, 2017, doi: 10.1613/jair.5507, 2007.
[3] T. Li, Z. Chunqiu, Y. Jiang, W. Zhou, L. Tang, Z. Liu, & Y. Huang. , “Data-Driven Techniques in Computing System Management”, ACM Computing Surveys, vol. 50, no. 3, pp. 45–88, 2017.
[4] A. O. Omondi, I. A. Lukandu and G. W. Wanyembi , “A Selection Variation for Improved Throughput and Accuracy of Monte Carlo Tree Search Algorithms”, IJCIT, vol. 7, no. 6, pp. 286–294, Jul. 2018. 
[5] A. Bousdekis, B. Magoutas, D. Apostolou and G. , “Mentzas A proactive decision making framework for condition-based maintenance”, Industrial Management & Data Systems, vol. 115, no. 7, pp. 1225–1250, 2015, doi: 10.1108/imds-03-2015-0071, 2015.
[6] U. K. Chajewska, D. Koller and R. Parr, R, “Making rational decisions using adaptive utility elicitation”, in American Association for Artificial Intelligence, 2000, pp. 363–369, 2000.
[7] G. Su, T. Chen, Y. Feng, D. S. Rosenblum and P. S. Thiagaranj, “An iterative decision-making scheme for Markov decision processes and its application to self-adaptive systems”, presented at the 19th International Conference Fundamental Approaches to Software Engineering (FASE 2016), Eindhoven, Netherlands, 2016.
[8] B. Hjørland, “Empiricism, rationalism and positivism in library and information science”, Journal of documentation, vol. 61, no. 1, pp. 130–155, 2005, doi: 10.1108/00220410510578050.
[9] T. Lemke, “Varieties of materialism, ”BioSocieties, vol. 10, no. 4, pp. 490–495, 2015, doi: 10.1057/biosoc.2015.41.
[10] P. Sanders, “Algorithm engineering: An attempt at a definition, in Efficient Algorithms: Lecture Notes in Computer Science”, S. Albers, H. Alt, and S. Naher, Eds. Heidelberg, Berlin: Springer-Verlag, 2009, pp. 321–340.
[11] R. Kitchin, “Thinking critically about and researching algorithms”, Information, Communication & Society, vol. 20, no. 1, pp. 14–29, 2017, doi: 10.1080/1369118X.2016.1154087.
[12] F. Desprez, G. Fox, E. Jeannot, K. Keahey, M. Kozuch, D. Margery, et al. , “ Supporting experimental computer science”, Argonne National Laboratory, Rocquencourt, France, Technical Memo 362, 2012.
[13] D. Van Aken, A. Pavlo, G. J. Gordon and B. Zhang, “Automatic Database Management System Tuning through Large-Scale Machine Learning”, presented at the ACM International Conference on Management of Data, Chicago, IL, USA, 2017, pp. 1009–1024, doi: 10.1145/3035918.3064029.
[14] S. W. Cheng and D. Garlan, “Stitch: A language for architecture-based self-adaptation”, Journal of Systems and Software, vol. 85, no. 12, pp. 2860–2875, 2012.
[15] J. O. Kephart and D. M. Chess, “The vision of autonomic computing”, Computer, vol. 36, no. 1, pp. 41–50, 2003.
[16] H. D. Autor, “Why are there still so many jobs? The history and future of workplace automation”, The Journal of Economic Perspectives, vol. 29, no. 3, pp. 3–30, 2015, doi: 10.1257/jep.29.3.3.
[17] J. W. Kim, S. H. Cho and I. M. Kim, “Workload-Based column partitioning to efficiently process data warehouse query”, International Journal of Applied Engineering Research, vol. 11, no. 2, pp. 917–921, 2016.
[18] S. Chaudhuri and V. Narasayya, “Self-Tuning Database Systems: A Decade of Progress”, in Proceedings of the 33rd international conference on Very Large Databases, 2007, pp. 3–14.