Title: Using economic models to capture importance policy for tuning in autonomic database management systems

Authors: Harley Boughton; Mingyi Zhang; Wendy Powley; Patrick Martin; Paul Bird; Randy Horman

Addresses: School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada ' School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada ' School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada ' School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada ' IBM Canada Software Lab, 8200 Warden Ave, Markham, ON L6G 1C7, Canada ' IBM Canada Software Lab, 8200 Warden Ave, Markham, ON L6G 1C7, Canada

Abstract: A key advantage of autonomic database management systems will be their ability to manage according to business policies. Translating high-level business policies into low-level tuning actions and parameters is, however, a non-trivial problem as there is little similarity in the metrics used for measuring database performance and business performance. These translations can be simplified, however, by having a model that reflects the business policies. In this paper, we utilise an economic model to implement importance policy as a parameter for the allocation of system resources. The relative importance of the workloads can therefore be utilised in allocating system resources, such as main memory for buffer space and shares of the CPU. We simulate the model in order to demonstrate the effectiveness of the approach. We present experiments to show the impact of the relative importance of workloads on the allocation of resources, specifically buffer area and CPU.

Keywords: autonomic computing; system management; database management systems; autonomic DBMS; policy-based management; economic models; importance policy; modelling; resource allocation; system resources; simulation; workload.

DOI: 10.1504/IJAC.2016.082027

International Journal of Autonomic Computing, 2016 Vol.2 No.2, pp.114 - 136

Available online: 31 Jan 2017 *

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