Solutions to optimization problems in resource constrained systems

This talk explores topics that relate to methods and techniques applicable for solving optimization problems that emerge from resource constrained systems. It addresses both deterministic problems, characterized by crisp decision variables, and stochastic problems, where decisions are described by probability distributions.

The presentation will include an overview of the most popular solution methods and two novel methodologies: Randomized Search method for solving hard non-linear, non-convex combinatorial problems and generalized stochastic Petri net (GSPN) based framework for stochastic problems.

The second part of the talk focuses on solutions of exact problems. First, we address a problem of energy efficient scheduling and allocation in heterogeneous multi-processor systems. The solution uses GSPN framework to address the problem of scheduling and allocating concurrent tasks when execution and arrival times are described by probability distributions. Next, we present a Gaussian mixture model vector quantization technique for estimating power consumption in virtual environments. The technique uses architectural metrics of the physical and virtual machines (VM) collected dynamically to predict both the physical machine and per VM level power consumption.

Curriculum Vitae

Kresimir Mihic is a Senior Researcher in Modeling, Simulation and Optimization group, Oracle Labs. His work is in the area of optimization of complex systems, with specific interest in discrete optimization techniques and applications thereof on non-linear, non-convex multi-objective problems, for static and dynamic cases. Kresimir received D.Engr in Electrical Engineering from Stanford University in 2011.