D.Phil. (Electrical and Electronic Engineering) / This thesis explores methods for improving, or optimizing, Operating System (OS) scheduling. We first study the problem of tuning an OS scheduler by setting various parameters, or knobs, made available. This problem has not been addressed extensively in the literature, and has never been solved for the default Linux OS scheduler. We present three methods useful for tuning an Operating System scheduler in order to improve the quality of scheduling leading to better performance for workloads. The first method is based on Response Surface Methodology, the second on the Particle Swarm Optimization (PSO), while the third is based on the Golden Section method. We test our proposed methods using experiments and suitable benchmarks and validate their viability. Results indicate significant gains in execution time for workloads tuned with these methods over execution time for workloads running under schedulers with default, unoptimized tuning parameters. The gains for using RSM-based over default scheduling parameter settings are only limited by the type of workload (how much time it needs to execute); gains of up to 16:48% were obtained, but even more are possible, as described in the thesis. When comparing PSO with Golden Section, PSO produced better scheduling parameter settings, but it took longer to do so, while Golden Section produced slightly worse parameter settings, but much faster. We also study a problem very critical to scheduling on modern Central Processing Units (CPUs). Modern CPUs have multicore designs, which corresponds to having more than one CPU on a single chip. These are known as Chip Multiprocessors (CMPs). The CMP is now the standard type of CPU for many different types of computers, including Personal Computers.
Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:7567 |
Date | 28 May 2013 |
Creators | Anderson, George Georgevich |
Source Sets | South African National ETD Portal |
Detected Language | English |
Type | Thesis |
Rights | University of Johannesburg |
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