Name
Abstract | ||
|---|---|---|
In a number of real-time applications such as target tracking, precise workloads are unknown a priori but may dynamically vary, for example, based on the changing number of targets to track. It is important to manage the CPU utilization, via feedback control, to avoid severe overload or underutilization even in the presence of dynamic workloads. However, it is challenge to model a real-time system for feedback control, as computer systems cannot be modeled via physics laws. In this paper, we present a novel closed-loop approach for utilization control based on formal fuzzy logic control theory, which is very effective to support the desired performance in a nonlinear dynamic system without requiring a system model. We mathematically prove the stability of the fuzzy closed-loop system. Further, in a real-time kernel, we implement and evaluate our fuzzy logic utilization controller as well as two existing utilization controllers based on the linear and model predictive control theory for an extensive set of workloads. Our approach supports the specified average utilization set-point, while showing the best transient performance in terms of utilization control among the tested approaches. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.jss.2010.01.031 | Journal of Systems and Software |
Keywords | Field | DocType |
feedback control,existing utilization controller,formal fuzzy logic control,utilization management,fuzzy control theory,fuzzy logic utilization controller,robust fuzzy cpu utilization,fuzzy closed-loop system,dynamic workloads,real-time systems,utilization control,specified average utilization set-point,computer system,cpu utilization,model predictive control theory,fuzzy logic,real time,fuzzy control,model predictive control,real time systems,system modeling | Control theory,Neuro-fuzzy,CPU time,Computer science,Fuzzy logic,Model predictive control,Control engineering,Real-time computing,Control system,Fuzzy control system,System model | Journal |
Volume | Issue | ISSN |
83 | 7 | The Journal of Systems & Software |
Citations | PageRank | References |
5 | 0.42 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Can Başaran | 1 | 87 | 7.72 |
| Mehmet H. Suzer | 2 | 21 | 2.44 |
| Kyoung-Don Kang | 3 | 563 | 37.51 |
| Xue Liu | 4 | 3058 | 193.41 |