Open AccessEvaluation of Finite Control Set - Model Predictive Control for Dynamic Resources Management in A Shared-Resources System
Author(s) -
Dharma Aryani,
L. Wang,
Nur Asyik Hidayatullah
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/619/1/012023
Subject(s) - model predictive control , provisioning , computer science , control (management) , set (abstract data type) , control theory (sociology) , realization (probability) , stability (learning theory) , range (aeronautics) , optimal control , control engineering , distributed computing , mathematical optimization , engineering , computer network , mathematics , artificial intelligence , statistics , machine learning , programming language , aerospace engineering
This paper presents an evaluation of Finite Control Set-Model Predictive Control (FCS-MPC) performance for dynamic resources provisioning. Realization of a shared-resources environment is experimented in software system network by virtualization technique. Control parameter regulates the resources provisioning among the users to guarantee the achievement of performance metrics in the network. The objective preferences are maintained in relative term by manipulating a finite set of operating points as the control states. Moreover, an optimization procedure is implemented based on receding horizon prediction of MPC in a feedback control scheme. Further investigation is conducted to examine the performance of optimal FCS and constrained FCS for dynamic system management. The experimental results demonstrate that constrained FCS system provides a better response stability than the optimal FCS. Correspondingly, the range and quantity of input states are significantly influential to the flow of control action. A wider range and longer control state sequence yields to a smoother control signal stream.