Energy-efficient optimal control for real-time computing systems
Author(s)
Thammawichai, Mason
Type
Thesis
Abstract
Moving toward ubiquitous Cyber-Physical Systems - where computation, control and communication units are mutually interacting - this thesis aims to provide fundamental frameworks to address the problems arising from such a system, namely the real-time multiprocessor scheduling problem (RTMSP) and the multi-UAV topology control problem (MUTCP).
The RTMSP is concerned with how tasks can be scheduled on available computing resources such that no task misses a deadline. An optimization-based control method was used to solve the problem. Though it is quite natural to formulate the task assignment problem as a mixed-integer nonlinear program, the computation cost is high. By reformulating the scheduling problem as a problem of first determining a percentage of task execution time and then finding the task execution order, the computation complexity can be reduced. Simulation results illustrate that our methods are both feasibility optimal and energy optimal. The framework is then extended to solve a scheduling problem with uncertainty in task execution times by adopting a feedback approach. The MUTCP is concerned with how a communication network topology can be determined such that the energy cost is minimized. An optimal control framework
to construct a data aggregation network is proposed to optimally trade-off between
communication and computation energy. The benefit of our network topology model is that it is a self-organized multi-hop hierarchical clustering network, which provides better performance in term of energy consumption, reliability and network scalability. In addition, our framework can be applied to both homogeneous and heterogeneous mobile sensor networks due to the generalization of the network model. Two multi-UAV information gathering applications, i.e. target tracking and area mapping, were chosen to test the proposed algorithm. Based on simulation results, our method can save up to 40% energy for a target tracking and 60% for an
area mapping compared to the baseline approach.
The RTMSP is concerned with how tasks can be scheduled on available computing resources such that no task misses a deadline. An optimization-based control method was used to solve the problem. Though it is quite natural to formulate the task assignment problem as a mixed-integer nonlinear program, the computation cost is high. By reformulating the scheduling problem as a problem of first determining a percentage of task execution time and then finding the task execution order, the computation complexity can be reduced. Simulation results illustrate that our methods are both feasibility optimal and energy optimal. The framework is then extended to solve a scheduling problem with uncertainty in task execution times by adopting a feedback approach. The MUTCP is concerned with how a communication network topology can be determined such that the energy cost is minimized. An optimal control framework
to construct a data aggregation network is proposed to optimally trade-off between
communication and computation energy. The benefit of our network topology model is that it is a self-organized multi-hop hierarchical clustering network, which provides better performance in term of energy consumption, reliability and network scalability. In addition, our framework can be applied to both homogeneous and heterogeneous mobile sensor networks due to the generalization of the network model. Two multi-UAV information gathering applications, i.e. target tracking and area mapping, were chosen to test the proposed algorithm. Based on simulation results, our method can save up to 40% energy for a target tracking and 60% for an
area mapping compared to the baseline approach.
Version
Open Access
Date Issued
2016-03
Date Awarded
2016-06
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Kerrigan, Eric
Sponsor
Thailand
Publisher Department
Aeronautics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)