Title:

Energy and performance optimisation with energy packet networks

The recent exponential growth of Information and Communication Technologies (ICT) also leads to a rapid increase in ICT energy consumption. The challenge of achieving energyefficient ICT has been therefore explored, including Energy Harvesting (EH) technologies with intermittent renewable energy sources. Thus there has been considerable interest in understanding how to optimise energy efficiency and quality of service (QoS) for computercommunication systems powered by EH technologies. This thesis investigates mathematical modelling and performance evaluation for computer communication systems powered by EH technologies with intermittent renewable energy sources. In our research, we use the Energy Packet Network (EPN) paradigm, which is a discrete statespace modelling framework based on G network theory. In such a system, discrete activities such as computer jobs and data in the form of packets consume energy represented by discrete energy packets (EPs), each of which is a basic unit of energy in Joules. This approach uses queueing theory such that the joint behaviour of discretised energy flows and job or data flows is analysed in a single model, which can evaluate both the performance and energy efficiency of a complex interconnected computercommunication system. Specifically, our work uses the EPN paradigm to address four relevant problems of practical interest to optimise performance and energy efficiency. We use energy flow only or job flow and energy flow together to optimise a multiserver system's performance or QoS, for instance, the average response time of jobs. In the first problem (Problem P1), we investigate how to select the optimal fraction of power that is shared between heterogeneous servers, so as to minimise the average response time of jobs. We use the Lagrange multiplier method to solve Problem P1 analytically, which is subject to power constraints. We also obtain a physically meaningful condition to guarantee system stability and global optimality. The second problem (Problem P2) is to minimise the average response time of jobs by dynamically deciding whether to move jobs between servers, so as to balance the workload at each server. Problem P2 is solved numerically through the gradient descent. Regarding the third problem (Problem P3), we consider a cost function that combines both the average response time of jobs and the rate of energy loss. Then we select the optimal fraction of power shared between heterogeneous servers again to minimise the cost function. The optimal solution is obtained by solving a system of equations simultaneously. The fourth problem (Problem P4) investigates how to match the energy flow into energy buffers and job flow into servers or workstations, so as to minimise the average response time of jobs. We have given a necessary condition to match energy flow and job flow, which finds the local minimum of the average response time of jobs analytically. Throughout this thesis, we generalise the assumption of the previous work of the EPN paradigm that one EP can only be used to execute one single job. We assume that the number of jobs can be executed by one EP is a random variable of Xi following general probability distribution. The derivation of the average number of jobs that can be executed by one EP is given in Appendix A. For the sake of computational convenience, we assume the random variable X_i follows a geometric distribution in our analysis. However, we have also discussed how to solve the problem with the general distribution in Chapter 6 and Appendix B.
