Optimal Energy Distribution With Energy Packet Networks

Abstract

We use Energy Packet Network paradigms to investigate energy distribution problems in a computer system with energy harvesting and storages units. Our goal is to minimize both the overall average response time of jobs at workstations and the total rate of energy lost in the network. Energy is lost when it arrives at idle workstations which are empty. Energy is also lost in storage leakages. We assume that the total rate of energy harvesting and the rate of jobs arriving at workstations are known. We also consider a special case in which the total rate of energy harvesting is sufficiently large so that workstations are less busy. In this case, energy is more likely to be sent to an idle workstation. Optimal solutions are obtained which minimize both the overall response time and energy loss under the constraint of a fixed energy harvesting rate.

G-Networks and the Performance of ICT with Renewable Energy

“…The amount of energy in such an EP can be small enough to be close to the smallest energy needs of consumers [36,39,41,84], or large enough to be a significant quantity to power large energy consumers [115]. In the latest research [63,117,118], the number of jobs or data packets that can be executed by one single EP is a random variable. Thus if a WS is energy-efficient, it will execute more jobs with a single EP.…”

“…The energy packet networks (EPN) [35,39,41,63,81,84,118] is a discrete state-space modeling framework that can analyze the interaction between discrete energy flows and job flows (or packets) in a single system. Energy packets (EPs), jobs and data packets are the customer classes in the EPN.…”

“…A remarkable property of G-networks is the PFS, which is the steady-state joint probability distribution of the number of positive customers at queues. The work of the EPN exploits G-networks to analyze and evaluate a multi-server system's performance or QoS, for instance, the average response time of jobs [41,63], energy efficiency and average response time of jobs [118] or energy reserve [41].…”

Introduction to the Special Issue on the French–Polish Collaboration in Mathematical Models of Computer Systems, Networks and Bioinformatics

“…Multihop EPN models are studied in [72] to represent the backbone of a mobile network, and related models can be found in [44]. In [145], a utility function including wasted power and the average delay of jobs is used to optimize an EPN.…”

Energy Packet Networks: an Annotated Bibliography

“…Two techniques were involved to obtain the expected result from this study, that includes (1) all bits of EP had arrived and were ready at the transmitter-end before the actual transmission started, and (2) the arrival times and sizes of EP were known to the cloud system. [49] used EPN to minimize both the overall average RT of tasks at various workstations and the total rate of energy lost (it happens when energy arrives at the idle workstations and during storage leaks) in the cloud. Prior knowledge of the rate of (1) energy harvesting, and (2) jobs arrival at workstations supported the workstations, supported to find the optimal solutions under the constraint of energy-harvesting network-based fixed energy-harvesting rate.…”