Timothy M. Lynar

Resource allocation to conserve energy in distributed computing

Energy consumption is an issue in grid computing. There has been substantial research into grid resource allocation, but little research on energy aware resource allocation. We propose that altering the resource allocation mechanism to incorporate node power and performance data can make a substantial difference to both the time taken to execute tasks and the energy consumed by the grid. This paper examines the use of three simple economic resource allocation mechanisms through simulation. We discover that different mechanisms perform better under different circumstances, and that changing the resource allocation mechanism to incorporate the power and performance information of individual nodes can result in a substantial difference to the time taken to execute tasks, and over time can make a marked difference to the total energy consumption of the grid resource.

Reducing energy consumption in distributed computing through economic resource allocation

Energy consumption is an increasingly important consideration in computing. High-performance computing environments consume substantial amounts of energy and the cost of energy is increasing. We explore the possibility of reducing the energy consumption of a grid of heterogeneous computers through appropriate resource allocation strategies. We examine a number of possible grid workload scenarios and analyse the impact of different resource allocation mechanisms on energy consumption and time taken to execute tasks. We perform this analysis first on a cluster of heterogeneous nodes and then scale up the experiment to a grid of multiple clusters. Our results show that different resource allocation mechanisms perform better under different scenarios, and that selection of the resource allocation mechanism can significantly alter grid energy consumption.

Reducing grid energy consumption through choice of resource allocation method

Energy consumption is an increasingly important consideration in computing. High-performance computing environments consume substantial amounts of energy, at an increasing financial and environmental cost. We explore the possibility of reducing the energy consumption of a grid of heterogeneous computers through appropriate resource allocation strategies. We examine a number of possible grid workload scenarios and analyse the impact of different resource allocation mechanisms on energy consumption. We perform this analysis first on a cluster of heterogeneous nodes, then on a grid of several clusters. Our results show that different resource allocation mechanisms perform better under different scenarios, and that selection of an appropriate resource allocation mechanism can significantly reduce the total grid energy consumption.

Clustering Obsolete Computers to Reduce E-Waste

Personal computers contribute significantly to the growing problem of electronic waste. Every computer, when finished with, must be stored, dumped, recycled, or somehow re-used. Most are dumped, at a huge cost to health and the environment, as their owners succumb to the desire to keep up with the ever-increasing power of new computers. Supercomputers and computer clusters provide more power than ordinary desktop and laptop computers, but they too are subject to rapid obsolescence. The authors have built a cluster of obsolete computers and have found that it easily outperforms a fairly standard new desktop computer. They explore how this approach can help to mitigate e-waste, and discuss the advantages and limitations of using such a system.