William J. Chivers

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.

Mismatch between marine plankton range movements and the velocity of climate change

The response of marine plankton to climate change is of critical importance to the oceanic food web and fish stocks. We use a 60-year ocean basin-wide data set comprising >148,000 samples to reveal huge differences in range changes associated with climate change across 35 plankton taxa. While the range of dinoflagellates and copepods tended to closely track the velocity of climate change (the rate of isotherm movement), the range of the diatoms moved much more slowly. Differences in range shifts were up to 900 km in a recent warming period, with average velocities of range movement between 7 km per decade northwards for taxa exhibiting niche plasticity and 99 km per decade for taxa exhibiting niche conservatism. The differing responses of taxa to global warming will cause spatial restructuring of the plankton ecosystem with likely consequences for grazing pressures on phytoplankton and hence for biogeochemical cycling, higher trophic levels and biodiversity.

Predicting Self-Reported Illness for Professional Team-Sport Athletes

PURPOSE To identify contributing factors to the incidence of illness for professional team-sport athletes, using training load (TL), self-reported illness, and well-being data. METHODS Thirty-two professional rugby league players (26.0 ± 4.8 y, 99.1 ± 9.6 kg, 1.84 ± 0.06 m) were recruited from the same club. Players participated in prescribed training and responded to a series of questionnaires to determine the presence of self-reported illness and markers of well-being. Internal TL was determined using the session rating of perceived exertion. These data were collected over 29 wk, across the preparatory and competition macrocycles. RESULTS The predictive models developed recognized increases in internal TL (strain values of >2282 AU, weekly TL >2786 AU, and monotony >0.78 AU) to best predict when athletes are at increased risk of self-reported illness. In addition, a reduction in overall well-being (<7.25 AU) in the presence of increased internal TL, as previously stated, was highlighted as a contributor to self-reported-illness occurrence. CONCLUSIONS These results indicate that self-report data can be successfully used to provide a novel understanding of the interactions between competition-associated stressors experienced by professional team-sport athletes and their susceptibility to illness. This may help coaching staff more effectively monitor players during the season and potentially implement preventive measures to reduce the likelihood of illnesses occurring.

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.

Predator-prey systems depend on a prey refuge.

Models of near-exclusive predator-prey systems such as that of the Canadian lynx and snowshoe hare have included factors such as a second prey species, a Holling Type II predator response and climatic or seasonal effects to reproduce sub-sets of six signature patterns in the empirical data. We present an agent-based model which does not require the factors or constraints of previous models to reproduce all six patterns in persistent populations. Our parsimonious model represents a generalised predator and prey species with a small prey refuge. The lack of the constraints of previous models, considered to be important for those models, casts doubt on the current hypothesised mechanisms of exclusive predator-prey systems. The implication for management of the lynx, a protected species, is that maintenance of an heterogeneous environment offering natural refuge areas for the hare is the most important factor for the conservation of this species.

THE EFFECTS OF VARYING PARAMETER VALUES AND HETEROGENEITY IN AN INDIVIDUAL-BASED MODEL OF PREDATOR-PREY INTERACTION

An individual-based model which produces nonlinear predator-prey dynamics is described. The importance of individual variation to the stability of the population dynamics predicted by the model and the advantages of the individual-based approach to modelling ecological systems is discussed. The individual-based model is compared with the traditional approach of population ecology — the modelling of populations with state variable equations. The individual-based model built here produces similar patterns of mutual dependence of the populations to those produced by the state variable model but has additional utility. It greatly simplifies the adjustment of individual environmental parameters which may be built into the model and it makes it possible to follow individuals or individual parameter values through the simulation. The cost of the utility of the individual-based approach is in the complexity of the model itself, which is more difficult to build than many state variable models. A common finding in the literature of individual-based modelling in ecology is the importance of individual variation. The individual-based model described here is built with a minimum of biological complexity, but still we find that individual variation in the model has profound effects on the stability of the population levels over long time periods.