Framework for task scheduling in heterogeneous distributed computing using genetic algorithms

An algorithm has been developed to dynamically schedule heterogeneous tasks on heterogeneous processors in a distributed system. The scheduler operates in an environment with dynamically changing resources and adapts to variable system resources. It operates in a batch fashion and utilises a genetic algorithm to minimise the total execution time. We have compared our scheduler to six other schedulers, three batch-mode and three immediate-mode schedulers. Experiments show that the algorithm outperforms each of the others and can achieve near optimal efficiency, with up to 100,000 tasks being scheduled

Complex Structure of Dynamic Stall on Wind Turbine Airfoils

Fluid dynamics video demonstrating the evolution of dynamic stall on a wind turbine blade.Comment: 2 pages with 2 attached movie

Adaptive Scheduling Across a Distributed Computation Platform

A programmable Java distributed system, which adapts to available resources, has been developed to minimise the overall processing time of computationally intensive problems. The system exploits the free resources of a heterogeneous set of computers linked together by a network, communicating using SUN Microsystems' Remote Method Invocation and Java sockets. It uses a multi-tiered distributed system model, which in principal allows for a system of unbounded size. The system consists of an n-ary tree of nodes where the internal nodes perform the scheduling and the leaves do the processing. The scheduler nodes communicate in a peer-to-peer manner and the processing nodes operate in a strictly client-server manner with their respective scheduler. The independent schedulers on each tier of the tree dynamically allocate resources between problems based on the constantly changing characteristics of the underlying network. The system has been evaluated over a network of 86 PCs with a bioinformatics application and the travelling salesman optimisation problem

Fatty acid modulation of the endocannabinoid system and the effect on food intake and metabolism

Endocannabinoids and their G-protein coupled receptors (GPCR) are a current research focus in the area of obesity due to the system’s role in food intake and glucose and lipid metabolism. Importantly, overweight and obese individuals often have higher circulating levels of the arachidonic acid-derived endocannabinoids anandamide (AEA) and 2-arachidonoyl glycerol (2-AG) and an altered pattern of receptor expression. Consequently, this leads to an increase in orexigenic stimuli, changes in fatty acid synthesis, insulin sensitivity, and glucose utilisation, with preferential energy storage in adipose tissue. As endocannabinoids are products of dietary fats, modification of dietary intake may modulate their levels, with eicosapentaenoic and docosahexaenoic acid based endocannabinoids being able to displace arachidonic acid from cell membranes, reducing AEA and 2-AG production. Similarly, oleoyl ethanolamide, a product of oleic acid, induces satiety, decreases circulating fatty acid concentrations, increases the capacity for β-oxidation, and is capable of inhibiting the action of AEA and 2-AG in adipose tissue. Thus, understanding how dietary fats alter endocannabinoid system activity is a pertinent area of research due to public health messages promoting a shift towards plant-derived fats, which are rich sources of AEA and 2-AG precursor fatty acids, possibly encouraging excessive energy intake and weight gain

Multi-tiered distributed computing platform

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