ENP Newswire -
Release date- 15012014 - Computer scientists at
The scientists have dubbed their new system 'Stratus'. Using mathematical algorithms, Stratus effectively balances the load between different computer servers located across the globe. All of the services on the Internet today are based in the 'Cloud', which means Twitter,
The cloud-computing facilities consume megawatts of power and generate a level of greenhouse gas emissions that varies depending on factors such as local time, the utility's fuel mix for electricity generation, and the use of sophisticated power-saving techniques. For example, a facility fed by a coal-fired generator in the middle of a hot summer day consumes power that is both expensive to buy, and which generates a very high carbon output. In contrast, a facility operating next to a large wind farm in the middle of the night will be cheaper and more carbon efficient.
Companies that host their services in the cloud need to buy sufficient capacity to meet demand, but they can choose where in the world they want their servers to be located and can even change this on an hourly basis. Stratus allows a company to set out how much importance they attach to cost, greenhouse gas emissions and network delays involved in servicing their internet load. The algorithms then work out how best to split the load across different cloud-computing facilities to achieve the 'best' result.
The research has just appeared in the inaugural issue of IEEE: Transactions on Cloud Computing, which is a new journal in the prestigious IEEE Transactions series. Professor in Computer Science at
To test Stratus, the scientists created a simulation based on three large facilities located at
In their simulations, the scientists found that by tailoring the algorithms to reduce carbon output, they could achieve a 21% reduction in the greenhouse gas emissions associated with the given load. Likewise, by targeting electricity cost reductions, they could achieve a 61% saving over simply splitting the load evenly. By assigning weights to each factor, the load could be spread to fully reflect individual preferences.
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