A Comparative Study on the Energy Consumption of PHP Single and Double Quotes

This paper is an enhanced version of the paper presented at the SEEDS Conference (Olaoluwa, et. al, 2015). The increasing rate of carbon dioxide and other greenhouse gas emission resulting from the use of IT and other human activities to the atmosphere has become a major source of concern. It is imperative for the IT sector to ensure that its products are effective and energy efficient accompanied by mitigated negative impact on the environment. Reducing energy consumption of IT products is a key to contributing towards a greener environment. Another alternative is to produce energy efficient codes for software applications. In programming or scripting languages, an end result can be achieved in more than one way. For example, in PHP, a print command can be executed using a single quote and can also be achieved using a double quote. They have similar functions with similar quality of the intended outcomes. The aim of this research is conduct an investigation on the energy consumption of selected PHP scripts that perform similar functions: print single and double quote; echo single and double quote, etc… The Joulemeter energy measuring tool is used to measure the amount of energy consumed when run the various PHP scripts

Green Parallel Metaheuristics: Design, Implementation, and Evaluation

Fecha de lectura de Tesis Doctoral 14 mayo 2020Green parallel metaheuristics (GPM) is a new concept we want to introduce in this thesis. It is an idea inspired by two facts: (i) parallel metaheuristics could help as unique tools to solve optimization problems in energy savings applications and sustainability, and (ii) these algorithms themselves run on multiprocessors, clusters, and grids of computers and then consume energy, so they need an energy analysis study for their different implementations over multiprocessors. The context for this thesis is to make a modern and competitive effort to extend the capability of present intelligent search optimization techniques. Analyzing the different sequential and parallel metaheuristics considering its energy consumption requires a deep investigation of the numerical performance, the execution time for efficient future designing to these algorithms. We present a study of the speed-up of the different parallel implementations over a different number of computing units. Moreover, we analyze and compare the energy consumption and numerical performance of the sequential/parallel algorithms and their components: a jump in the efficiency of the algorithms that would probably have a wide impact on the domains involved.El Instituto Egipcio en Madrid, dependiente del Gobierno de Egipto

Sensors and Technologies in Spain: State-of-the-Art

The aim of this special issue was to provide a comprehensive view on the state-of-the-art sensor technology in Spain. Different problems cause the appearance and development of new sensor technologies and vice versa, the emergence of new sensors facilitates the solution of existing real problems. [...

GreenC5: An Adaptive, Energy-Aware Collection for Green Software Development

Dynamic data structures in software applications have been shown to have a large impact on system performance. In this paper, we explore energy saving opportunities of interface-based dynamic data structures. Our results suggest that savings opportunities exist in the C5 Collection between 16.95% and 97.50%. We propose a prototype and architecture for creating adaptive green data structures by applying machine learning tools to build a model for predicting energy efficient data structures based on the dynamic workload. Our neural network model can classify energy efficient data structures based on features such as the number of elements, frequency of operations, interface and set/bag semantics. The 10-fold cross validation result show 95.80% average accuracy of these predictions. Our n-gram model can accurately predict the most energy efficient data structure sequence in 19 simulated and real-world programs - on average, with more than 50% accuracy and up to 98% using a bigram predictor. Our GreenC5 prototype demonstrates how a green data structure can be implemented. With a simple decision making technique, the data structure can efficiently adapt for energy efficiency with low overhead. The median of GreenC5\u27s potential energy savings is more than 60% and ranges from 18% to 95%

Power Measurement Methods for Energy Efficient Applications

Energy consumption constraints on computing systems are more important than ever. Maintenance costs for high performance systems are limiting the applicability of processing devices with large dissipation power. New solutions are needed to increase both the computation capability and the power efficiency. Moreover, energy efficient applications should balance performance vs. consumption. Therefore power data of components are important. This work presents the most remarkable alternatives to measure the power consumption of different types of computing systems, describing the advantages and limitations of available power measurement systems. Finally, a methodology is proposed to select the right power consumption measurement system taking into account precision of the measure, scalability and controllability of the acquisition system