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

Data-Oriented Characterization of Application-Level Energy Optimization

Abstract. Empowering application programmers to make energy-aware decisions is a critical dimension of energy optimization for computer systems. In this paper, we study the energy impact of alternative data management choices by programmers, such as data access patterns, data precision choices, and data organization. Second, we attempt to build a bridge between application-level energy management and hardware-level energy management, by elucidating how various application-level data management features respond to Dynamic Voltage and Frequency Scal-ing (DVFS). Finally, we apply our findings to real-world applications, demonstrating their potential for guiding application-level energy opti-mization. The empirical study is particularly relevant in the Big Data era, where data-intensive applications are large energy consumers, and their energy efficiency is strongly correlated to how data are maintained and handled in programs

How an Energy Awareness Application Affects User Behavior : A Case Study

A significant number of people have problems with the batteries of their mobile devices. There are several energy saving applications available to tackle those problems. Most of them increase battery life mostly by automatically closing applications or operations. The user is for the most part unaware of what the energy saving application actually does. Therefore, even though the battery might last somewhat longer, the user may continue using behavioral patterns that drain the battery. There is little evidence to support the assumption that battery awareness applications lead to behavioral changes. Therefore, we decided to conduct a research on how a mobile awareness application called Carat is used, and how the behavior of its users changes over time. The study was carried out in two phases. In the first part of the study, we conducted a user survey, and studied the Carat log data of the devices represented in the survey. We found out that the behavior changes over time; there were significant differences between new Carat users, and the more experienced ones. The more experienced users had reduced their use of problematic applications more, were interested in different features of Carat, and opened it less often. In the second part of the study, we studied how new Carat users used Carat and what they thought of using it. They were interviewed twice, and they also wrote in a web diary about their experiences. We found out that the main difference in their thinking was the increase in overall awareness about applications as something that affects battery life. We also found out that inadequate, uninteresting and unclear information causes confusion and frustration, and decreases the likelihood of users sticking with Carat

Mango: A model-driven approach to engineering green Mobile Cloud Applications

With the resource constrained nature of mobile devices and the resource abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource/computation intensive tasks from mobile devices to the cloud. Most of the existing offloading techniques can only be applied to legacy mobile applications as they are motivated by existing systems. Consequently, they are realised with custom runtimes which incur overhead on the application. Moreover, existing approaches which can be applied to the software development phase, are difficult to implement (based on manual process) and also fall short of overall (mobile to cloud) efficiency in software qualityattributes or awareness of full-tier (mobile to cloud) implications.To address the above issues, the thesis proposes a model-driven architecturefor integration of software quality with green optimisation in Mobile Cloud Applications (MCAs), abbreviated as Mango architecture. The core aim of the architecture is to present an approach which easily integrates software quality attributes (SQAs) with the green optimisation objective of Mobile Cloud Computing (MCC). Also, as MCA is an application domain which spans through the mobile and cloud tiers; the Mango architecture, therefore, takesinto account the specification of SQAs across the mobile and cloud tiers, for overall efficiency. Furthermore, as a model-driven architecture, models can be built for computation intensive tasks and their SQAs, which in turn drives the development – for development efficiency. Thus, a modelling framework (called Mosaic) and a full-tier test framework (called Beftigre) were proposed to automate the architecture derivation and demonstrate the efficiency of Mango approach. By use of real world scenarios/applications, Mango has been demonstrated to enhance the MCA development process while achieving overall efficiency in terms of SQAs (including mobile performance and energy usage compared to existing counterparts)