Optimizing Mobile Application Performance with Model–Driven Engineering

SummaryConsidering the constrained resources of mobile devices, a thorough performance evaluation of a mobile application is crucial. However, performance evaluation in the mobile domain is still a manual and time-consuming task.The diversity of mobile devices only increases the complexity of this task. We propose EPE-Mobile, a framework to automate early performance estimation in mobile applications. It is composed of a configurable library of basic operations and an engine that automatically creates a synthetic program based on the specification of a new app. The synthetic program that EPE-Mobile generates provides feedback for mobile developers at the first design stages and before the actual implementation of a new application. The fast evaluation can also guide developers in optimizing their applications or in choosing devices with the best trade-off between cost and performance to run a given application.Finally, developers can reuse the data collection infrastructure of the framework to collect performance data during all development stages. We validate the proposed framework using 4 applications from the Android Play Store. Based on their specifications, 4 synthetic programs were generated and executed on different devices. We compared the results to those obtained from the execution of the actual applications in the same devices. Experimental results show that it is possible to create synthetic applications with similar behavior to that of real applications and, thus, classify devices based on the actual application needs. The framework uses aspect-oriented programming to collect the metrics of interest. This approach provides increased modularity and separation of concerns, thus facilitating the improvement of the framework itself, by adding other metrics or basic operations.

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“…When the test operation is completed, the metrics are collected and stored (lines [17][18][19][20][21][22][23][24][25][26][27].…”

Section: Figurementioning

confidence: 99%

EPE‐Mobile—A framework for early performance estimation of mobile applications

2017

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“…This DSML allows developers to specify their software architecture visually with respect to power consuming components, as shown in Figure 2. Prior work (Thompson et al, 2009;White et al, 2010;Turner et al, ) showed how the following components are often significant power consumers in mobile applications:…”

Section: Mobile Application Architecture Modeling and Power Consumptimentioning

confidence: 99%

“…We therefore hypothesized that SPOT could provide power consumption information to within 25% of the actual power consumption of WreckWatch and OpenGPSTracker. Based on prior work (Thompson et al, 2009;White et al, 2010;Turner et al, ), we also hypothesized that the components we chose represented the key factors in mobile application power consumption and would be adequate to provide this level of accuracy.…”

Section: Experiments 1: Empirical Evaluation Of Spot's Emulation Code mentioning

confidence: 99%

Analyzing Mobile Application Software Power Consumption via Model-Driven Engineering

Thompson

Schmidt

Turner

et al. 2014

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Abstract:Smartphones are mobile devices that travel with their owners and provide increasingly powerful services. The software implementing these services must conserve battery power since smartphones may operate for days without being recharged. It is hard, however, to design smartphone software that minimizes power consumption. For example, multiple layers of abstractions and middleware sit between an application and the hardware, which make it hard to predict the power consumption of a potential application design accurately. Application developers must therefore wait until after implementation (when changes are more expensive) to determine the power consumption characteristics of a design. This paper provides three contributions to the study of applying model-driven engineering to analyze power consumption early in the lifecycle of smartphone applications. First, it presents a model-driven methodology for accurately emulating the power consumption of smartphone application architectures. Second, it describes the System Power Optimization Tool (SPOT), which is a model-driven tool that automates power consumption emulation code generation and simplifies analysis. Third, it empirically demonstrates how SPOT can estimate power consumption to within ∼3-4% of actual power consumption for representative smartphone applications.

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“…However CPS can also be seen in increasingly dynamic settings. Examples of mobile cyber-physical systems include applications to track and analyse Carbon Dioxide emissions [21], detect traffic accidents and provide situational awareness services to first responders [22], measure traffic [23], and monitor cardiac patients [24].…”

Section: Doorsmentioning

confidence: 99%

Getting Software Engineering out of Isolation

Larsen¹,

Coleman²,

Fitzgerald³

2012

Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering

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Abstract-We argue that the main challenges to be overcome in developing future generations of IT-enabled products and services lie not so much in the software engineering discipline itself as in the collaborative relationships that software engineers have with other disciplines. We briefly review the need for more emphasis on multi-disciplinary approaches and consider three classes of demanding system: embedded products, systems-of-systems and cyber-physical systems. In each of these areas, we argue that there is a need for engineering with formal semantic bases that enable joint modelling, analysis and simulation of groups of heterogeneous models.

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Optimizing Mobile Application Performance with Model–Driven Engineering

Cited by 36 publications

References 15 publications

EPE‐Mobile—A framework for early performance estimation of mobile applications

EPE‐Mobile—A framework for early performance estimation of mobile applications

Analyzing Mobile Application Software Power Consumption via Model-Driven Engineering

Getting Software Engineering out of Isolation

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