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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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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. First, it presents a model-driven methodology for accurately emulating the power consumption of smartphone application architectures. This paper provides three contributions to the study of applying model-driven engineering to analyze power consumption early in the lifecycle of smartphone applications. Application developers must therefore wait until after implementation (when changes are more expensive) to determine the power consumption characteristics of a design. 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.
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It is hard, however, to design smartphone software that minimizes power consumption. The software implementing these services must conserve battery power since smartphones may operate for days without being recharged. Mobile computing, low-power, model driven engineering Smartphones are mobile devices that travel with their owners and provide increasingly powerful services.
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Our evaluation of this case study showed a reduction in manual effort that our technique provides. The paper also presents the results of a case study that applies our CSP weaving technique to a representative enterprise Java application. By mapping model weaving to a CSP and leveraging a constraint solver, our technique (1) generates solutions that are correct with respect to the weaving constraints, (2) can incorporate complex global weaving constraints, (3) can provide weaving solutions that are optimal with respect to a weaving cost function, and (4) can eliminate manual effort that would normally be required to specify pointcuts and maintain them as target models change.
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This paper presents a technique called constraint-based weaving that maps model weaving to a constraint satisfaction problem (CSP) and uses a constraintsolver to deduce the appropriate weaving strategy. For example, modeling the types and sizes of caches that can be leveraged by a web application is much easier than deducing the optimal way to weave the caches back into the solution architecture to achieve high system throughput. In many domains, such as multi-tiered e-commerce web applications, separating concerns is much easier than deducing the proper way to weave the concerns back together into a solution model. AOM decomposes the cross-cutting concerns of a model into separate models that can be woven together to form a composite solution model. Aspect-Oriented Modeling (AOM) is a promising technique for untangling the concerns of complex enterprise software systems. 2 Chris Thompson, Jules White, Brian Dougherty, and Douglas C. The paper provides the following contributions to the study of mobile software development: (1) it shows how models of a mobile software architecture can be built, (2) it describes how instrumented emulation code can be generated to run on the target mobile device, and (3) it discusses how this emulation code can be used to glean important estimates of software power consumption and performance. This paper describes current research in developing an MDE tool for modeling mobile software architectures and using them to generate synthetic emulation code to estimate power consumption properties.
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In an MDE process, a model of the software architecture can be built and analyzed early in the design cycle to identify key characteristics, such as power consumption. Model‐driven Engineering (MDE) is a promising solution to this problem. Typically, the power consumption of a mobile software architecture can only be determined after the architecture is implemented, which is late in the development cycle when design changes are costly. Determining how a software architecture will affect power consumption is hard because the impact of software design on power consumption is not well understood. A critical aspect of developing future applications for mobile devices will be ensuring that the application provides sufficient performance while maximizing battery life. Future embedded and ubiquitous computing systems will operate continuously on mobile de‐ vices, such as smartphones, with limited processing capabilities, memory, and power.