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(a) morphone.app: a self-optimizing application. (b) morphone.OS: optimization of machine parameters by an external observer.
Source publication
Mobile devices, due to their wide distribution and to their increasing smartness and availability of computational power, can become the interaction point between users and their surrounding environments. However, current mobile devices OSes lack of the ability to anticipate and overcome internal and external changes. Integrating mechanisms of self...
Context in source publication
Context 1
... paradigms are possible and can coexists in this system, as shown in Figure 1. In the first paradigm, applications are self-monitored and self-adaptive, while in the second one, they are monitored by an external entity which can enable actuators to perform actions to modify global system conditions. Application knobs and global knobs belong to the same framework and are entities that can act on the system configuration in terms of objectives. The two paradigms are not mutually exclusive and the could live together with the only restriction that an application is denied to modify global parameters. This is due to the lack of knowledge, on the application side, of the system global conditions. On the other hand a global entity, if necessary, can modify single application parameters to orchestrate diverse components to reach a global target. In ...
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Citations
... In addition to advances in new hardware, the installed software itself also increasingly requires a larger battery capacity so that the user can obtain good interaction with the installed application without having to worry about recharging the battery more often than previously necessary [Nacci et al. 2013]. And for this, resources such as GPS, speech recognition and notification systems often end up being used in an excessively unnecessary way [Cañete et al. 2020]. ...
... When a mobile application is running, the energy consumption not only depends on the installed code, but also on how the user interacts with the application [3] . Based on the user's interaction, the application functionality uses the available resources differently. ...
... Context This module maintains the information about the current application context. The contextual information is divided into three submodules [3] : (1) Environment represents the external information of the application (e.g.,location); (2) App Status maintains the application state (user behavior) between executions by using the information provided by Monitors ; and (3) Device Status gets the state about the mobile itself (e.g., battery level). ...
... In mobile applications, despite the fact that adaptations are frequently driven by non-functional requirements such as improving the application's performance [10,12,15] , increasing the failure tolerance [15] , or improving the user experience [3,32,48] , among others; reducing the energy consumption is the most important objective [3,10,45,46,49,53] , as the most recent energy optimization approaches focus on [10,50,53,54] . A dynamic adaptation system is characterized by different dimensions or characteristics such as the primary reconfiguration goal (e.g., improving performance, energy saving), the type of adaptation engine (application-oriented vs system-oriented) [10,13] , or the context that is monitored (e.g., device status, environment, user's interactions) [3] . ...
Context The energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application.
Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained.
Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption.
Results The proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%.
Conclusion The adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.
... Designing such platforms is really challenging since, on the one hand, they need to be small (in order to be portable) while, on the other hand, they need to embed a lot of different electronic components (i.e., a large variety of sensors and communication interfaces [2]) to assist the user during his daily routine. As a consequence, managing thermal factors of such devices is crucial: as first, there is no space for fans or cooling mechanisms in a system where many critical components (e.g., the battery, the radio communication elements, the processor, etc.) generate a significant quantity of heat; secondly, the same external temperature highly influences the behaviour of such devices [3]. These aspects are the factors that increase the relevance of the thermal problem on mobile devices: it directly harms the user experience, as these devices are meant to be kept in a hand, while high temperatures may damage the electronic components of the device itself too [4]. ...
... If compared to traditional computing platforms, smartphones and tablets are exposed to a wider variety of external environmental conditions [3]. It is then interesting to study if and how the external temperature can influence their internal status and consequently their performance, laying the foundation for future thermal management systems for mobile devices. ...
In the context of mobile devices, the thermal prob- lem is an emerging one, as it affects the user experience and involves factors that are both internal and external with respect to the device. In this paper, we present an evaluation of these factors, that consists of two parts. The first one is the analysis of thermal interactions between the internal components of the system, performed with an infrared camera. The second part consists in the analysis of the impact of external temperature on the performance of CPUs and batteries. We finally propose the VirtIRCamera app, a thermal simulator for Android devices, able to generate thermal maps relying on the thermal model proposed within this paper. As a characterization of the thermal phenomenon, this work is the first step in the creation of thermal management techniques that are specifically designed for mobile devices.
... Generalizing the examples, we inferred that the idea of context aware computing is strictly related with the so called Observe, Decide and Act (ODA) loop [8]. In fact, everytime we want to implement such a system, we need an observation phase on the environment, a decision phase to analyze data gathered in order to choose which are the actions to be performed, and finally an action phase to actually perform those actions. ...
... We introduced the context awareness at the OS level in a previous work, [8]. Anyway, nowadays, everyone has a mobile device with a pre-loaded OS and generally applications can be easily downloaded form a marketplace. ...
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Mobile devices have become the main interaction mean between users and the surrounding environment. An indirect measure of this trend is the increasing amount of security threats against mobile devices, which in turn created a demand for protection tools. Protection tools, unfortunately, add an additional burden for the smartphone's battery power, which is a precious resource. This observation motivates the need for smarter (security) applications, designed and capable of running within adaptive energy goals. Although this problem affects other areas, in the security area this research direction is referred to as "green security". In general, a fundamental need to the researches toward creating energy-aware applications, consist in having appropriate power models that capture the full dynamic of devices and users. This is not an easy task because of the highly dynamic environment and usage habits. In practice, this goal requires easy mechanisms to measure the power consumption and approaches to create accurate models. The existing approaches that tackle this problem are either not accurate or not applicable in practice due to their limiting requirements. We propose MPower, a power-sensing platform and adaptive power modeling platform for Android mobile devices. The MPower approach creates an adequate and precise knowledge base of the power "behavior" of several different devices and users, which allows us to create better device-centric power models that considers the main hardware components and how they contributed to the overall power consumption. In this paper we consolidate our perspective work on MPower by providing the implementation details and evaluation on 278 users and about 22.5 million power-related data. Also, we explain how MPower is useful in those scenarios where low-power, unobtrusive, accurate power modeling is necessary (e.g., green security applications).
