An energy-saving model for service-oriented mobile application development

The development of mobile applications that combine Web Services from different providers --also referred as mashup applications-- is growing as a consequence of the ubiquity of bandwidth connections and the increasing number of available Web Services. In this context, providing higher maintainability to Web Service applications is a worth of matter, because of the dynamic nature of the Web. EasySOC (1) solves this problem by decoupling mashups from application components. However, mobile devices have energy constraints because of the limitations in the current battery capacities. This work proposes a model that builds on the benefits of the EasySOC approach and improves this latter by assisting developers to select Web Service combinations that reduce energy consumption. We evaluated the feasibility of the model through a case study in which we compare the estimations provided by the model against real energy measurements. The results indicated that our model had an efficacy of 81% for the analyzed case study.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A performance comparison of data-aware heuristics for scheduling jobs in mobile Grids

Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our goal is to contribute to better understand both the capabilities and limitations of this kind of schedulers in the incipient area of mobile Grids.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A performance comparison of data-aware heuristics for scheduling jobs in mobile Grids

Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our goal is to contribute to better understand both the capabilities and limitations of this kind of schedulers in the incipient area of mobile Grids.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Watts2Share: Energy-Aware Traffic Consolidation

Energy consumption is becoming the Achilles' heel of the mobile user quality of experience partly due to undisciplined use of the cellular (3G) transmissions by applications. The operator infrastructure is typically configured for peak performance, whereas during periods of underutilisation the handsets pay the price by staying in high energy states even if each application only uses a fraction of the maximum available bandwidth. In this paper we promote a bi-radio scenario where instead of independently using own cellular connections, several users share a single cellular link offered by one member of a coalition (a rotating aggregator). We present Watts2Share, an architecture for energy-aware traffic consolidation whereby group members' data flows transmitted through a second radio (e.g., WiFi) are aggregated by the aggregator and retransmitted through the cellular link. Through careful and repeatable studies we demonstrate that this scheme saves up to 68% of the total transmission energy in handsets compared to a pure 3G scenario. The studies are based on a wide range of real traffic traces and real cellular operator settings, and further illustrate that this scheme reduces the overall energy by reducing the signalling overhead, as well as extending the lifetime of all handsets

A performance comparison of data-aware heuristics for scheduling jobs in mobile Grids

Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our goal is to contribute to better understand both the capabilities and limitations of this kind of schedulers in the incipient area of mobile Grids.Sociedad Argentina de Informática e Investigación Operativa (SADIO

An energy-saving model for service-oriented mobile application development

The development of mobile applications that combine Web Services from different providers --also referred as mashup applications-- is growing as a consequence of the ubiquity of bandwidth connections and the increasing number of available Web Services. In this context, providing higher maintainability to Web Service applications is a worth of matter, because of the dynamic nature of the Web. EasySOC solves this problem by decoupling mashups from application components. However, mobile devices have energy constraints because of the limitations in the current battery capacities. This work proposes a model that builds on the benefits of the EasySOC approach and improves this latter by assisting developers to select Web Service combinations that reduce energy consumption. We evaluated the feasibility of the model through a case study in which we compare the estimations provided by the model against real energy measurements and two handsets. The results indicated that our model had an efficacy of 81-85% for the analyzed case study.Sociedad Argentina de Informática e Investigación Operativ

Simulation and experimental testbed for adaptive video streaming in ad hoc networks

[EN] This paper presents a performance evaluation of the scalable video streaming over mobile ad hoc networks. In particular, we focus on the rate-adaptive method for streaming scalable video (H.264/SVC). For effective adaptation a new cross-layer routing protocol is introduced. This protocol provides an efficient algorithm for available bandwidth estimation. With this information, the video source adjusts its bit rate during the video transmission according to the network state. We also propose a free simulation framework that supports evaluation studies for scalable video streaming. The simulation experiments performed in this study involve the transmission of SVC streams with Medium Grain Scalability (MGS) as well as temporal scalability over different network scenarios. The results reveal that the rate-adaptive strategy helps avoid or reduce the congestion in MANETs obtaining a better quality in the received videos. Additionally, an actual ad hoc network was implemented using embedded devices (Raspberry Pi) in order to assess the performance of the proposed adaptive transmission mechanism in a real environment. Additional experiments were carried out prior to the implementation with the aim of characterizing the wireless medium and packet loss profile. Finally, the proposed approach shows an important reduction in energy consumption, as the study revealed.This paper was performed with the support of the National Secretary of Higher Education, Science, Technology and Innovation (SENESCYT)–Ecuador Government (scholarship 195-2012) and the Multimedia Communications Group (COMM) belong to the Institute of Telecommunications and Multimedia Applications (iTEAM)-Universitat Politècnica de València.Gonzalez-Martinez, SR.; Castellanos Hernández, WE.; Guzmán Castillo, PF.; Arce Vila, P.; Guerri Cebollada, JC. (2016). Simulation and experimental testbed for adaptive video streaming in ad hoc networks. Ad Hoc Networks. 52:89-105. https://doi.org/10.1016/j.adhoc.2016.07.007S891055

Energy-Aware Mobile Learning:Opportunities and Challenges

As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided