A Survey of Energy Efficient Wireless Transmission and Modeling in Mobile Cloud Computing
TL;DR: A survey on the universal energy estimation model for mobile devices is presented to provide a comprehensive summary of recent work on transmission energy savings.
Abstract: The emergence of mobile cloud computing (MCC) indicates that increasingly abundant applications are available, thus deeming energy problems even more significant. To achieve energy optimization in mobile systems, power consumption involved with each component or application need to be estimated prior to execution. In this paper, we present a survey on the universal energy estimation model for mobile devices. Additionally, due to the significance of wireless network interface card (WNIC) in the power use of mobile devices, considerable researches have been devoted to a low-power design of the WNIC (i.e., Cellular and WiFi). These efforts have allowed us to provide a comprehensive summary of recent work on transmission energy savings. Finally, we conclude the survey and discuss the future research directions.
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22 Jun 2015
TL;DR: In this article, the authors considered an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server and formulated the offloading problem as the joint optimization of the radio resources and the computational resources to minimize the overall users' energy consumption, while meeting latency constraints.
Abstract: Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles/second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.
715 citations
TL;DR: This paper considers an MIMO multicell system where multiple mobile users ask for computation offloading to a common cloud server, and proposes an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem.
Abstract: Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider a MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to express the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. Then, we reformulate the algorithm in a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.
632 citations
TL;DR: This article proposes a novel mechanism for data uploading in smart cyber-physical systems, which considers both energy conservation and privacy preservation, and proposes a heuristic algorithm that achieves an energy-efficient scheme for data upload by introducing an acceptable number of extra contents.
Abstract: To provide fine-grained access to different dimensions of the physical world, the data uploading in smart cyber-physical systems suffers novel challenges on both energy conservation and privacy preservation. It is always critical for participants to consume as little energy as possible for data uploading. However, simply pursuing energy efficiency may lead to extreme disclosure of private information, especially when the uploaded contents from participants are more informative than ever. In this article, we propose a novel mechanism for data uploading in smart cyber-physical systems, which considers both energy conservation and privacy preservation. The mechanism preserves privacy by concealing abnormal behaviors of participants, while still achieves an energy-efficient scheme for data uploading by introducing an acceptable number of extra contents. To derive an optimal uploading scheme is proved to be NP-hard. Accordingly, we propose a heuristic algorithm and analyze its effectiveness. The evaluation results towards a real-world dataset demonstrate that the performance of the proposed algorithm is comparable with the optimal results.
447 citations
TL;DR: This paper presents the current offloading frameworks, computation offloading techniques, and analyzes them along with their main critical issues and summarizes the issues in off loading frameworks in the MCC domain that requires further research.
160 citations
Cites background from "A Survey of Energy Efficient Wirele..."
...This will help the offloading process to be performed in a seamless fashion while discovering the surrounded environment [5,9,14]....
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TL;DR: A thorough overview of mobile cloud computing is presented and a generic architecture that evaluates 30 recently proposed mobile cloud Computing research architectures (i.e., published since 2010) is presented by utilizing a set of assessment criteria.
133 citations
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04 Nov 2009
TL;DR: TailEnder is developed, a protocol that reduces energy consumption of common mobile applications and aggressively prefetches several times more data and improves user-specified response times while consuming less energy.
Abstract: In this paper, we present a measurement study of the energy consumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM incur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on these measurements, we develop a model for the energy consumed by network activity for each technology.Using this model, we develop TailEnder, a protocol that reduces energy consumption of common mobile applications. For applications that can tolerate a small delay such as e-mail, TailEnder schedules transfers so as to minimize the cumulative energy consumed meeting user-specified deadlines. We show that the TailEnder scheduling algorithm is within a factor 2x of the optimal and show that any online algorithm can at best be within a factor 1.62x of the optimal. For applications like web search that can benefit from prefetching, TailEnder aggressively prefetches several times more data and improves user-specified response times while consuming less energy. We evaluate the benefits of TailEnder for three different case study applications - email, news feeds, and web search - based on real user logs and show significant reduction in energy consumption in each case. Experiments conducted on the mobile phone show that TailEnder can download 60% more news feed updates and download search results for more than 50% of web queries, compared to using the default policy.
1,239 citations
"A Survey of Energy Efficient Wirele..." refers background in this paper
...A considerable amount of energy (nearly 60%), referred to tail energy, is consumed after each transfer procedure is completed [4]....
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24 Oct 2010
TL;DR: PowerBooter is an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components.
Abstract: This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.
1,225 citations
15 Jun 2010
TL;DR: A comprehensive study of smartphone use finds that qualitative similarities exist among users that facilitate the task of learning user behavior and demonstrates the value of adapting to user behavior in the context of a mechanism to predict future energy drain.
Abstract: Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.
901 citations
"A Survey of Energy Efficient Wirele..." refers background in this paper
...However, the diversity of components and user’s behaviors [11, 23], as generated by various sensors and mobile applications, presents a great...
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12 Dec 2009
TL;DR: A regression-based power estimation model is presented that accurately estimates power consumption and provides insights about the power breakdown among hardware components, and it is shown that energy consumption widely varies depending upon the user.
Abstract: As the market for mobile architectures continues its rapid growth, it has become increasingly important to understand and optimize the power consumption of these battery-driven devices. While energy consumption has been heavily explored, there is one critical factor that is often overlooked -- the end user. Ultimately, the energy consumption of a mobile architecture is defined by user activity. In this paper, we study mobile architectures in their natural environment -- in the hands of the end user. Specifically, we develop a logger application for Android G1 mobile phones and release the logger into the wild to collect traces of real user activity. We then show how the traces can be used to characterize power consumption, and guide the development of power optimizations. We present a regression-based power estimation model that only relies on easily-accessible measurements collected by our logger. The model accurately estimates power consumption and provides insights about the power breakdown among hardware components. We show that energy consumption widely varies depending upon the user. In addition, our results show that the screen and the CPU are the two largest power consuming components. We also study patterns in user behavior to derive power optimizations. We observe that majority of the active screen time is dominated by long screen intervals. To reduce the energy consumption during these long intervals, we implement a scheme that slowly reduces the screen brightness over time. Our results reveal that the users are happier with a system that slowly reduces the screen brightness rather than abruptly doing so, even though the two schemes settle at the same brightness. Similarly, we experiment with a scheme that slowly reduces the CPU frequency over time. We evaluate these optimizations with a user study and demonstrate 10.6% total system energy savings with a minimal impact on user satisfaction.
397 citations
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