A Cognitive Social IoT Approach for Smart Energy Management in a Real Environment

Energy usage inside buildings is a critical problem, especially considering high loads such as Heating, Ventilation and Air Conditioning (HVAC) systems: around 50% of the buildings’ energy demand resides in HVAC usage which causes a significant waste of energy resources due to improper uses. Usage awareness and efficient management have the potential to reduce related costs. However, strict saving policies may contrast with users’ comfort. In this sense, this paper proposes a multi-user multi-room smart energy management approach where a trade-off between the energy cost and the users’ thermal comfort is achieved. The proposed user-centric approach takes advantage of the novel paradigm of the Social Internet of Things to leverage a social consciousness and allow automated interactions between objects. Accordingly, the system automatically obtains the thermal profiles of both rooms and users. All these profiles are continuously updated based on the system experience and are then analysed through an optimization model to drive the selection of the most appropriate working times for HVACs. Experimental results in a real environment demonstrated the cognitive behaviour of the system which can adapt to users’ needs and ensure an acceptable comfort level while at the same time reducing energy costs compared to traditional usage

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

스마트폰을 위한 사용자 중심 최적화 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 김지홍.Recently, smartphones have become an integral part of everyday life. In addition, as smartphone users are expecting their devices to deliver PC-level user experience, numerous design requirements are rapidly emerging with the technology development. In order to meet the demanding system design requirements, many conventional techniques, whose basic concepts are almost same as those of the traditional computing devices such as PCs, are applied to the smartphones. However, as truly personalized and interaction-oriented devices, the smartphones have distinct characteristics which distinguish the devices from traditional computing devices. Therefore, it is highly required to understand and to analyze the distinctive inherent characteristics of smartphones for a provision of a new novel opportunity for system optimization. In this dissertation, we propose new user-centric optimization techniques to satisfy various design requirements of smartphones such as energy efficiency, effective thermal management and rapid responsiveness without any performance degradation by taking advantage of high-level information from the smartphone users. We first introduce a new definition of the response time, the user-perceived response time, which is known to be a critical metric for the quality of user experience of the smartphone. We also present a user-perceived response time analyzer for Android-based smartphones, which can identify the user-perceived response time of smartphone apps during run time. Based on on-line identification of the user-perceived response time, we propose a novel CPU power management framework, which enables more aggressive low-power techniques to be employed while executing display-insensitive parts of task executions. Our experimental results on a smartphone development board show that the proposed technique can reduce the CPU energy consumption by up to 65.6% over the Android's default ondemand cpufreq governor. Second, we propose a novel dynamic thermal management (DTM) technique for smartphones, which ensures the quality of user experience during the execution of display-sensitive parts without any thermal violations. In the proposed DTM technique, in order to identify that the current execution could affect the visible portion of the display, we develop a user-perceived response time prediction model for each interactive session based on statistical analysis of the user-perceived response times for the past interactive sessions. By exploiting the on-line prediction of the user-perceived response time, the proposed DTM technique carefully makes the DTM decisions for a higher quality of user experience. Our experimental results on an ODROID-XU+E board show that the proposed technique can improve the user-perceived performance by up to 37.96% over the Androids default DTM policy. Third, we present a personalized optimization framework for smartphones which can provide valuable high-level hints for optimizing the smartphone design requirements. The main goal of the proposed framework is collecting an app usage log of a smartphone user and analyzing the collected log so that particular usage patterns, if any, can be effectively identified. In order to identify app usage patterns, a couple of app usage models are also proposed. Based on the app usage models developed, we also propose a launching experience optimization which avoids unnecessary app restarts considering the detrimental effects of the restart on user experience from the perspective of performance, energy, and loss of previous state. Our experimental results on the Nexus S Android reference phones show that our proposed optimization technique can avoid unnecessary application restarts by up to 78.4% over the default LRU-based policy of the Android platform. Based on the evaluation for each technique, we verified that the user-centric optimization techniques improve the quality of user experience in terms of energy efficiency, effective thermal management and rapid responsiveness over previous system-centric techniques.Chapter I. Introduction 13 1.1 Motivation 13 1.1.1 Distinctive Characteristics of Smartphone 13 1.1.2 Existing Optimization Techniques for Smartphones and Their Limitations 16 1.2 Dissertation Goals 19 1.3 Contributions 20 1.4 Dissertation Structure 21 Chapter II. Related Work 23 2.1 Power Management Techniques 23 2.2 User Behavior Characterization 25 2.3 Launching Time Optimization Techniques 26 Chapter III. CPU Power Management Technique Using User-Perceived Response Time Analysis 31 3.1 Motivation 31 3.2 Design and Implementation of URA 36 3.2.1 Overview 36 3.2.2 User-Perceived Resoponse Time Identification 38 3.2.3 URA-based CPU Power Optimization Technique 41 3.3 Experimental Results 42 Chapter IV. SmartDTM: Smart Thermal Management for Smartphones 49 4.1 Overview 49 4.2 Motivation 52 4.3 Design and Implementation of SmartDTM 56 4.3.1 Basic Idea 56 4.3.2 Architectural Overview 59 4.3.3 User-Perceived Response Time Prediction 62 4.3.4 Worst-Case Temperature Estimation Model 64 4.4 Experimental Results 66 4.4.1 Experimental Environment 66 4.4.2 Performance Evaluation 68 4.4.3 Temperature Evaluation 70 Chapter V. Personalized Optimization Framework 77 5.1 Motivation 77 5.2 Design and Implementation of POA 78 5.2.1 Design Overview 78 5.2.2 App Usage Modeling Module 82 5.2.3 Usage Model-Based Optimization Module 83 5.3 App Usage Model Construction 83 5.3.1 P-AUM: Pattern-based App Usage Model 83 5.3.2 C-AUM: Clustering-based App Usage Model 87 Chapter VI. AUM-based Launching Experience Optimization Technique 96 6.1 Motivation 96 6.1.1 Impact of Cold Starts on App Launching Experience 98 6.1.2 Android Task Management Scheme 101 6.1.3 Problem of the LRU-based Task Killer 102 6.2 AUM-based Launching Experience Optimization 106 6.2.1 App Usage (AU)-aware Task Killer 106 6.2.2 App Usage (AU)-aware Prelauncher 107 6.3 Experimental Results 109 6.3.1 Experiment Environment 109 6.3.2 Results of Task Killing Mechanism Optimization 110 6.3.3 Results of Prelaunching Technique 118 Chapter VII. Conclusions 119 7.1 Summary and Conclusions 119 7.2 Future Work 121 7.2.1 Improving Prediction Accuracy of the AUMs Using Context Information 121 7.2.2 Integrated Intra- and Inter-App Approaches for User-Centric Optimizations 123 7.2.3 User-Centric Optimizations for The Other Design Requirements 124 Bibliography 126 국문 초록 132Docto

An Intelligent Framework for Energy-Aware Mobile Computing Subject to Stochastic System Dynamics

abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201