Will Edge Computing Enable Location-based Extended/Mixed Reality Mobile Gaming? Demystifying Trade-off of Execution Time vs. Energy Consumption

The trailblazing development in mobile and wearable-based gaming dictates both the support of new technology enablers to allow for current demand and the development of modern computational offloading strategies to decrease the energy of handheld devices and maintain the energy emissions caused both by computation and transmission of data. Modern cellular networks already provide some support for proximity-based gaming, e.g., Ingress, PokemonGo, and The Witcher: Monster Slayer, among others. However, the demand of users is pushing the boundaries toward full-immersive Extended and Mixed Reality (XR/MR) experiences. Thus, computational offloading to the wireless network Edge becomes inevitable to keep the immersion high. This paper aims to analyze the impact of computational offloading (and, thus, execution time) on energy consumption. Computationally demanding games are analyzed for cases run locally, sent to a conventional remote server (cloud), offloaded to the user-owned more energy-independent device, or to the network edge. The results show that Edge computing operates the most efficiently regarding the trade-off between energy spent for execution vs. data transmission. It is also noted that distance to the edge node remains one of the critical factors affecting energy consumption.Peer reviewe

VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone

Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption

Energy Efficient LTE/Wi-Fi Coexistence in the Unlicensed Spectrum

The dramatic growth in demand for wireless services has fueled a severe shortage in RF spectrum, especially in the overcrowded licensed bands. The regulatory approach for meeting this galloping demand is to allow the coexistence of competing wireless technologies (e.g., LTE Unlicensed and Wi-Fi coexisting in the 5GHz U-NII band). This shared spectrum paradigm poses novel challenges for secure, efficient, and fair resource access. Many of these challenges stem from the heterogeneity of the coexisting systems, the system scale, and the lack of explicit coordination mechanisms between them. The fundamentally different spectrum access mechanisms and PHY-layer capabilities–dynamic vs. fixed access, schedule-based vs. random access, interference-avoiding vs. interference-mitigating, etc.–create a complex and interdependent ecosystem, without a unified control plane Motivated by the shared spectrum paradigm, we address the problem of implicit coordination between coexisting wireless systems that do not share a common control plane. We consider the coexistence of LTE and Wi-Fi and study mechanisms for conserving energy when the wireless channel is occupied. In a Wi-Fi only system, the network allocation vector (NAV) included in the preamble of IEEE 802.11 frames, advertises the duration of an eminent transmission. Nearby Wi-Fi terminals decode the frame preamble and transition to sleep mode to conserve energy. However, when heterogeneous systems co-exist (e.g., LTE and Wi-Fi), frames that belong to other systems are not decodable, leading to continuous channel sensing, even when the channel is to be occupied for long duration. In this thesis, we study mechanisms to achieve coordination between LTE and Wi-Fi operating on the same band, without relying on explicit messaging. We develop and implement several implicit techniques for monitoring the operational10 parameters of LTE on the Wi-Fi side. We use these parameters to conserve energy at Wi-Fi terminals by transitioning them to sleep mode whenever the channel is occupied by an LTE station. We exploit the unique backoff characteristics of each priority traffic class to predict the length of an imminent LTE transmission. We propose two class estimation mechanisms. The first is a conservative mechanism that maximizes the Wi-Fi sleep time without missing an opportunity to contend for the channel. In the second mechanism, we apply Bayesian estimation to get a more accurate prediction of the priority class and avoid waking up the receiver too early. This comes at the expense of oversleeping when a high priority class is misclassified, thus leading to a small loss in transmission opportunities. Although we present our work from the Wi-Fi perspective, the same methodology can be applied to conserve energy on the LTE side

Power Consumption Analysis, Measurement, Management, and Issues:A State-of-the-Art Review of Smartphone Battery and Energy Usage

The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed

Energy Awareness for Multiple Network Interface-Activated Smartphone

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 최성현.최신 스마트폰은 LTE와 Wi-Fi와 같은 네트워크 인터페이스 여러 개를 동시에 사용하여 전송율을 증가시키거나 네트워크 접속성을 향상시킬 수 있다. 이런 경우에, 스마트폰의 에너지 소모는 LTE와 Wi-Fi를 동시에 사용함에 따라 증가할 수 있다. 특히, 제한된 용량을 가지는 배터리로 동작을 하는 스마트폰에서 에너지 소모는 중요한 이슈이기 때문에, 에너지 증가와 성능 향상 사이의 trade-off를 고려하는 것이 요구된다. 에너지 인지 기술과 함께 스마트폰의 LTE와 Wi-Fi 인터페이스를 전략적으로 잘 사용함으로써, 배터리 에너지 소모를 줄임과 동시에 스마트폰 어플리케이션의 성능을 최적화 하는 것이 가능해진다. 본 논문에서는 LTE와 Wi-Fi 링크를 동시에 활용할 수 있는 스마트폰에서 에너지 인지를 가능하게 하는 다음 세 가지 전략을 고려하였다. (i) LTE와 Wi-Fi를 동시에 사용하는 스마트폰의 전력 소모 모델링, (ii) 스마트폰의 배터리 소모율의 실시간 예측 기법, (iii) dynamic adaptive streaming over HTTP (DASH) 기반의 비디오 스트리밍의 성능 최적화. 먼저, 동시에 여러 네트워크를 활성화하여 사용하는 스마트폰의 정밀한 전력 소모 모델링을 제시한다. 패킷 처리에 의해 소모되는 전력과 네트워크 인터페이스에서 소모되는 전력을 분해하여, 다중 네트워크 인터페이스를 활성화시키는 경우의 정밀한 전력 소모 모델을 구성한다. 다양한 시나리오에서 측정된 전력과 모델을 통해 예측된 전력을 비교하며 제안하는 전력 소모 모델의 정확성을 평가하였다. 기존 전력 소모 모델에 비해 단일 네트워크 통신의 경우에도 7%-35% 만큼 추정 오차를 줄였으며, 다중 네트워크 통신의 경우 추정 오차를 25% 줄임을 보였다. 두 번째로, 스마트폰의 실시간 에너지 인지를 가능하게 하기 위해서, 스마트폰에서 사용하고 있는 Li-ion 배터리의 특성을 고려하여 배터리 소모율을 추정하는 기법을 고안한다. Li-ion 배터리는 온도와 노화 상태에 따라서 가용 용량과 내부 저항이 달라지기 때문에, 온도와 노화 상태에 따라서 배터리 소모율이 달라질 수 있다. 배터리 특성을 모델링하는 것은 어려운 일이기 때문에, effective resistance 개념을 도입하여 용량과 내부 저항을 모르고도 배터리 소모율을 예측할 수 있는 BattTracker를 고안한다. BattTracker는 실시간 배터리 소모율을 최대 0.5초 마다 추정할 수 있다. 실제 스마트폰으로 다양한 실험을 통하여 BattTracker가 배터리 소모 예측을 5% 오차율 이내로 예측함을 보였으며, 이를 활용하면 높은 시간 해상도로 스마트폰의 에너지 인지 동작이 가능해진다. 마지막으로, 애너지 인지 기법과 LTE와 Wi-Fi 링크를 동시에 활용하는 것을 dynamic adaptive streaming over HTTP (DASH) 기반 비디오 스트리밍 어플리케이션에 적용한다. 스마트폰에서 LTE와 Wi-Fi 링크를 동시에 활용하는 것은 다양한 측면에서 DASH 기반의 비디오 스트리밍의 성능을 향상시킬 수 있다. 그러나, 배터리 에너지와 LTE 데이터 사용량을 절약하면서 끊김없는 고화질의 비디오를 스트리밍하는 것은 도전적인 일이다. 따라서, DASH 비디오를 시청하는 사용자의 Quality of Experience (QoE)를 향상시키기 위하여 LTE와 Wi-Fi를 동시에 활용하는 DASH video chunk 요청 기법인 REQUEST를 제안한다. REQUEST는 주어진 배터리 에너지와 LTE 사용량 예산 내에서 최적에 가까운 품질의 비디오를 끊김없이 스트리밍해 주는 것을 가능하게 한다. 다양한 환경에서의 시뮬레이션과 실 환경에서의 측정을 통하여, REQUEST가 기존 비디오 스트리밍 기법에 비해 평균 비디오 품질, 재버퍼링, 자원 낭비량 관점에서 상당히 우수함을 보인다. 요약하자면, 우리는 LTE와 Wi-Fi의 동시 사용하는 스마트폰에서의 전력 모델링 방법론, 실시간 배터리 소모율 추정 기법, DASH 기반 비디오 스트리밍 성능 최적화를 제안한다. 이 연구를 통해서, 우리는 프로토타입 구현과 실험 장비들을 통한 실측 기반으로 LTE와 Wi-Fi를 동시에 활용하는 스마트폰을 위한 에너지 인지 기술을 제안한다. 제안하는 기법들의 성능은 상용 스마트폰에 구현하여 실 환경에서의 성능 평가를 통해 검증하였다.State-of-the-art smartphones can utilize multiple network interfaces simultaneously, e.g., LTE and Wi-Fi, to enhance throughput and network connectivity in various use cases. In this situation, energy consumption of smartphones can increase while using both LTE and Wi-Fi interfaces simultaneously. Since energy consumption is an important issue for smartphones powered by batteries with limited capacity, it is required to consider the trade-off between energy increase and performance enhancement. By judiciously utilizing both LTE and Wi-Fi interfaces of smartphones and energy awareness techniques, it is enabled to optimize the performance of smartphones applications while saving battery energy. In this dissertation, we consider the following three strategies to enable the energy awareness for smartphones which utilize both LTE and Wi-Fi links: (i) Power modeling for smartphone which utilizes both LTE and Wi-Fi links simultaneously, (ii) real-time battery drain rate estimation for smartphones, and (iii) optimizing the performance of dynamic adaptive streaming over HTTP (DASH)-based video streaming for smartphones. First, an accurate power modeling is presented for the smartphones, especially, those capable of activating/utilizing multiple networks simultaneously. By decomposing packet processing power and power consumed by network interfaces, we construct the accurate power model for multiple network interface-activated cases. The accuracy of our model is comparatively evaluated by comparing the estimated power with the measured power in various scenarios. We find that our model reduces estimation error by 7%–35% even for single network transmissions, and by 25% for multiple network transmissions compared with existing power models. Second, in order to enable real-time energy awareness for smartphones, we develop a battery drain rate monitoring technique by considering characteristics of Li-ion batteries which are used by smartphones. With Li-ion battery, battery drain rate varies with temperature and battery aging, since they affect battery characteristics such as capacity and internal resistance. Since it is difficult to model the battery characteristics, we develop BattTracker, an algorithm to estimate battery drain rate without knowing the exact capacity and internal resistance by incorporating the concept of effective resistance. BattTracker tracks the instantaneous battery drain rate with up to 0.5 second time granularity. Extensive evaluation with smartphones demonstrates that BattTracker accurately estimates the battery drain rate with less than 5% estimation error, thus enabling energy-aware operation of smartphones with fine-grained time granularity. Finally, we adapt an energy awareness and utilize both LTE and Wi-Fi links for a Dynamic Adaptive Streaming over HTTP (DASH)-based video streaming application. Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of DASH-based video streaming in various aspects. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. Thus, we propose REQUEST, a video chunk request policy for DASH in a smartphone, which can utilize both LTE andWi-Fi to enhance users Quality of Experience (QoE). REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste. In summary, we propose a power modeling methodology, a real-time battery drain rate estimation method, and performance optimization of DASH-based video streaming for a smartphone which utilizes both LTE and Wi-Fi simultaneously. Through this research, we propose several energy-aware techniques for the smartphone, which especially utilizes both LTE and Wi-Fi, based on prototype implementation and the real measurement with experimental equipment. The performance of the proposed methods are validated by implementation on off-the-shelf smartphones and evaluations in real environments.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Power modeling of smartphone 3 1.2.2 Battery drain rate estimation 4 1.2.3 Optimizing the performance of video streaming 5 1.3 Main Contributions 6 1.3.1 PIMM: Power Modeling of Multiple Network Interface-Activated Smartphone 6 1.3.2 BattTracker: Real-Time Battery Drain Rate Estimation 7 1.3.3 REQUEST: Performance Optimization of DASH Video Streaming 8 1.4 Organization of the Dissertation 8 2 PIMM: Packet Interval-Based Power Modeling of Multiple Network Interface-Activated Smartphones 10 2.1 Introduction 10 2.2 Background 12 2.2.1 Power saving operations of Wi-Fi and LTE 12 2.2.2 Related work 13 2.3 Power Consumption Modeling 14 2.3.1 Modeling Methodology 15 2.3.2 Packet interval-based power modeling 21 2.4 Practical issues 30 2.4.1 Impact of packet length 30 2.4.2 Impact of channel quality 33 2.5 Performance Evaluation 34 2.5.1 On-line power estimation 35 2.5.2 Single network data connections 37 2.5.3 Multiple network data connections 42 2.5.4 Model generation complexity 45 2.6 Summary 46 3 BattTracker: Enabling Energy Awareness for Smartphone Using Li-ion Battery Characteristics 47 3.1 Introduction 47 3.2 Background 50 3.2.1 Smartphones battery interface 50 3.2.2 State of Charge (SoC) and Open Circuit Voltage (Voc) 50 3.2.3 Batterys internal resistance 53 3.2.4 Related Work 53 3.3 Li-ion Battery Characteristics 54 3.3.1 Impact of temperature and aging on Li-ion battery 54 3.3.2 Measuring battery characteristics 54 3.3.3 Battery characteristics models 57 3.4 Effective Internal Resistance 58 3.4.1 Effective internal resistance (re) 58 3.4.2 Battery drain rate and lifetime derived from re 60 3.5 BattTracker Design 61 3.5.1 Rd estimator 62 3.5.2 Voc estimator 62 3.5.3 re estimator 64 3.6 Performance Evaluation 65 3.6.1 Comparison schemes and performance metrics 66 3.6.2 Convergence time of re 67 3.6.3 Power consumption overhead of BattTracker 67 3.6.4 Comparison with measurement using equipment 68 3.6.5 BattTracker with aged batteries 70 3.6.6 BattTracker with various video applications 70 3.6.7 BattTracker with varying temperature 73 3.7 Summary 74 4 REQUEST: Seamless Dynamic Adaptive Streaming over HTTP for Multi-Homed Smartphone under Resource Constraints 75 4.1 Introduction 75 4.2 Background and Related Work 77 4.2.1 Background 77 4.2.2 Related Work 79 4.3 Motivation 80 4.3.1 Wi-Fi Throughput Fluctuation 80 4.3.2 Optimizing Resource Utilization 82 4.4 Proposed Chunk Request Policy 83 4.5 Problem Formulation 86 4.6 REQUEST Algorithm 90 4.6.1 Request Interval Adaptation 91 4.6.2 Chunk Request Adaptation 94 4.7 Performance Evaluation 96 4.7.1 Prototype-Based Evaluation 98 4.7.2 Trace-Driven Simulation 102 4.8 Summary 104 5 Concluding Remarks 106 5.1 Research Contributions 106 5.2 Future Work 107 Abstract (In Korean) 117Docto