Performance-Aware Energy Optimization on Mobile Devices in Cellular Network

Cui, Yong; Xiao, Shihan; Wang, Xin; Lai, Zeqi; Yang, Zhen; Li, Minming; Wang, Hongyi

doi:10.1109/tmc.2016.2586052

Cited by 26 publications

(19 citation statements)

References 28 publications

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“…Results showed that periodic patterns of RRC connection may cause increased power consumption and signaling overload. To optimize the smartphone energy consumption caused by frequent RRC connection, the authors in [10] have modelled the performance-aware hybrid energy optimization problem of mobile devices in mobile cellular networks.…”

Section: Related Workmentioning

confidence: 99%

“…We demonstrate the process to acquire the measurements for the six major parameters of mobile services shown in Figure 2: data traffic T S_data (bps), signaling traffic T S_sig (bps), data duration D data (s), signaling duration D sig (s), CPU power consumption P S_CPU (W) and 4G connection power consumption P S_4G (W). These parameters are crucial in developing the end-to-end energy consumption model since service energy consumption of smartphones represents a major portion, yet existing research [7][8][9][10][11][12][13][14][15] has limited insights into this aspect. Therefore, we develop test cases for 15 different mobile services from seven mobile applications, as shown in Table 1.…”

Section: Service-specific Smartphone Energy and Traffic Measurementsmentioning

confidence: 99%

“…For example, various power models have been developed for estimating the energy consumption of different smartphone components such as 3G/4G, WiFi, central processing unit (CPU), liquid crystal display (LCD) and global positioning system (GPS). The researches in [7][8][9][10] show that the energy consumption of smartphones is influenced by different traffic characteristics and signaling patterns of mobile applications. Various power models of a long-term evolution (LTE) BS proposed in [11][12][13][14][15] try to assess energy consumption of mobile applications by separating their total energy consumption into data and signaling energy components.…”

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confidence: 99%

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Modeling the Total Energy Consumption of Mobile Network Services and Applications

et al. 2019

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Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.Energies 2019, 12, 184 2 of 18 increase since 2014 [2]. Furthermore, the data centers of Akamai consumed 233,090 MWh electricity in 2016, which is an increase of around 50% since 2012 [3].The increasing number of smartphone users together with the emergence of data-heavy mobile applications such as high-definition video play, virtual reality (VR) and augmented reality (AR) are driving the growth in mobile data traffic. These applications typically require very high network bandwidth and smartphone computing resources [4][5][6]. Moreover, the instant messaging (IM) applications, such as WeChat, Twitter, etc., attract a huge number of users because they offer a variety of mobile services including text/picture/voice messages, audio/video chat, moments, etc., and also consume a lot of network resources. Existing research to reduce the overall energy consumption of the Internet, including communication networks, cloud data centers and mobile devices, has focused on segment-specific energy consumption modeling and assessment of the end-to-end delivery of mobile applications, such as power models for smartphones, BSs, and edge and core networks. For example, various power models have been developed for estimating the energy consumption of different smartphone components such as 3G/4G, WiFi, central processing unit (CPU), liquid crystal display (LCD) and global positioning system (GPS). The researches in [7][8][9][10] show that the energy consumption of smartphones is influenced by different traffic characteristics and signaling patterns of mobile applications. Various power models of a long-term evolution (LTE) BS proposed in [11][12][13][14][15] try to assess energy consumption of mobile applications by sep...

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Section: Related Workmentioning

confidence: 99%

Section: Service-specific Smartphone Energy and Traffic Measurementsmentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling the Total Energy Consumption of Mobile Network Services and Applications

et al. 2019

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“…Nowadays, without the consideration of energy [26], any one cannot afford transmission [27,28,32]. In addition to this, the bar on usage of energy resources put a constraint for data transmission [33][34][35][36][37][38][39]. To consider the concept energy consumption, the topic of green computing was explored in [40,41] for data transmission.…”

Section: Preliminariesmentioning

confidence: 99%

A Framework for Risk-Energy Aware Service-Level Agreement Provisioning (RESP) for Computing the Quickest Path

Sharma

Kumar

2019

Journal of Computer Networks and Communications

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This paper involved the computation of quickest paths with the help of service-level agreement (SLA) and energy constraints. The consideration of these constraints helps to propose a novel system model which computes the path to strengthen the continuity and criticality of the data transmission. The proposed risk-energy aware SLA provisioning (RESP) system model incorporates the research gaps of the existing risk assessment models in the literature without any increase in time complexity. The formulation of RESP shows the usefulness in the critical applications. The results show that the computation of risk-provisioned path gives the SLA satisfied paths and has a great impact of variations in the data traffic. The performance of the proposed algorithm has been indicated in terms of different performance parameters such as mean s−t paths, average hop counts, and average energy efficiency. Simulation result implies that as data traffic increases, the number of the risk‐provisioned s−t paths decreases whereas corresponding average hop counts and average energy efficiency increases.

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“…However, traditional energy efficient methods based on prediction are not well adapted to the dynamical mobile environment. To break the bottleneck, recent efforts [9] [10] [11] have introduced Lyapunov optimization framework based on which a non-predication online scheduling scheme is always designed. SALSA [9] considers both delay and wireless link quality and designs a multi-interface online scheduler based on Lyapunov optimization without considering the impact of tail energy.…”

Section: Introductionmentioning

confidence: 99%

QoE Aware and Energy Efficient Online Scheduling for Video Streaming in Cellular Networks

Xiao¹,

Liu²

2017

Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications

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In recent years, mobile video streaming is gaining overwhelming popularity and occupying an increasing proportion in mobile data traffic. Video streaming is sensitive to viewing interruptions and hungry for energy, so it is of great significance to mitigate the effect of viewing interruptions and reduce energy cost while enjoying video streaming services in cellular networks. In this paper, considering both channel quality and user buffer state, we propose a QoE aware and energy efficient online scheduling scheme based on Lyapunov optimization framework. In the scheme, a novel method by dynamically controlling the weighting value of energy consumption is introduced to obtain more energy saving and guarantee QoE performance. Simulation results demonstrate that our proposed scheme can reduce both rebuffering delay and energy consumption compared with the state-of-art method.

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Performance-Aware Energy Optimization on Mobile Devices in Cellular Network

Cited by 26 publications

References 28 publications

Modeling the Total Energy Consumption of Mobile Network Services and Applications

Modeling the Total Energy Consumption of Mobile Network Services and Applications

A Framework for Risk-Energy Aware Service-Level Agreement Provisioning (RESP) for Computing the Quickest Path

QoE Aware and Energy Efficient Online Scheduling for Video Streaming in Cellular Networks

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