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...

The energy savings of 10 Gbps vertical-cavity surface-emitting lasers (VCSELs) for use in energy-efficient optical network units (ONUs) is critically examined in this work. We experimentally characterize and analytically show that the fast settling time and low power consumption during active and power-saving modes allow the VCSEL-ONU to achieve significant energy savings over the distributed feedback laser (DFB) based ONU. The power consumption per customer using VCSEL-ONUs and DFB-ONUs, is compared through an illustrative example of 10G-EPON for Video-on-Demand delivery. Using energy consumption models and numerical analyses in sleep and doze mode operations, we present an impact study of network and protocol parameters, e.g. polling cycle time, network load, and upstream access scheme used, on the achievable energy savings of VCSEL-ONUs over DFB-ONUs. Guidance on the specific power-saving mode to maximum energy savings throughout the day, is also presented.

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

Multiaccess edge computing and caching (MEC) is regarded as one of the key technologies of fifth-generation (5G) radio access networks. By bringing computing and storage resources closer to the end users, MEC could help to reduce network congestion and improve user experience. However, deploying many distributed MEC servers at the edge of wireless networks is challenging not only in terms of managing resource allocation and distribution but also in regard to reducing network energy consumption. Here, we focus on the latter by assessing the network energy consumption of different cache updating and replacement algorithms. First, we introduce our proposed proactive caching (PC) algorithm for mobile edge caching with Zipf request patterns, which could potentially improve the cache hit rates compared to other caching algorithms such as least recently used, least frequently used, and popularity-based caching. Then, we present the energy assessment models for mobile edge caching by breaking down the total network energy consumption into transmission and storage energy consumption. Finally, we perform a comprehensive simulation to assess the energy consumption of the PC algorithm under different key factors and compare with that of conventional algorithms. The simulation results show that improving cache hit rates by using the PC algorithm comes at the expense of additional energy consumption for network transmission. INDEX TERMS Wireless edge caching, energy consumption, 5G, multiaccess edge computing, proactive caching.

Internet traffic has grown rapidly in recent years and is expected to continue to expand significantly over the next decade. Consequently, the resulting greenhouse gas (GHG) emissions of telecommunications service-supporting infrastructures have become an important issue. In this study, we develop a set of models for assessing the use-phase power consumption and carbon dioxide emissions of telecom network services to help telecom providers gain a better understanding of the GHG emissions associated with the energy required for their networks and services. Due to the fact that measuring the power consumption and traffic in a telecom network is a challenging task, these models utilize different granularities of available network information. As the granularity of the network measurement information decreases, the corresponding models have the potential to produce larger estimation errors. Therefore, we examine the accuracy of these models under various network scenarios using two approaches: (i) a sensitivity analysis through simulations and (ii) a case study of a deployed network. Both approaches show that the accuracy of the models depends on the network size, the total amount of network service traffic (i.e., for the service under assessment), and the number of network nodes used to process the service.