Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableMobility-Aware Energy Optimization in Hosts Selection for Computation Offloading in Multi-Access Edge Computing(Institute of Electrical and Electronics Engineers (IEEE), 2020-07-15)Multi-access edge computing (MEC) has been proposed as an approach capable of addressing latency and bandwidth issues in application computation offloading to extend the capabilities beyond the computational and storage limitations of mobile devices. However, there is a critical challenge in MEC to maintain the service continuity between the offloaded user application that is running on the MEC host and the mobile device when a user is moving from radio node to radio node. Furthermore, energy consumption of application computation offloading is an important consideration for MEC service providers in terms of operational costs. Therefore, we formulate the MEC host selection and user application migration problem as a shortest path problem of network energy minimization. We simulate the problem in a hierarchical MEC network deployment environment. We also propose the metric, computational intensity (CI), that can be used by MEC service providers to address the MEC host selection problem. Our results show that with the increment of CI, the selection of MEC hosts tends to move toward level 3 (central deployment) due to energy efficiency and then return to the deployment at level 1 (radio node level) due to latency constraint of the user application. We show that with high accuracy in predicting the user mobility and the available resources in the MEC network, latency- and mobility-aware MEC host selection and user application migration can be pre-calculated to improve response time and energy efficiency.
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ItemNetwork Energy Consumption Assessment of Conventional Mobile Services and Over-the-Top Instant Messaging Applications(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-12)The rapid growth in the energy consumption of mobile networks has become a major concern for mobile operators. Today’s mobile networks’ usage is dominated by over-the-top (OTT) applications and operators are keen to determine the network energy consumed by these OTT applications. With a recent shift in user behavior towards a preference for instant messaging (IM) applications over conventional mobile services, operators are interested in exploring what impact OTT IM applications such as WeChat will have on the energy consumption of a network when compared to a corresponding conventional mobile service. Here, we present for the first time energy assessment models for mobile services based on real network and service measurements to address this need. Using WeChat as an OTT IM application example, our results show that WeChat consumes more network energy than conventional mobile services for both light users and heavy text users due to the network signaling energy overhead. In comparison, for heavy voice users, WeChat consumes less network energy since voice messages are first recorded and then sent in packet bursts. Our findings provide a quantitative analysis of the energy consumption of mobile services, which should be valuable for mobile operators and OTT application developers to improve the energy-efficiency of mobile applications and services.
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ItemTelecommunications energy and greenhouse gas emissions management for future network growth(ELSEVIER SCI LTD, 2016-03-15)A key aspect of greener network deployment is how to achieve sustainable growth of a telecommunications network, both in terms of operational and embodied energy. Hence, in this paper we investigate how the overall energy consumption and greenhouse gas emissions of a fast growing telecommunications network can be minimized. Due to the complexities in modeling the embodied energy of networks, this aspect of energy consumption has received limited attention by network operators. Here, we present the first model to evaluate the interdependencies of the four main contributing factors in managing the sustainable growth of a telecommunications network: (i) the network’s operational energy consumption; (ii) the embodied energy of network equipment; (iii) network traffic growth; and (iv) the expected energy efficiency improvements in both the operational and embodied phases. Using Monte Carlo techniques with real network data, our results demonstrate that under the current trends in overall energy efficiency improvements the network embodied energy will account for over 40% of the total network energy in 2025 compared to 20% in 2015. Further, we find that the optimum equipment replacement cycle, which will result in the lowest total network life cycle energy, is directly dependent on the technological progress in energy efficiency improvements of both operational and embodied phases. Our model and analysis highlight the need for a comprehensive approach to better understand the interactions between network growth, technological progress, equipment replacement lifetime, energy consumption, and the resulting carbon footprint.