Abstract
The massive deployment of small cell Base Stations (SBSs) empowered with computing capabilities presents one of the most ingenious solutions adopted for 5G cellular networks towards meeting the foreseen data explosion and the ultralow latency demanded by mobile applications. This empowerment of SBSs with Multi-access Edge Computing (MEC) has emerged as a tentative solution to overcome the latency demands and bandwidth consumption required by mobile applications at the network edge. The MEC paradigm offers a limited amount of resources to support computation, thus mandating the use of intelligence mechanisms for resource allocation. The use of green energy for powering the network apparatuses (e.g., Base Stations (BSs), MEC servers) has attracted attention towards minimizing the carbon footprint and network operational costs. However, due to their high intermittency and unpredictability, the adoption of learning methods is a requisite. Towards intelligent edge system management, this paper proposes a Green-based Edge Network Management (GENM) algorithm, which is an online edge system management algorithm for enabling green-based load balancing in BSs and energy savings within the MEC server. The main goal is to minimize the overall energy consumption and guarantee the Quality of Service (QoS) within the network. To achieve this, the GENM algorithm performs dynamic management of BSs, autoscaling and reconfiguration of the computing resources, and on/off switching of the fast tunable laser drivers coupled with location-aware traffic scheduling in the MEC server. The obtained simulation results validate our analysis and demonstrate the superior performance of GENM compared to a benchmark algorithm.
Highlights
The foreseen dense deployment of BS empowered with computing capabilities in order to meet the ultralow latency demanded by mobile users raises concerns related to energy consumption
This paper considers an energy cost model that takes into account the computing, caching and communication processes within the Multi-access Edge Computing (MEC) server, and transmission-related energy consumption in BS
The mobile traces are aggregated from 10 observation time to τ
Summary
The foreseen dense deployment of BS empowered with computing capabilities in order to meet the ultralow latency demanded by mobile users raises concerns related to energy consumption. Apart from the fact that BS energy costs account for a large part of the operating expenses of MN operators, there are increasing concerns regarding their environmental impact in terms of high carbon dioxide (CO2) emissions. In an effort to minimize energy consumption and energy costs in 5G cellular networks within the MEC paradigm, this paper advocates for the integration of EH systems into network apparatuses and the use of container-based virtualization within computing platforms (i.e., MEC servers). The use of green energy mitigates the negative environmental impact of MN and enables cost-saving for mobile operators in terms of lowering operational energy costs. The benefits of container-based virtualization is the reduction in energy drained in the computing platform due to their lower overheads when compared with VM [1, 2]. For a qualitative comparison of different virtualization techniques, interested readers are referred to [1]
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