GreenSlice: Enabling renewable energy powered cellular base stations using asynchronous delivery

Abstract

Globally, there is a tremendous growth in mobile data traffic and mobile network operators (MNO) are investing heavily to expand and operate their networks to serve them. The operational expenditure incurred by MNOs are increasing much more compared to revenue from mobile data traffic, thereby decreasing their profit margins. One of the major components of operational expenditure is the cost of operating cellular base stations using grid power or diesel generators in the absence of grid power. MNOs in off-grid sites constantly rely on diesel generators to run base stations that is very expensive. At the same time, they also cause adverse environmental impact due to C02 emission. While the economics of renewable energy are already favorable in remote off-grid areas, it has been quite challenging for operators to handle the unpredictability of renewable energy sources while meeting the user QoE. In this work, we present GreenSlice, an 'energy-source' aware asynchronous content deliv ery mechanism that adapts delivery of bulk delay-tolerant content in periods of availability of renewable energy. In particular, given a set of heavy demands (such as downloads of videos, software updates etc.), we present a scheduling algorithm that balances the operator's energy cost and delay experienced by users. We use a novel adaptation of Lyapunov optimization framework to provide bounds on the content delivery time and resulting operational cost. Simulation studies show our mechanism reduces the energy cost by more than 33% compared to traditional schemes of using renewables and non-renewables without asynchronous content delivery, at the same time keeping the increase in delay negligible.

A survey on QoE-oriented wireless resources scheduling

“…Uplink (Essaili et al, 2011;Wu et al, 2012;Condoluci et al, 2017;Liu et al, 2018;Ranjan et al, 2018) Multi-cell (Zheng et al, 2014;Cho et al, 2015;Kim et al, 2015;Miller et al, 2015) Heterogeneous networks (Toseef et al, 2011;Jailton et al, 2013;Seyedebrahimi and Peng, 2015;Morel and Randriamasy, 2017;Abbas et al, 2017) Device-to-device communications (Zhu et al, 2015a;Hong et al, 2017;Biswash and Jayakody, 2018) Vehicular networks (Xu et al, 2013;Yaacoub et al, 2015;Ding et al, 2018) Cognitive radio networks (Jiang et al, 2012;Wu et al, 2013;He et al, 2016;Zhang et al, 2017;Piran et al, 2017;Lin et al, 2017) Relay networks (Reis et al, 2010;Wu et al, 2013;Bethanabhotla et al, 2016;Xiang et al, 2017;Fan and Zhao, 2018) Multi-user MIMO networks (Cao et al, 2012;Bethanabhotla et al, 2016;Chen et al, 2017;Huang and Zhang, 2018) Base stations energy consumption (Ma et al, 2012;Li et al, 2012;Gabale and Subramanian, 2014;Draxler et al, 2014;Sapountzis et al, 2014;…”

“…For instance, Ma et al (2012) proposed a framework to optimize the power allocation at the base station side, in order to provide a balance between energy efficiency and QoE for video streaming users, whereas Li et al (2012) devised a power allocation optimization method regarding the QoE for multi-services. In (Gabale and Subramanian, 2014), an "energy-source" based method for delivering multimedia content asynchronously is presented, which adjusts the consignment of delay-tolerant content according to the time intervals where the renewable energy is available, i.e., it tries to balance the service provider energy costs and the QoE (namely the delay) experienced by users. An approach to power-cycle base stations and to control the playback of video streams is presented in (Draxler et al, 2014), where the goal is to reduce the overall energy consumed by the base stations while maintaining a high QoE for the users.…”