Fatigue-Aware Management of Cellular Networks Infrastructure with Sleep Modes

We consider the problem of controlling the rate of failures triggered by fatigue processes of Base Stations (BSs) in cellular networks subject to Sleep Modes (SMs). Specifically, the increase of time spent in SM tends to decrease the BS failure rate by following, e.g., the Arrhenius law. However, the transitions between the power states tend to increase the BS failure rate, which can be predicted by the Coffin-Manson model. In this context, the energy savings triggered by SMs would not be economically useful if the BS failure rate were increased too much. Our goal is therefore to tackle the problem of minimizing the BS failure rate in a cellular network subject to SMs. After showing that the optimal formulation of the problem is NP-Hard, we propose a new algorithm, named LIFE, to practically solve it. We run LIFE on different scenarios (driven by LTE and legacy UMTS technologies). Our results show that LIFE outperforms two previous energy-aware algorithms, which instead do not take into account the BS failure rate. Specifically, our solution is able to achieve up to 40 percent of power saving at night, without a strong penalty in the BS failure rate.

[1]  Jwo Pan,et al.  Fatigue Testing and Analysis: Theory and Practice , 2004 .

[2]  Leandros Tassiulas,et al.  Transmit beamforming and power control for cellular wireless systems , 1998, IEEE J. Sel. Areas Commun..

[3]  Jeroen Wigard,et al.  COST Action 231: Digital Mobile Radio Towards Future Generation System, Final Report. , 1999 .

[4]  Moshe Zukerman,et al.  Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[5]  Josip Lorincz,et al.  A measurement study of short-time cell outages in mobile cellular networks , 2016, Comput. Commun..

[6]  Guna S Selvaduray,et al.  Solder joint fatigue models: review and applicability to chip scale packages , 2000 .

[7]  Leandros Tassiulas,et al.  Resource Allocation and Cross-Layer Control in Wireless Networks , 2006, Found. Trends Netw..

[8]  Vera Stavroulaki,et al.  5G on the Horizon: Key Challenges for the Radio-Access Network , 2013, IEEE Vehicular Technology Magazine.

[9]  Deep Medhi,et al.  Routing, flow, and capacity design in communication and computer networks , 2004 .

[10]  Zhifeng Zhao,et al.  On the $\alpha$-Stable Distribution of Base Stations in Cellular Networks , 2015, IEEE Communications Letters.

[11]  J. Newman A crack-closure model for predicting fatigue crack growth under aircraft spectrum loading , 1981 .

[12]  Josip Lorincz,et al.  Is green networking beneficial in terms of device lifetime? , 2015, IEEE Communications Magazine.

[13]  G. Reimbold,et al.  Experimental and theoretical investigation of nonvolatile memory data-retention , 1999 .

[14]  Rudolf Mathar,et al.  Power control, capacity, and duality of uplink and downlink in cellular CDMA systems , 2004, IEEE Transactions on Communications.

[15]  Klaudia Frankfurter Computers And Intractability A Guide To The Theory Of Np Completeness , 2016 .

[16]  Shanzhi Chen,et al.  The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication , 2014, IEEE Communications Magazine.

[17]  Leandros Tassiulas,et al.  Energy-efficient planning and management of cellular networks , 2012, 2012 9th Annual Conference on Wireless On-Demand Network Systems and Services (WONS).

[18]  Zhisheng Niu,et al.  Toward dynamic energy-efficient operation of cellular network infrastructure , 2011, IEEE Communications Magazine.

[19]  Josip Lorincz,et al.  Modeling the Impact of Power State Transitions on the Lifetime of Cellular Networks , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[20]  Rudolf Mathar,et al.  Dynamic Downlink Power Control Strategies for LTE Femtocells , 2013, 2013 Seventh International Conference on Next Generation Mobile Apps, Services and Technologies.

[21]  Marco Listanti,et al.  Sleep to stay alive: Optimizing reliability in energy-efficient backbone networks , 2015, 2015 17th International Conference on Transparent Optical Networks (ICTON).

[22]  Hamid Aghvami,et al.  Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation: Theory and Practice , 2006 .

[23]  Dirk Wübben,et al.  Cloud technologies for flexible 5G radio access networks , 2014, IEEE Communications Magazine.

[24]  Marco Listanti,et al.  Sleep to Stay Healthy: Managing the Lifetime of Energy-Efficient Cellular Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[25]  Josip Lorincz,et al.  Optimized network management for energy savings of wireless access networks , 2011, Comput. Networks.

[26]  Leandros Tassiulas,et al.  Dynamic Resource Provisioning for Energy Efficiency in Wireless Access Networks: A Survey and an Outlook , 2014, IEEE Communications Surveys & Tutorials.

[27]  Weiwen Peng,et al.  Thermal Cycling Life Prediction of Sn-3.0Ag-0.5Cu Solder Joint Using Type-I Censored Data , 2014, TheScientificWorldJournal.

[28]  Raj Jain,et al.  Network virtualization and software defined networking for cloud computing: a survey , 2013, IEEE Communications Magazine.

[29]  Carlo Ratti,et al.  Towards a comparative science of cities: using mobile traffic records in New York, London and Hong Kong , 2014, ArXiv.

[30]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[31]  Bianca Schroeder,et al.  Temperature management in data centers: why some (might) like it hot , 2012, SIGMETRICS '12.

[32]  Luca Venturino,et al.  Energy-Efficient Scheduling and Power Allocation in Downlink OFDMA Networks With Base Station Coordination , 2014, IEEE Transactions on Wireless Communications.

[33]  Reza Ghaffarian,et al.  Accelerated Thermal Cycling and Failure Mechanisms for BGA and CSP Assemblies , 2000 .

[34]  Marco Ajmone Marsan,et al.  Cell wilting and blossoming for energy efficiency , 2011, IEEE Wireless Communications.

[35]  Josip Lorincz,et al.  Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads † , 2012, Sensors.

[36]  Zhisheng Niu,et al.  TANGO: traffic-aware network planning and green operation , 2011, IEEE Wireless Communications.

[37]  Andrzej Jajszczyk,et al.  Routing, Flow, and Capacity Design in Communication and Computer Networks - [Book Review] , 2005, IEEE Communications Magazine.

[38]  Approaches to Technology of Thermal Fatigue Life Prediction of Solder Joints , .

[39]  Liesbet Van der Perre,et al.  Challenges and enabling technologies for energy aware mobile radio networks , 2010, IEEE Communications Magazine.

[40]  D. Colle,et al.  Worldwide electricity consumption of communication networks. , 2012, Optics express.

[41]  Junyi Li,et al.  Network densification: the dominant theme for wireless evolution into 5G , 2014, IEEE Communications Magazine.

[42]  Vijay K. Bhargava,et al.  Green Cellular Networks: A Survey, Some Research Issues and Challenges , 2011, IEEE Communications Surveys & Tutorials.

[43]  Palo Alto,et al.  Impact of Intermetallic Growth on the Mechanical Strength of Lead-Free BGA Assemblies , 2000 .

[44]  Marco Listanti,et al.  Lifetime awareness in backbone networks with sleep modes , 2015, 2015 7th International Workshop on Reliable Networks Design and Modeling (RNDM).