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View all- Dlamini TVilakati S(2020)LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge SystemWireless Communications & Mobile Computing10.1155/2020/88253962020Online publication date: 15-Dec-2020
Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities
Authors: 
Thembelihle Dlamini, Ángel Fernández Gambín, Daniele Munaretto, Michele Rossi Academic Editor: Gianluigi FerrariAuthors Info & Claims
Thembelihle Dlamini, Ángel Fernández Gambín, Daniele Munaretto, Michele Rossi Academic Editor: Gianluigi FerrariAuthors Info & ClaimsAbstract
The convergence of communication and computing has led to the emergence of multi-access edge computing (MEC), where computing resources (supported by virtual machines (VMs)) are distributed at the edge of the mobile network (MN), i.e., in base stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with energy harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called Energy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and Quality of Service (QoS) provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% to 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.
References
[1]
“Five Trends to Small Cells 2020,” Huawei Technologies, Helsinki, Finland, 2016.
[2]
M. Patel, Y. Hu, P. Hédé, J. Joubert, C. Thornton, B. Naughton, J. R. Ramos, C. Chan, V. Young, S. J. Tan, D. Lynch, N. Sprecher, T. Musiol, C. Manzanares, U. Rauschenbach, S. Abeta, L. Chen, K. Shimizu, A. Neal, P. Cosimini, A. Pollard, and G. Klas, “Mobile edge computing introductory technical white paper,” ETSI, Sophia-Antipolis, France, 2014.
[3]
E. Oh, B. Krishnamachari, X. Liu, and Z. Niu, “Toward dynamic energy-efficient operation of cellular network infrastructure,” IEEE Communications Magazine, vol. 49, no. 6, pp. 56–61, 2011.
[4]
J. Erman and K. Ramakrishnan, “Understanding the super-sized traffic of the super bowl,” in Proceedings of the 2013 conference on Internet measurement conference, Barcelona, Spain, October 2013.
[5]
R. Morabito, “Power consumption of virtualization technologies: An empirical investigation,” in Proceedings of the in IEEE International Conference on Utility and Cloud Computing (UCC), Limassol, Cyprus, Dec 2015.
[6]
Y. Jin, Y. Wen, and Q. Chen, “Energy efficiency and server virtualization in data centers: An empirical investigation,” in proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM Workshops), Orlando, FL, USA, Mar 2012.
[7]
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, Mass, USA, 2016.
[8]
S. Abdelwahed, N. Kandasamy, and S. Neema, “Online control for self-management in computing systems,” in Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Ontario, Canada, 2004.
[9]
T. Dlamini, A. F. Gambin, D. Munaretto, and M. Rossi, “Online resource management in energy harvesting bs sites through prediction and soft-scaling of computing resources,” in Proceedings of the 2018 IEEE 29th annual international symposium on personal, indoor and mobile radio communications (PIMRC), Bologna, Italy, September 2018.
[10]
“Open Big Data Challenge,” https://dandelion.eu/datamine/open-big-data/.
[11]
“Solar Radiation Measurement Data,” https://energydata.info/dataset/armenia-solar-radiation-measurement-data-2017.
[12]
H. Zhang, J. Cai, and X. Li, “Energy-efficient base station control with dynamic clustering in cellular network,” in Proceedings of the IEEE International Conference on Communications and Networking (CHINACOM), Guilin, China, August 2013.
[13]
S. Samarakoon, M. Bennis, W. Saad, and M. Latva-Aho, “Dynamic clustering and on/off strategies for wireless small cell networks,” IEEE Transactions on Wireless Communications, vol. 15, no. 3, pp. 2164–2178, 2016.
[14]
S. Cai, L. Xiao, H. Yang, J. Wang, and S. Zhou, “A cross-layer optimization of the joint macro-and picocell deployment with sleep mode for green communications,” in Proceedings of the 22nd Wireless and Optical Communications Conference, WOCC 2013, Chongqing, China, May 2013.
[15]
Y. Zhu, Z. Zeng, T. Zhang, and D. Liu, “A QoS-aware adaptive access point sleeping in relay cellular networks for energy efficiency,” in Proceedings of the IEEE Vehicular Technology Conference (VTC Spring), Seoul, Korea, May 2014.
[16]
Y. Yuan and P. Gong, “A QoE-orientated base station sleeping strategy for multi-services in cellular networks,” in Proceedings of the International Conference on Wireless Communications and Signal Processing, (WCSP 2015), Nanjing, China, October 2015.
[17]
F. Han, Z. Safar, and K. J. R. Liu, “Energy-efficient base-station cooperative operation with guaranteed QoS,” IEEE Transactions on Communications, vol. 61, no. 8, pp. 3505–3517, 2013.
[18]
C. Liu, Y. Wan, L. Tian, Y. Zhou, and J. Shi, “Base station sleeping control with energy-stability tradeoff in centralized radio access networks,” in Proceedings of the IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA, December 2015.
[19]
A. Bousia, A. Antonopoulos, L. Alonso, and C. Verikoukis, “‘Green’ distance-aware base station sleeping algorithm in LTE-Advanced,” in Proceedings of the IEEE International Conference on Communications (ICC '12), pp. 1347–1351, Ottawa, Canada, June 2012.
[20]
H. Tabassum, U. Siddique, E. Hossain, and M. J. Hossain, “Downlink performance of cellular systems with base station sleeping, user association, and scheduling,” IEEE Transactions on Wireless Communications, vol. 13, no. 10, pp. 5752–5767, 2014.
[21]
Y. Zhu, Z. Zeng, T. Zhang, L. An, and L. Xiao, “An energy efficient user association scheme based on cell sleeping in LTE heterogeneous networks,” in Proceedings of the 2014 International Symposium on Wireless Personal Multimedia Communications (WPMC), pp. 75–79, Sydney, Australia, September 2014.
[22]
A. Bousia, E. Kartsakli, A. Antonopoulos, L. Alonso, and C. Verikoukis, “Multiobjective auction-based switching-off scheme in heterogeneous networks: to bid or not to bid?” IEEE Transactions on Vehicular Technology, vol. 65, no. 11, pp. 9168–9180, 2016.
[23]
B. Z. Dongsheng Han and Z. Chen, “Sleep mechanism of base station based on minimum energy cost,” Wireless Communications and Mobile Computing, vol. 2018, 13 pages, 2018.
[24]
M. D'Amours, A. Girard, and B. Sanso, “Planning solar in energy-managed cellular networks,” IEEE Access, vol. 6, pp. 65212–65226, 2018.
[25]
N. Piovesan, A. Fernandez Gambin, M. Miozzo, M. Rossi, and P. Dini, “Energy sustainable paradigms and methods for future mobile networks: A survey,” Elsevier - Computer Communications, vol. 119, pp. 101–117, 2018.
[26]
D. Thembelihle, M. Rossi, and D. Munaretto, “Softwarization of mobile network functions towards agile and energy efficient 5G architectures: a survey,” Wireless Communications and Mobile Computing, vol. 2017, 21 pages, 2017.
[27]
A. Antonopoulos, E. Kartsakli, A. Bousia, L. Alonso, and C. Verikoukis, “Energy-efficient infrastructure sharing in multi-operator mobile networks,” IEEE Communications Magazine, vol. 53, no. 5, pp. 242–249, 2015.
[28]
M. Oikonomakou, A. Antonopoulos, L. Alonso, and C. Verikoukis, “Evaluating cost allocation imposed by cooperative switching off in multi-operator shared HetNets,” IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 11352–11365, 2017.
[29]
J. Maciejowski, Predictive Control with Constraints, Prentice Hall, 2002.
[30]
S.-L. Chung, S. Lafortune, and F. Lin, “Limited lookahead policies in supervisory control of discrete event systems,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 37, no. 12, pp. 1921–1935, 1992.
[31]
T. Ergen and S. S. Kozat, “Online training of LSTM networks in distributed systems for variable length data sequences,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 5159–5165, 2017.
[32]
J. Xu and S. Ren, “Online learning for offloading and autoscaling in renewable-powered mobile edge computing,” in Proceedings of the 59th IEEE Global Communications Conference, GLOBECOM 2016, Washington, DC, USA, December 2016.
[33]
A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, 2012.
[34]
R. Nathuji and K. Schwan, “VirtualPower: coordinated power management in virtualized enterprise systems,” in Proceedings of the 21st ACM SIGOPS symposium on operating systems principles, SOSP'07, pp. 265–278, Washington, DC, USA, October 2007.
[35]
M. Nelson, B.-H. Lim, and G. Hutchins, “Fast transparent migration for virtual machines,” in Proceedings of the annual conference on usenix annual technical conference, Berkeley, calif, USA, Apr 2005.
[36]
C. Jeffrey, A. Darrell, T. Prachi, V. Amin, and D. Ronald, “Managing energy and server resources in hosting centers,” in Proceedings of the 18th ACM Symposium on Operating Systems Principles, Alberta, Canada, Oct 2001.
[37]
J. Lorch and A. J. Smith, “PACE: A new approach to dynamic voltage scaling,” IEEE Transactions on Computers, vol. 53, no. 7, pp. 856–869, 2004.
[38]
R. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, OTexts, Melbourne, Australia, 2013.
[39]
Network., Network Functions Virtualisation (NFV): Hypervisor Domain, ETSI, Sophia-Antipolis, France, Jan 2015.
[40]
“3GPP TS 32.2.297, charging data rececord (CDR) file format and transfer,” ETSI, Sophia-Antipolis, France, 2016.
[41]
C. Peng, S.-B. Lee, S. Lu, H. Luo, and H. Li, “Traffic-driven power saving in operational 3G cellular networks,” in Proceedings of the 17th Annual International Conference on Mobile Computing and Networking (MobiCom '11), pp. 121–132, Las Vegas, Nev, USA, September 2011.
[42]
D. Pelleg and A. W. Moore, “X-means: Extending K-means with efficient estimation of the number of clusters,” in Proceedings of the Seventeenth International Conference on Machine Learning (ICML), San Francisco, calif, USA, Jun 2000.
[43]
F. B. Abdesslem and A. Lindgren, “Large scale characterisation of YouTube requests in a cellular network,” in Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 1–9, Sydney, Australia, June 2014.
[44]
L. Chen, S. Zhou, and J. Xu, “Energy efficient mobile edge computing in dense cellular networks,” in Proceedings of the IEEE International Conference on Communications (ICC), pp. 1–6, Paris, France, May 2017.
[45]
P.-S. Yu, J. Lee, T. Q. S. Quek, and Y.-W. P. Hong, “Traffic offloading in heterogeneous networks with energy harvesting personal cells-network throughput and energy efficiency,” IEEE Transactions on Wireless Communications, vol. 15, no. 2, pp. 1146–1161, 2016.
[46]
J. Wu, Y. Bao, G. Miao, S. Zhou, and Z. Niu, “Base-station sleeping control and power matching for energy-delay tradeoffs with bursty traffic,” IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 3657–3675, 2016.
[47]
L. Haikun, X. Cheng-Zhong, J. Hai, G. Jiayu, and L. Xiaofei, “Performance and energy modeling for live migration of virtual machines,” in Proceedings of the 20th international symposium on high performance distributed computing, California, calif, USA, Jun 2011.
[48]
“Virtualization for small cells: Overview,” Small Cell Forum, Draycott, England, 2015.
[49]
K. Li, “Performance analysis of power-aware task scheduling algorithms on multiprocessor computers with dynamic voltage and speed,” IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 11, pp. 1484–1497, 2008.
[50]
M. Shojafar, N. Cordeschi, and E. Baccarelli, “Energy-efficient adaptive resource management for real-time vehicular cloud services,” IEEE Transactions on Cloud Computing, 2016.
[51]
C. Canali, L. Chiaraviglio, R. Lancellotti, and M. Shojafar, “Joint minimization of the energy costs from computing, data transmission, and migrations in cloud data centers,” IEEE Transactions on Green Communications and Networking, vol. 2, no. 2, pp. 580–595, 2018.
[52]
M. Chen, W. Saad, and C. Yin, “Machine learning for wireless networks with artificial intelligence: a tutorial on neural networks,” IEEE Wireless Communications, 2017, https://arxiv.org/abs/1710.02913.
[53]
C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo, “Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98–105, 2017.
[54]
O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook, Springer, 2010.
[55]
E. Oh, K. Son, and B. Krishnamachari, “Dynamic base station switching-on/off strategies for green cellular networks,” IEEE Transactions on Wireless Communications, vol. 12, no. 5, pp. 2126–2136, 2013.
[56]
“Standard Performance Evaluation Corporation,” SPEC, Virginia, USA, https://www.spec.org/virt_sc2013/results/res2013q2/.
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Copyright © 2019 Thembelihle Dlamini et al.
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Published: 01 January 2019
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View all- Dlamini TVilakati S(2020)LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge SystemWireless Communications & Mobile Computing10.1155/2020/88253962020Online publication date: 15-Dec-2020
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