Abstract
In this paper the problem of guaranteeing Quality of Service via optimal buffer allocation is considered: namely, we compute the optimal buffer with respect to a real-world dataset accounting for the expected data traffic volume for a predefined set of business users belonging to a mobile network scenario. Two distinct approaches are followed to tackle this issue, in a static and dynamic fashion, respectively: the former relies on nonlinear programming, while the latter relies on model predictive control. The proposed formulation in terms of optimal static buffer allocation enables the minimization of the wasted amount of data traffic in order to prevent users from paying the assignment of extra resources in addition to the available traffic bundle. Instead, optimal dynamic buffer allocation pushes resource optimization forward by enabling personalization: indeed, tracking the prediction of data traffic consumption for each user allows to satisfy personalized Quality of Service guarantees. Numerical simulations show the effectiveness of the proposed approaches in terms of buffer saving and decrease in Quality of Service mismatch.
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References
A. Nandan, R. Marimuthu, and M. Kurth, Magic Quadrant for Operations Support Systems, Gartner Research, 2019.
C. Lai, Y. Chang, H. Chao, M. S. Hossain, and A. Ghoneim, “A buffer-aware QoS streaming approach for SDN-enabled 5G vehicular networks,” IEEE Communications Magazine, vol. 55, pp. 68–73, 2017.
S. Weiss, J. A. Schwarz, and R. Stolletz, “The buffer allocation problem in production lines: formulations, solution methods and instances,” IISE Transactions, vol. 51, no. 5, pp. 456–485, 2019.
F. Liu and C.-H. Fu, “Improving mobile network performance with two queueing buffer allocations of prioritybased queueing scheme,” Wireless Networks, vol. 20, pp. 1349–1367, 2014.
Y. Feng and B. Xiao, “A continuous-time seat control model for single-leg flights with no-shows and optimal overbooking upper bound,” European Journal of Operational Research, vol. 174, pp. 1298–1316, 2006.
Z. Liu, Q. Zhang, R. Ahmed, R. Boutaba, Y. Liu, and Z. Gong, “Dynamic resource allocation for MapReduce with partitioning skew,” IEEE Transactions on Computers, vol. 65, no. 11, pp. 3304–3317, 2016.
X. Xu, S. Fu, Q. Cai, W. Tian, W. Liu, W. Dou, X. Sun, and A. X. Liu, “Dynamic resource allocation for load balancing in fog environment,” Wireless Communications and Mobile Computing, pp. 1–15, 2018.
K. Zhang, S. Ge, and Y. Ge, “Adaptive dynamic programming for minimal energy control with guaranteed convergence rate of linear systems,” International Journal of Control, Automation and Systems, vol. 17, pp. 3140–4148, 2019.
L. de Cicco, S. Mascolo, V. Palmisano, and G. Ribezzo, “Reducing the network bandwidth requirements for 360° immersive video streaming,” Internet Technology Letters, vol. 2, no. 4, e118, 2019.
A. Pietrabissa, L. R. Celsi, F. Cimorelli, V. Suraci, F. Delli Priscoli, A. Di Giorgio, A. Giuseppi, and S. Monaco, “Lyapunov-based design of a distributed wardrop loadbalancing algorithm with application to software-defined networking,” IEEE Transactions on Control Systems Technology, vol. 27, no. 5, pp. 1924–1936, 2019.
V. Suraci, L. R. Celsi, A. Giuseppi, and A. di Giorgio, “A distributed wardrop control algorithm for load balancing in smart grids,” Proc. of 25th Mediterranean Conference on Control and Automation (MED), pp. 761–767, 2017.
D. F. Ciocan and V. Farias, “Model predictive control for dynamic resource allocation,” Mathematics of Operations Research, vol. 37, pp. 501–525, 2012.
F. Borrelli, A. Bemporad, and M. Morari, Predictive Control for Linear and Hybrid Systems, Cambridge University Press, 2017.
I. Griva, S. G. Nash, and A. Sofer, Linear and Nonlinear Programming, SIAM, 2009.
https://www.whatsupgold.com/best-practices/trafficmonitoring-software/
D. Limon, I. Alvarado, T. Alamo, and E. F. Camacho, “MPC for tracking piecewise constant references for constrained linear systems,” Automatica, vol. 44, pp. 2382–2387, 2008.
K. Muske and J. Rawlings, “Model predictive control with linear models,” AIChE Journal, vol. 39, pp. 262–287, 1993.
E. G. Gilbert, and K. Tan, “Linear systems with state and control constraints: the theory and application of maximal output admissible sets,” IEEE Transactions on Automatic Control, vol. 36, pp. 1008–1020, 1991.
A. Caliciotti, G. Fasano, S. G. Nash, and M. Roma, “An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization,” Operations Research Letters, vol. 46, no. 1, pp. 7–12, 2018.
A. Caliciotti, G. Fasano, S. G. Nash, and M. Roma, “Data and performance profiles applying an adaptive truncation criterion, within linesearch-based truncated Newton methods, in large scale nonconvex optimization,” Data in Brief, vol. 17, pp. 246–255, 2018.
D. Q. Mayne, “Model predictive control: Recent developments and future promise,” Automatica, vol. 50, no. 12, pp. 2967–2986, 2014.
M. Mesbahi and M. Egerstedt, Graph Theoretic Methods in Multiagent Networks, Princeton University Press, 2010.
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Recommended by Associate Editor Choon Ki Ahn under the direction of Editor Fuchun Sun. This work was supported by ELIS Consulting & Labs within a collaboration with Vodafone. The work presented in this paper was carried out while Dr. Caliciotti was with ELIS Consulting & Labs and does not reflect the results of any activity carried out at Accenture S.p.A., to which the author is currently affiliated. Andrea Caliciotti and Lorenzo Ricciardi Celsi wish to thank Prof. G. Fasano, Ing. L. de Costanzo and Ing. M. O. Migliori for the helpful suggestions, as well as G. Oceana for the valuable data preparation activity.
Andrea Caliciotti received his Ph.D. degree in Operations Research from University of Rome, “La Sapienza” in 2018. His research interests include nonlinear optimization and preconditioning techniques.
Lorenzo Ricciardi Celsi received his Ph.D. degree (cum laude) in Sciences et Technologies de l’Information et de la Communication, Specialité Automatique from Université Paris-Saclay, Paris, France, in 2018. His research interests include nonlinear networked systems, cooperative control methodologies for multiagent systems, and applied research in spacecraft control and network science.
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Caliciotti, A., Celsi, L.R. On Optimal Buffer Allocation for Guaranteeing Quality of Service in Multimedia Internet Broadcasting for Mobile Networks. Int. J. Control Autom. Syst. 18, 3043–3050 (2020). https://doi.org/10.1007/s12555-019-0129-y
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DOI: https://doi.org/10.1007/s12555-019-0129-y