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Managing surges in online demand using bandwidth throttling: An optimal strategy amid the COVID-19 pandemic

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  • Gupta, Varun
  • Perera, Sandun

Abstract

Managing supply chains requires quick access to the enterprise– and customer–facing online network applications and transparency of data across supply chains. While it is challenging to meet the rapidly growing demand for bandwidth to support supply chain applications in the Blockchain era, the COVID-19 pandemic has triggered bandwidth demand surges across all online services such as the internet, content delivery, e-platforms, and social media. A sudden demand surge may impel users to lose their access or have a poor online experience due to bandwidth throttling by their Online Service Providers (OSPs). Earlier work on optimal throttling mechanism under stochastic demand has overlooked such demand surges. In this paper, we recast the user demand to adequately capture the demand surges, such as home bandwidth demand surges during the COVID-19 pandemic. Specifically, we model the user demand as a geometric Lévy (jump–diffusion) process. Within our general setting, we show that it is optimal for the OSP to follow a modified control-band policy that encompasses the existing results as a special case. Our numerical study not only enhances the current insights about the optimal throttling mechanism by including demand surges but also provides new insights concerning the nature of demand surges. For example, our study suggests that OSPs should initiate demand throttling at relatively lower usage levels if the likelihood of having demand surges is high; furthermore, we find that when OSPs are exposed to demand surges with higher intensities, it is optimal for them to wait until the usage level reaches a relatively high level before throttling it.

Suggested Citation

  • Gupta, Varun & Perera, Sandun, 2021. "Managing surges in online demand using bandwidth throttling: An optimal strategy amid the COVID-19 pandemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:transe:v:151:y:2021:i:c:s1366554521001113
    DOI: 10.1016/j.tre.2021.102339
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    as
    1. S. G. Kou, 2002. "A Jump-Diffusion Model for Option Pricing," Management Science, INFORMS, vol. 48(8), pages 1086-1101, August.
    2. Ren, Shuyun & Choi, Tsan-Ming & Lee, Ka-Man & Lin, Lei, 2020. "Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    3. Wada, Kentaro & Akamatsu, Takashi, 2013. "A hybrid implementation mechanism of tradable network permits system which obviates path enumeration: An auction mechanism with day-to-day capacity control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 60(C), pages 94-112.
    4. Ward Whitt, 1981. "The Stationary Distribution of a Stochastic Clearing Process," Operations Research, INFORMS, vol. 29(2), pages 294-308, April.
    5. Rahul R. Marathe & Sarah M. Ryan, 2009. "Capacity expansion under a service‐level constraint for uncertain demand with lead times," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(3), pages 250-263, April.
    6. Buckley, Winston & Long, Hongwei & Perera, Sandun, 2014. "A jump model for fads in asset prices under asymmetric information," European Journal of Operational Research, Elsevier, vol. 236(1), pages 200-208.
    7. Winston Buckley & Sandun Perera, 2019. "Optimal demand in a mispriced asymmetric Carr–Geman–Madan–Yor (CGMY) economy," Annals of Finance, Springer, vol. 15(3), pages 337-368, September.
    8. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 456-499.
    9. Sarah M. Ryan, 2004. "Capacity Expansion for Random Exponential Demand Growth with Lead Times," Management Science, INFORMS, vol. 50(6), pages 740-748, June.
    10. Jiang, George J. & Oomen, Roel C.A., 2008. "Testing for jumps when asset prices are observed with noise-a "swap variance" approach," Journal of Econometrics, Elsevier, vol. 144(2), pages 352-370, June.
    11. Merton, Robert C., 1971. "Optimum consumption and portfolio rules in a continuous-time model," Journal of Economic Theory, Elsevier, vol. 3(4), pages 373-413, December.
    12. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    13. Jahani, Hamed & Abbasi, Babak & Alavifard, Farzad & Talluri, Srinivas, 2018. "Supply chain network redesign with demand and price uncertainty," International Journal of Production Economics, Elsevier, vol. 205(C), pages 287-312.
    14. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    15. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    16. Govindan, Kannan & Mina, Hassan & Alavi, Behrouz, 2020. "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    17. Sandun Perera & Winston Buckley, 2017. "On the existence and uniqueness of the optimal central bank intervention policy in a forex market with jumps," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(8), pages 877-885, August.
    18. Lieberman, Marvin B., 1989. "Capacity utilization: Theoretical models and empirical tests," European Journal of Operational Research, Elsevier, vol. 40(2), pages 155-168, May.
    19. Corsi, Thomas M. & Boyson, Sandor, 2003. "Real-time e-supply chain management: diffusion of new technologies and business practices," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 39(2), pages 79-82, March.
    20. Perera, Sandun & Gupta, Varun & Buckley, Winston, 2020. "Management of online server congestion using optimal demand throttling," European Journal of Operational Research, Elsevier, vol. 285(1), pages 324-342.
    21. He, Bo & Gupta, Varun & Mirchandani, Prakash, 2021. "Online selling through O2O platform or on your own? Strategic implications for local Brick-and-Mortar stores," Omega, Elsevier, vol. 103(C).
    22. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    23. Choi, Tsan-Ming, 2020. "Internet based elastic logistics platforms for fashion quick response systems in the digital era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    24. Chowdhury, Priyabrata & Paul, Sanjoy Kumar & Kaisar, Shahriar & Moktadir, Md. Abdul, 2021. "COVID-19 pandemic related supply chain studies: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    25. Sandun Perera & Winston Buckley & Hongwei Long, 2018. "Market-reaction-adjusted optimal central bank intervention policy in a forex market with jumps," Annals of Operations Research, Springer, vol. 262(1), pages 213-238, March.
    26. Abel Cadenillas & Peter Lakner & Michael Pinedo, 2010. "Optimal Control of a Mean-Reverting Inventory," Operations Research, INFORMS, vol. 58(6), pages 1697-1710, December.
    27. Parajuli, Anubhuti & Kuzgunkaya, Onur & Vidyarthi, Navneet, 2021. "The impact of congestion on protection decisions in supply networks under disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    28. Wu, Weitiao & Li, Peng & Liu, Ronghui & Jin, Wenzhou & Yao, Baozhen & Xie, Yuanqi & Ma, Changxi, 2020. "Predicting peak load of bus routes with supply optimization and scaled Shepard interpolation: A newsvendor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    29. Lester Blackmon & Ross Chan & Omar Carbral & Geeta Chintapally & Sandip Dhara & Peter Felix & Aditi Jagdish & Srini Konakalla & Jasbir Labana & Jeff McIlvain & Jason Stone & Christopher S. Tang & Jaso, 2021. "Rapid Development of a Decision Support System to Alleviate Food Insecurity at the Los Angeles Regional Food Bank amid the COVID‐19 Pandemic," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3391-3407, October.
    30. LeRoy, Stephen F & Porter, Richard D, 1981. "The Present-Value Relation: Tests Based on Implied Variance Bounds," Econometrica, Econometric Society, vol. 49(3), pages 555-574, May.
    31. Bailey, Joseph P. & Rabinovich, Elliot, 2005. "Internet book retailing and supply chain management: an analytical study of inventory location speculation and postponement," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(3), pages 159-177, May.
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