Utility Maximizing Load Balancing Policies

References

• Benameur N, Fredj SB, Oueslati-Boulahia S, Roberts JW (2002) Quality of service and flow level admission control in the Internet. Comput. Networks 40(1):57–71.Google Scholar
• Bhamidi S, Budhiraja A, Dewaskar M (2022) Near equilibrium fluctuations for supermarket models with growing choices. Ann. Appl. Probab. (Institute of Mathematical Statistics), 32(3):2083–2138.Google Scholar
• Bramson M (1998) State space collapse with application to heavy traffic limits for multiclass queueing networks. Queueing Systems 30(1):89–140.Google Scholar
• Eschenfeldt P, Gamarnik D (2018) Join the shortest queue with many servers. The heavy-traffic asymptotics. Math. Oper. Res. 43(3):867–886.Link, Google Scholar
• Fontes LR (1989) A note on Kolmogorov backward equations. Brazilian J. Probability Statist. (Institute of Mathematical Statistics), 3(1):59–65.Google Scholar
• Gamarnik D, Tsitsiklis JN, Zubeldia M (2018) Delay, memory, and messaging tradeoffs in distributed service systems. Stochastic Systems 8(1):45–74.Link, Google Scholar
• Gamarnik D, Tsitsiklis JN, Zubeldia M (2020) A lower bound on the queueing delay in resource constrained load balancing. Ann. Appl. Probability 30(2):870–901.Google Scholar
• Gardner K, Stephens C (2019) Smart dispatching in heterogeneous systems. Performance Evaluation Rev. 47(2):12–14.Google Scholar
• Gardner K, Jaleel JA, Wickeham A, Doroudi S (2021) Scalable load balancing in the presence of heterogeneous servers. Performance Evaluation Rev. 48(3):37–38.Google Scholar
• Goldsztajn D, Borst SC, Van Leeuwaarden JS (2021a) Learning and balancing unknown loads in large-scale systems. Preprint, submitted December 16, https://arxiv.org/abs/2012.10142.Google Scholar
• Goldsztajn D, Ferragut A, Paganini F (2021b) Automatic cloud instance provisioning with quality and efficiency. Performance Evaluation 149–150:102209.Google Scholar
• Goldsztajn D, Ferragut A, Paganini F, Jonckheere M (2018) Controlling the number of active instances in a cloud environment. Performance Evaluation Rev. 45(3):15–20.Google Scholar
• Goldsztajn D, Borst SC, Van Leeuwaarden JS, Mukherjee D, Whiting PA (2022) Self-learning threshold-based load balancing. INFORMS J. Comput. 34(1):39–54.Google Scholar
• Hajek B (2006) Notes for ECE 467: Communication Network Analysis (University of Illinois at Urbana-Champaign, Urbana).Google Scholar
• Horváth IA, Scully Z, Van Houdt B (2019) Mean field analysis of join-below-threshold load balancing for resource sharing servers. Proc. ACM on Measurement and Anal. of Comput. Systems (ACM, New York), 3(3):1–21.Google Scholar
• Jaleel JA, Wickeham A, Doroudi S, Gardner K (2022) A general “power-of-d” dispatching framework for heterogeneous systems. Queueing Systems 102(3):431–480.Google Scholar
• Karthik A, Mukhopadhyay A, Mazumdar RR (2017) Choosing among heterogeneous server clouds. Queueing Systems 85(1):1–29.Google Scholar
• Key P, Massoulié L, Bain A, Kelly F (2004) Fair Internet traffic integration: Network flow models and analysis. Ann. Telecomm. 59(11):1338–1352.Google Scholar
• Lu Y, Xie Q, Kliot G, Geller A, Larus JR, Greenberg A (2011) Join-idle-queue: A novel load balancing algorithm for dynamically scalable web services. Performance Evaluation 68(11):1056–1071.Google Scholar
• Menich R, Serfozo RF (1991) Optimality of routing and servicing in dependent parallel processing systems. Queueing Systems 9(4):403–418.Google Scholar
• Mitzenmacher M (2001) The power of two choices in randomized load balancing. IEEE Trans. Parallel Distribution Systems 12(10):1094–1104.Google Scholar
• Mukherjee D, Borst SC, Van Leeuwaarden JS, Whiting PA (2018) Universality of power-of-d load balancing in many-server systems. Stochastic Systems 8(4):265–292.Link, Google Scholar
• Mukherjee D, Borst SC, Van Leeuwaarden JS, Whiting PA (2020) Asymptotic optimality of power-of-d load balancing in large-scale systems. Math. Oper. Res. 45(4):1535–1571.Link, Google Scholar
• Mukherjee D, Dhara S, Borst SC, Van Leeuwaarden JS (2017) Optimal service elasticity in large-scale distributed systems. Proc. ACM on Measurement and Anal. of Comput. Systems (ACM, New York, Philadelphia), 1(1):1–28.Google Scholar
• Mukhopadhyay A, Mazumdar RR, Guillemin F (2015a) The power of randomized routing in heterogeneous loss systems. Proc. 27th Internat. Teletraffic Congress (IEEE, New York), 125–133.Google Scholar
• Mukhopadhyay A, Karthik A, Mazumdar RR, Guillemin F (2015b) Mean field and propagation of chaos in multi-class heterogeneous loss models. Performance Evaluation 91:117–131.Google Scholar
• Rudin W (1976) Principles of Mathematical Analysis, vol. 3 (McGraw-Hill, New York).Google Scholar
• Sparaggis PD, Towsley D, Cassandras C (1993) Extremal properties of the shortest/longest non-full queue policies in finite-capacity systems with state-dependent service rates. J. Appl. Probability 30(1):223–236.Google Scholar
• Stolyar AL (2015) Pull-based load distribution in large-scale heterogeneous service systems. Queueing Systems 80(4):341–361.Google Scholar
• Van der Boor M, Borst SC, Van Leeuwaarden JS, Mukherjee D (2022) Scalable load balancing in networked systems: A survey of recent advances. SIAM Rev. (SIAM, Philadelphia), 64(3):554–622.Google Scholar
• Vvedenskaya ND, Dobrushin RL, Karpelevich FI (1996) Queueing system with selection of the shortest of two queues: An asymptotic approach. Problemy Peredachi Informatsii 32(1):20–34.Google Scholar
• Winston W (1977) Optimality of the shortest line discipline. J. Appl. Probability 14(1):181–189.Google Scholar
• Xie Q, Dong X, Lu Y, Srikant R (2015) Power of d choices for large-scale bin packing: A loss model. Performance Evaluation Rev. 43(1):321–334.Google Scholar
• Zhou X, Tan J, Shroff N (2018) Heavy-traffic delay optimality in pull-based load balancing systems: Necessary and sufficient conditions. Proc. ACM on Measurement and Anal. of Comput. Systems (ACM, New York), 2(3):1–33.Google Scholar
• Zhou X, Wu F, Tan J, Sun Y, Shroff N (2017) Designing low-complexity heavy-traffic delay-optimal load balancing schemes: Theory to algorithms. Proc. ACM on Measurement and Anal. of Comput. Systems (ACM, New York), 1(2):1–30.Google Scholar