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1. Stochastic Traffic Regulator for End-to-End Network Delay Guarantees

   https://doi.org/10.1109/ICC40277.2020.9149446  

   Boroujeny, Massieh Kordi; Mark, Brian L.; Ephraim, Yariv (June 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC))

   Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Due to the looseness of the deterministic bounds, various frameworks for stochastic network calculus have been proposed to provide tighter, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We propose and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Numerical results are provided to demonstrate the performance of the proposed traffic regulator. 

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2. Design of a Stochastic Traffic Regulator for End-to-End Network Delay Guarantees

   https://doi.org/10.1109/TNET.2022.3181858  

   Boroujeny, Massieh Kordi; Mark, Brian L. (June 2022, IEEE/ACM Transactions on Networking)

   Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Since deterministic bounds are based on the worst-case traffic behavior, various frameworks for stochastic network calculus have been proposed to provide less conservative, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We design and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Two alternative implementations of the stochastic regulator are developed and compared. Numerical results are provided to demonstrate the performance of the proposed stochastic traffic regulator. 

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3. On the Benefits of Traffic “Reprofiling” the Multiple Hops Case—Part I

   https://doi.org/10.1109/TNET.2024.3392030  

   Qiu, Jiaming; Song, Jiayi; Guérin, Roch; Sariowan, Henry (August 2024, IEEE/ACM Transactions on Networking)

   This paper considers networks where user traffic is regulated through deterministic traffic profiles, e.g., token buckets, and requires hard delay bounds. The network's goal is to minimize the resources it needs to meet those bounds. The paper explores how reprofiling, i.e., proactively modifying how user traffic enters the network, can be of benefit. Reprofiling produces "smoother" flows but introduces an up-front access delay that forces tighter network delays. The paper explores this trade-off and demonstrates that, unlike what holds in the single-hop case, reprofiling can be of benefit even when "optimal" schedulers are available at each hop. 

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4. M. Kordi Boroujeny and B.L. Mark, "A Framework for Providing Stochastic Delay Guarantees in Communication Networks," IEEE ICC 2021 Workshop on Time-sensitive and Deterministic Networking, Montreal, Canada, June 2021

   Kordi Boroujeny, Massieh; Mark, Brian L. (June 2021, IEEE ICC 2021 Workshop on Time-sensitive and Deterministic Networking)

   null (Ed.)

   The provisioning of delay guarantees in packet-switched networks such as the Internet remains an important, yet challenging open problem. We propose and evaluate a framework, based on results from stochastic network calculus, for guaranteeing stochastic bounds on network delay at a statistical multiplexer. The framework consists of phase-type traffic bounds and moment generating function traffic envelopes, stochastic traffic regulators to enforce the traffic bounds, and an admission control scheme to ensure that a stochastic delay bound is maintained for a given set of flows. Through numerical examples, we show that a stochastic delay bound is maintained at the multiplexer, and contrast the proposed framework to an approach based on deterministic network calculus. 

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5. Deep Reinforcement Learning for Delay-Sensitive LTE Downlink Scheduling

   https://doi.org/10.1109/PIMRC48278.2020.9217110  

   Sharma, Nikhilesh; Zhang, Sen; Somayajula Venkata, Someshwar Rao; Malandra, Filippo; Mastronarde, Nicholas; Chakareski, Jacob (August 2020, 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications)

   null (Ed.)

   We consider an LTE downlink scheduling system where a base station allocates resource blocks (RBs) to users running delay-sensitive applications. We aim to find a scheduling policy that minimizes the queuing delay experienced by the users. We formulate this problem as a Markov Decision Process (MDP) that integrates the channel quality indicator (CQI) of each user in each RB, and queue status of each user. To solve this complex problem involving high dimensional state and action spaces, we propose a Deep Reinforcement Learning based scheduling framework that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm to minimize the queuing delay experienced by the users. Our extensive experiments demonstrate that our approach outperforms state-of-the-art benchmarks in terms of average throughput, queuing delay, and fairness, achieving up to 55% lower queuing delay than the best benchmark. 

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