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1. Proactive Scheduling for mmWave Wireless LANs

   https://doi.org/10.1016/j.comcom.2024.107979  

   Deng, Ang; Blough, Douglas M (December 2024, Computer Communications)

   To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential. 

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2. Exploring Performance Limits on Proactive Fair Scheduling for mmWave WLANs

   https://doi.org/10.1109/LANMAN54755.2022.9820446  

   Deng, Ang; Liu, Yuchen; Blough, Douglas M. (January 2022, Proceedings of the IEEE International Symposium on Local and Metropolitan Area Networks)

   Although the millimeter wave (mmWave) band has great potential to address ever-increasing demands for wireless bandwidth, its intrinsically unique propagation characteristics call for different scheduling strategies in order to minimize performance drops caused by blockages. A promising approach to mitigate the blockage problem is proactive scheduling, which uses blockage predictions to schedule users when they are experiencing good channel conditions. In this paper, we formulate an optimal scheduling problem with fairness constraints that allows us to find a schedule with maximum aggregate rate that achieves approximately the same fairness as the classic proportional fair scheduler. The results show that, for the problem settings studied, up to around 30% increase in aggregate rate compared to classic proportional fair scheduling (PFS) is possible with no decrease in fairness when blockages can be accurately predicted 0.5 seconds in advance. Furthermore, aggregate rate could be doubled compared to PFS if blockages can be accurately predicted 5 seconds in advance. While these results demonstrate the very promising potential of proactive scheduling, we also discuss several future research directions that must be pursued to effectively realize the approach. 

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3. Enoki: High Velocity Linux Kernel Scheduler Development

   https://doi.org/10.1145/3627703.3629569  

   Miller, Samantha; Kumar, Anirudh; Vakharia, Tanay; Chen, Ang; Zhuo, Danyang; Anderson, Thomas (April 2024, ACM)

   Kernel task scheduling is important for application performance, adaptability to new hardware, and complex user requirements. However, developing, testing, and debugging new scheduling algorithms in Linux, the most widely used cloud operating system, is slow and difficult. We developed Enoki, a framework for high velocity development of Linux kernel schedulers. Enoki schedulers are written in safe Rust, and the system supports live upgrade of new scheduling policies into the kernel, userspace debugging, and bidirectional communication with applications. A scheduler implemented with Enoki achieved near identical performance (within 1% on average) to the default Linux scheduler CFS on a wide range of benchmarks. Enoki is also able to support a range of research schedulers, specifically the Shinjuku scheduler, a locality aware scheduler, and the Arachne core arbiter, with good performance. 

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4. Scalable verification of GNN-based job schedulers

   https://doi.org/10.1145/3563325  

   Wu, Haoze; Barrett, Clark; Sharif, Mahmood; Narodytska, Nina; Singh, Gagandeep (October 2022, Proceedings of the ACM on Programming Languages)

   Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics. Despite their impressive performance, concerns remain over whether these GNN-based job schedulers meet users’ expectations about other important properties, such as strategy-proofness, sharing incentive, and stability. In this work, we consider formal verification of GNN-based job schedulers. We address several domain-specific challenges such as networks that are deeper and specifications that are richer than those encountered when verifying image and NLP classifiers. We develop vegas, the first general framework for verifying both single-step and multi-step properties of these schedulers based on carefully designed algorithms that combine abstractions, refinements, solvers, and proof transfer. Our experimental results show that vegas achieves significant speed-up when verifying important properties of a state-of-the-art GNN-based scheduler compared to previous methods. 

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5. End-to-end Simulation of mmWave Out-of-band Backhaul Networks in ns-3

   https://doi.org/https://doi.org/10.1145/3337941.3337943  

   Hu, Qiang; Liu, Yuchen; Yan, Yan; Blough, Douglas M. (January 2019, Proceedings of the 2019 Workshop on Next-Generation Wireless with ns-3)

   In this paper, we focus on the end-to-end simulation of millimeter-wave (mmWave) out-of-band backhaul networks in ns-3. We first introduce a design for an out-of-band backhaul module in ns-3. A custom backhaul network device is proposed that can be added to nodes to enable out-of-band backhaul transmissions. Since the new backhaul module is still under development, we extended the existing mmWave integrated-access-and-backhaul (IAB) module to enable mmWave out-of-band backhaul simulation and produce some preliminary results on throughput performance. We also modified the existing scheduler in the IAB module to support customizable scheduling for logical links in out-of-band backhaul networks. We used the customizable scheduler to implement an optimal scheduling algorithm from prior work and demonstrate that it increases throughput performance by up to 40% in certain scenarios. 

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