arXiv subjects

Mowei Wang

Publications and source records attributed to Mowei Wang.

5 recordsLinked to original sources

arXiv2024

NegotiaToR: Towards A Simple Yet Effective On-demand Reconfigurable Datacenter Network

Recent advances in fast optical switching technology show promise in meeting the high goodput and low latency requirements of datacenter networks (DCN). We present NegotiaToR, a simple network architecture for optical reconfigurable DCNs that utilizes on-demand scheduling to handle dynamic traffic. In NegotiaToR, racks exchange scheduling messages through an in-band control plane and distributedly calculate non-conflicting paths from binary traffic demand information. Optimized for incasts, it also provides opportunities to bypass scheduling delays. NegotiaToR is compatible with prevalent flat topologies, and is tailored towards a minimalist design for on-demand reconfigurable DCNs, enhancing practicality. Through large-scale simulations, we show that NegotiaToR achieves both small mice flow completion time (FCT) and high goodput on two representative flat topologies, especially under heavy loads. Particularly, the FCT of mice flows is one to two orders of magnitude better than the state-of-the-art traffic-oblivious reconfigurable DCN design.

cs.NI↗

arXiv2022

Digital Twin for Networking: A Data-driven Performance Modeling Perspective

Emerging technologies and applications make the network unprecedentedly complex and heterogeneous, leading physical network practices to be costly and risky. The digital twin network (DTN) can ease these burdens by virtually enabling users to understand how performance changes accordingly with modifications. For this "What-if" performance evaluation, conventional simulation and analytical approaches are inefficient, inaccurate, and inflexible, and we argue that data-driven methods are most promising. In this article, we identify three requirements (fidelity, efficiency, and flexibility) for performance evaluation. Then we present a comparison of selected data-driven methods and investigate their potential trends in data, models, and applications. Although extensive applications have been enabled, there are still significant conflicts between models' capacities to handle diversified inputs and limited data collected from the production network. We further illustrate the opportunities for data collection, model construction, and application prospects. This survey aims to provide a reference for performance evaluation while also facilitating future DTN research.

cs.NI↗

arXiv2021

Zeropod: Simplifying Datacenter Networking with Future-Proof Zero-Buffer Packet Switches

With the rapid growth of traffic volume in datacenter networks (DCNs), packet switches suffer from insufficient switching chip capacity and difficulties in transmission control, making it challenging to provide high goodput and low latency for emerging cloud applications. We present Zeropod, a future-proof DCN architecture featuring simplified zero-buffer packet switches inside the point-of-delivery (pod). Within each pod, traffic transmission is scheduled by a per-pod centralized scheduler for collision avoidance, enabling a highly simplified data plane, facilitating benefits like higher switching capacity and precise transmission control. Among the pods, buffered Core switches work as barriers and relay inter-pod data, limiting the scope of centralized scheduling and thus simplifying the control plane. Zeropod combines host-level and flow-level scheduling for high performance with low overhead. Evaluation results show that Zeropod consistently performs better or equivalent to traditional buffered DCN, particularly regarding flow completion time (FCT). When accounting for the increased switching capacity due to the removal of buffers, its performance is further improved. Zeropod explores an extreme end of the design spectrum, and we hope it can encourage further exploration in the DCN community.

cs.NI↗

arXiv2021

DeepCC: Bridging the Gap Between Congestion Control and Applications via Multi-Objective Optimization

The increasingly complicated and diverse applications have distinct network performance demands, e.g., some desire high throughput while others require low latency. Traditional congestion controls (CC) have no perception of these demands. Consequently, literatures have explored the objective-specific algorithms, which are based on either offline training or online learning, to adapt to certain application demands. However, once generated, such algorithms are tailored to a specific performance objective function. Newly emerged performance demands in a changeable network environment require either expensive retraining (in the case of offline training), or manually redesigning a new objective function (in the case of online learning). To address this problem, we propose a novel architecture, DeepCC. It generates a CC agent that is generically applicable to a wide range of application requirements and network conditions. The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a specific objective function, DeepCC trains its deep neural network model using multi-objective optimization. With the trained model, DeepCC offers near Pareto optimal policies w.r.t different user-specified trade-offs between throughput, delay, and loss rate without any redesigning or retraining. In addition, a quick online fine-tuning phase further helps DeepCC achieve the application-specific demands under dynamic network conditions. The simulation and real-world experiments show that DeepCC outperforms state-of-the-art schemes in a wide range of settings. DeepCC gains a higher target completion ratio of application requirements up to 67.4% than that of other schemes, even in an untrained environment.

cs.NI↗

arXiv2017

Machine Learning for Networking: Workflow, Advances and Opportunities

Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities on networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help and motivate researchers to develop innovative algorithms, standards and frameworks.

cs.NI↗