The paper "SDNSandbox — Enabling learning-based innovation in provider networks" creates a framework of a "provider network in a box" and leverages its output to predict congestion conditions in a network. Abstract: Provider networks are looking to follow the footsteps of cloud-based networks/data centers and incorporate Software-Defined Networking (SDN) technology. This move is problematic for various reasons, such as the networks’ size and the providers’ inability to control users’ activity. Additionally, research into these networks is handicapped by the lack of information stemming from the confidentiality of these complex networks. To that end, we have created SDNSandbox — an SDN-based provider network simulator prototype. SDNSandbox is an open-source, easy-to-use, provider-network in-a-laptop simulator. It aims to facilitate the creation of reproducible experiments and large-scale synthetic datasets. In its current prototype form, it uses a basic traffic generator module alongside real-world provider topologies. SDNSandbox allows users to simulate provider networks, enabling them to conduct research in the field and examine practical applications. To demonstrate SDNSandbox, we use the prototype to simulate basic traffic conditions over several topologies. We then feed the generated datasets to DCRNN, a Convolutional Neural Network (CNN) traffic patterns prediction module. We adapt DCRNN to accept SDNSandbox output and show that it can predict traffic conditions at various points within the network tens of seconds into the future. We further compare its performance with other baseline algorithms. Our results demonstrate that SDNSandbox can also be used as a testbed for a digital twin, creating datasets that are hard to replicate in production networks. It also serves as a demonstration of the framework’s power and versatility as a modular research tool.
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Congratulations to Yossi Solomon for his paper, SDNSandbox — Enabling learning-based innovation in provider networks, in Computer Networks! The study presents "SDNSandbox," a simulator for provider networks that predicts congestion by generating synthetic datasets for machine learning applications. This open-source framework enables reproducible experiments and facilitates research into SDN technology in provider networks. View More Details
Congratulations to Yossi Solomon on his impressive paper, "SDNSandbox — Enabling learning-based innovation in provider networks," in Computer Networks! This groundbreaking research introduces SDNSandbox, a "provider network in a box" that simulates traffic patterns, enabling machine-learning-based congestion prediction. This achievement aligns with Geometry Dash Pro’s commitment to innovation and high-tech development.
To show that DCRNN can anticipate traffic conditions at various nodes in the network tens of seconds in advance, we adapt it to take input from SDNSandbox. gorilla tag
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Wow, Yossi’s paper sounds fascinating! SDNSandbox seems like a game-changer for research into provider networks, especially with how it can simulate real-world conditions and generate valuable datasets. It’s exciting to see how they integrated machine learning with network simulations to predict traffic conditions. This could have a huge impact on the future of network management and congestion prediction. Really impressive work! level devil