Network protocols often suffer from undetected performance degradations due to inadequate testing and a lack of efficient and accurate evaluation tools across diverse network configurations. Current methodologies face challenges like pre-modeling, oversimplifications, and focusing on limited failure modes, making them costly, impractical, or imprecise.We propose Mahak, a novel black-box framework that reconstructs the empirical performance of a given protocol throughout the multidimensional configuration space using an active learning-guided sampling strategy, without prior modeling or knowledge of the internal algorithm of the protocol. Applied to state-of-the-art Internet Congestion Control (e.g., BBR2, Sage, Orca) and Adaptive Bitrate Streaming protocols (e.g. Pensieve, BOLA, RobustMPC), Mahak explores less than 0.1% of the configuration space, and achieves up to a 12.5× reduction in mapping error compared to interpolation-based methods.By systematically identifying the complete empirical performance surface, rather than focusing on a single prominent failure, Mahak uncovers issues that would otherwise remain hidden. This end-to-end mapping equips protocol designers, QA engineers, and network operators with actionable data-driven insights across diverse metrics and configurations, supporting more reliable deployments.

In this paper, our primary objective is to showcase that the application of machine learning techniques extends beyond network protocol design. We aim to demonstrate that performance assessment of network protocols, a vital aspect of improving network infrastructures and developing better protocol designs, can be modernized through the utilization of machine learning. As a step towards this goal, we have designed and introduced Mahak, the first tool that harnesses active learning techniques to automate the performance assessment of congestion control schemes. Mahak actively learns to optimize the evaluation process of congestion control schemes so that they can generate their performance maps over a desired space without exhaustively testing them in every scenario. Mahak treats schemes under the test as black boxes. This protocol-agnostic aspect of Mahak enables users to directly assess the performance of the actual implementation of a protocol instead of their over-simplified mathematical models or simplified simulated versions.

Traffic processing on cloud-scale bandwidths has given rise to a new type of network structure, comprising a large number of highly-structured virtual entities working in close harmony. This structure, which we call a virtual sub-network, might be in need of migration, for reasons of load-balancing, maintenance, and disaster prevention. In this paper, we argue that the common migration schemes are not adequate for the complexity of this task. Therefore, we present Qanat, a migration system specifically optimized for the live migration of a virtual sub-network in its entirety to a different physical location. We show how Qanat employs widely-used techniques, such as traffic prioritization, buffering, and network tunnels, to overcome the main issues of live migration. In the paper, we categorize the main challenges of the migration task, provide an analytical study of Qanat’s algorithms, and measure its performance metrics through large-scale simulations. We conclude that Qanat can efficiently and transparently migrate virtual sub-networks and can provide a useful tool for system administrators.