Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packet delay values in response to the time-varying load offered by a sender, which is typically a function of the previously output delays. The problem setting is unique, and renders the state-of-the-art text and time-series generative models inapplicable or ineffective. We formulate an ML problem at the intersection of dynamical systems, sequential decision making, and time-series modeling. We propose a novel grey-box approach to network simulation that embeds the semantics of physical network path in a new RNN-style model called Recurrent Buffering Unit, providing the interpretability of standard network simulator tools, the power of neural models, the efficiency of SGD-based techniques for learning, and yielding promising results on synthetic and real-world network traces.

Interactive networked applications require high throughput, low latency, and high reliability from the network to provide a seamless user experience. While meeting these three requirements simultaneously is difficult, there has been an emergence of heterogeneous virtual channels (HVCs) which support some subset of them at the expense of the others. For instance, URLLC sacrifices throughput to achieve low latency and reliability in 5G NR, and Wi-Fi 7 and other novel Internet architectures provide similar disparate types of service. Prior work either focuses on aggregating the bandwidth of these channels whilst neglecting their unique properties or fails to generalize in the sense of achieving high performance across different applications and channels. To utilize HVCs to their fullest, we argue that there are challenges and opportunities across the network, transport and application layers, and the application-transport interface of the network stack. In this work, we explore the trade-offs of these architectural choices in the context of web browsing and real-time video, and identify the constituting principles of a design that is general, performant, and deployable.

Microfluidic technologies enable replacement of time-consuming and complex steps of biochemical laboratory protocols with a tiny chip. Sample preparation (i.e., dilution or mixing of fluids) is one of the primary tasks of any bioprotocol. In real-life applications where several assays need to be executed for different diagnostic purposes, the same sample fluid is often required with different target concentration factors ( CF s). Although several multi-target dilution algorithms have been developed for digital microfluidic biochips, they are not efficient for implementation with continuous-flow-based microfluidic chips, which are preferred in the laboratories. In this article, we present a multi-target dilution algorithm ( MTDA ) for continuous-flow-based microfluidic biochips, which to the best of our knowledge is the first of its kind. We design a flow-based rotary mixer with a suitable number of segments depending on the target- CF profile, error tolerance, and optimization criteria. To schedule several intermediate fluid-mixing tasks, we develop a multi-target scheduling algorithm ( MTSA ) aiming to minimize the usage of storage units while producing dilutions with multiple CF s. Furthermore, we propose a storage architecture for efficiently loading (storing) of intermediate fluids from (to) the storage units.