Many networked systems rely on hashing and randomized algorithms for tasks such as load balancing, thereby avoiding the need for coordination or communication among participants on each request. However, purely random routing can lead to collisions and missed opportunities for beneficial colocation. Quantum entanglement enables participants to instantly make correlated decisions without communicating. We explore how this capability can expand the Pareto frontier of achievable performance in networked systems, presenting both positive and negative results. Notably, many of these advantages can be realized using small, currently available quantum devices that can often operate at room temperature.

Architecting a modern data center network is increasingly complicated. Seeking the highest performance and support for emerging workloads, network architects planning a buildout must choose from a large selection of switching components, NICs, network stacks, congestion control algorithms, routing schemes, measurement systems, virtualization software, centralized bandwidth allocators and security mechanisms, all from various vendors. Today, manual planning by human experts is time-consuming at best, and can easily result in overlooked design choices or missed complex inter-dependencies.

Heuristics are ubiquitous in computer systems. Examples include congestion control, adaptive bit rate streaming, scheduling, load balancing, and caching. In some domains, theoretical proofs have provided clarity on the conditions where a heuristic is guaranteed to work well. This has not been possible in all domains because proving such guarantees can involve combinatorial reasoning making it hard, cumbersome and error-prone. In this paper we argue that computers should help humans with the combinatorial part of reasoning. We model reasoning questions as ∃∀ formulas [1] and solve them using the counterexample guided inductive synthesis (CEGIS) framework. As preliminary evidence, we prototype CCmatic, a tool that semi-automatically synthesizes congestion control algorithms that are provably robust. It rediscovered a recent congestion control algorithm that provably achieves high utilization and bounded delay under a challenging network model. It also found previously unknown variants of the algorithm that achieve different throughput-delay trade-offs.

Modern distributed applications run across numerous microservices and components deployed in cloud datacenters, using shared cloud services for computing and storage, edge services such as content distribution networks, network functions such as rate limiters and firewalls, security infrastructures, network routers, and physical links. When a user-visible fault occurs, the first step toward diagnosis is localization to determine where the fault has occurred. However, because application delivery spans different layers and different organizations, no entity has complete visibility or access to the information required to localize faults quickly. This paper proposes a cross-layer, cross-domain, and cross-application fault localization primitive with a simple and standardized information interface for the Internet.

To overcome weaknesses in traditional loss-based congestion control algorithms (CCAs), researchers have developed and deployed several delay-bounding CCAs that achieve high utilization without bloating delays (e.g., Vegas, FAST, BBR, PCC, Copa, etc.). When run on a path with a fixed bottleneck rate, these CCAs converge to a small delay range in equilibrium. This paper proves a surprising result: although designed to achieve reasonable inter-flow fairness, current methods to develop delay-bounding CCAs cannot always avoid starvation, an extreme form of unfairness. Starvation may occur when such a CCA runs on paths where non-congestive network delay variations due to real-world factors such as ACK aggregation and end-host scheduling exceed double the delay range that the CCA converges to in equilibrium. We provide experimental evidence for this result for BBR, PCC Vivace, and Copa with a link emulator. We discuss the implications of this result and posit that to guarantee no starvation an efficient delay-bounding CCA should design for a certain amount of non-congestive jitter and ensure that its equilibrium delay oscillations are at least one-half of this jitter.

This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness.

The diversity of paths on the Internet makes it difficult for designers and operators to confidently deploy new congestion control algorithms (CCAs) without extensive real-world experiments, but such capabilities are not available to most of the networking community. And even when they are available, understanding why a CCA underperforms by trawling through massive amounts of statistical data from network connections is challenging. The history of congestion control is replete with many examples of surprising and unanticipated behaviors unseen in simulation but observed on real-world paths. In this paper, we propose initial steps toward modeling and improving our confidence in a CCA's behavior. We have developed CCAC, a tool that uses formal verification to establish certain properties of CCAs. It is able to prove hypotheses about CCAs or generate counterexamples for invalid hypotheses. With CCAC, a designer can not only gain greater confidence prior to deployment to avoid unpleasant surprises, but can also use the counterexamples to iteratively improvetheir algorithm. We have modeled additive-increase/multiplicative-decrease (AIMD), Copa, and BBR with CCAC, and describe some surprising results from the exercise.

Network performance monitoring today is restricted by existing switch support for measurement, forcing operators to rely heavily on endpoints with poor visibility into the network core. Switch vendors have added progressively more monitoring features to switches, but the current trajectory of adding specific features is unsustainable given the ever-changing demands of network operators. Instead, we ask what switch hardware primitives are required to support an expressive language of network performance questions. We believe that the resulting switch hardware design could address a wide variety of current and future performance monitoring needs.