Network Functions (NFs) now touch a significant fraction of Internet traffic. The hope has been that software-based NF Virtualization (NFV) would enable rapid development of new NFs by vendors and leverage the power and economics of commodity computing infrastructure for NF deployment. To date, no cloud NFV systems achieve NF chaining, isolation, SLO-adherence, and scaling together with existing cloud computing infrastructure and abstractions, all while achieving generality, speed, and ease of deployment; these properties are taken for granted in other cloud contexts but unavailable for NF processing. We present Quadrant, an efficient and secure cloud-deployable NFV system, and show that Quadrant’s approach of adapting existing cloud infrastructure to support packet processing can achieve NF chaining, isolation, generality, and performance in NFV. Quadrant reuses common cloud infrastructure such as Kubernetes, cloud functions, the Linux kernel, NIC hardware, and switches. It enables easy NFV deployment while delivering up to double the performance per core compared to the state of the art.

Oblivious routing distributes traffic from sources to destinations following predefined routes with rules independent of traffic demands. While finding optimal oblivious routing is intractable for general topologies, we show that it is tractable for structured topologies often used in datacenter networks. To achieve this, we apply graph automorphism and prove the existence of the optimal automorphism-invariant solution. This result reduces the search space to targeting the optimal automorphism-invariant solution. We design an iterative algorithm to obtain such a solution by alternating between two linear programs. The first program finds an automorphism-invariant solution based on representative variables and constraints, making the problem tractable. The second program generates adversarial demands to ensure the final result satisfies all possible demands. Since, the construction of the representative variables and constraints are combinatorial problems, we design polynomial-time algorithms for the construction. We evaluate proposed iterative algorithm in terms of throughput performance, scalability, and generality over three potential applications. The algorithm i) improves the throughput up to 87.5% over a heuristic algorithm for partially deployed FatTree, ii) scales for FatClique with a thousand switches, iii) is applicable to a general structured topology with non-uniform link capacity and server distribution.

Autonomous vehicles use 3D sensors for perception. Cooperative perception enables vehicles to share sensor readings with each other to improve safety. Prior work in cooperative perception scales poorly even with infrastructure support. AUTOCAST1 enables scalable infrastructure-less cooperative perception using direct vehicle-to-vehicle communication. It carefully determines which objects to share based on positional relationships between traffic participants, and the time evolution of their trajectories. It coordinates vehicles and optimally schedules transmissions in a distributed fashion. Extensive evaluation results under different scenarios show that, unlike competing approaches, AUTOCAST can avoid crashes and near-misses which occur frequently without cooperative perception, its performance scales gracefully in dense traffic scenarios providing 2-4x visibility into safety critical objects compared to existing cooperative perception schemes, its transmission schedules can be completed on the real radio testbed, and its scheduling algorithm is near-optimal with negligible computation overhead.

In their quest to provide customers with good tools to manage cloud services, cloud providers are hampered by having very little visibility into cloud service functionality; a provider often only knows where VMs of a service are placed, how the virtual networks are configured, how VMs are provisioned, and how VMs communicate with each other. In this paper, we show that, using the VM-to-VM traffic matrix, we can unearth the functional structure of a cloud service and use it to aid cloud service management. Leveraging the observation that cloud services use well-known design patterns for scaling (e.g., replication, communication locality), we show that clustering the VM-to-VM traffic matrix yields the functional structure of the cloud service. Our clustering algorithm, CloudCluster, must overcome challenges imposed by scale (cloud services contain tens of thousands of VMs) and must be robust to orders-of-magnitude variability in traffic volume and measurement noise. To do this, CloudCluster uses a novel combination of feature scaling, dimensionality reduction, and hierarchical clustering to achieve clustering with over 92% homogeneity and completeness. We show that CloudCluster can be used to explore opportunities to reduce cost for customers, identify anomalous traffic and potential misconfigurations.

Advances in deep learning (DL) have prompted the development of cloud-hosted DL-based media applications that process video and audio streams in real-time. Such applications must satisfy throughput and latency objectives and adapt to novel types of dynamics, while incurring minimal cost. Scrooge, a system that provides media applications as a service, achieves these objectives by packing computations efficiently into GPU-equipped cloud VMs, using an optimization formulation to find the lowest cost VM allocations that meet the performance objectives, and rapidly reacting to variations in input complexity (e.g., changes in participants in a video). Experiments show that Scrooge can save serving cost by 16-32% (which translate to tens of thousands of dollars per year) relative to the state-of-the-art while achieving latency objectives for over 98% under dynamic workloads.