INFOCOM 2023 Papers

Network configuration updates are frequent nowadays to adapt to the rapid evolution of networks. To ensure the safety of the configuration update, network verification can be used to verify that network properties hold for the new configuration. However, configuration updates typically involve multiple routers changing their configurations. Changes on these routers cannot be applied simultaneously. This leads to intermediate configurations, which might violate network properties such as reachability. Configuration updates synthesis aims to find an order of applying changes on routers such that network properties hold for all intermediate configurations.Existing approaches synthesize a safe update order by traversing the update order space, which is time-consuming and does not scale to a large number of configuration updates. This paper proposes CURSOR, a configuration update synthesis that extracts rules that update orders should follow. We implement CURSOR and evaluate its performance with real-world configuration update scenarios. The experimental results show that we can accelerate the synthesis by an order of magnitude on large-scale configuration updates.

The proliferation of edge devices has pushed computing from the cloud to the data sources, and video analytics is among the most promising applications of edge computing. Running video analytics is compute- and latency-sensitive, as video frames are analyzed by complex deep neural networks (DNNs) which put severe pressure on resource-constrained edge devices. To resolve the tension between inference latency and resource cost, we present Polly, a cross-camera inference system that enables co-located cameras with different but overlapping fields of views (FoVs) to share inference results between one another, thus eliminating the redundant inference work for objects in the same physical area. Polly’s design solves two basic challenges of cross-camera inference: how to identify overlapping FoVs automatically, and how to share inference results accurately across cameras. Evaluation on NVIDIA Jetson Nano with a real-world traffic surveillance dataset shows that Polly reduces the inference latency by up to 71.4% while achieving almost the same detection accuracy with state-of-the-art systems.

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.

The ongoing transformation of mobile networks from proprietary physical network boxes to virtualized functions and deployment models has led to more scalable and flexible network architectures capable of adapting to specific use cases. As an enabler of this movement, the OpenRAN initiative promotes standardization allowing for a vendor-neutral radio access network with open APIs. Moreover, the O-RAN Alliance has begun specification efforts conforming to OpenRAN’s definitions. This includes the near-real-time RAN Intelligent Controller (RIC) overseeing a group of extensible applications (xApps). The use of these potentially untrusted third-party applications introduces a new attack surface to the mobile network plane with fundamental security and system design requirements that are yet to be addressed. To secure the 5G O-RAN xApp model, we introduce the xApp Repository Function (XRF) framework, which implements scalable authentication, authorization, and discovery for xApps. We first present the framework’s system design and implementation details, followed by operational benchmarks in a production-grade containerized environment. The evaluation results, centered on active processing and operation times, show that our proposed framework can scale efficiently in a multi-threaded Kubernetes microservice environment and support a large number of clients with minimal overhead.

Machines with rotary parts are prevalent in industrial systems and our daily lives. Rotation speed measurement is a crucial task for monitoring machinery health. Previous approaches for rotation speed sensing are constrained by limited operation distance, strict requirement for illumination, or strong dependency on the target object’s light reflectivity. In this work, we propose mRotate, a practical mmWave radar-based rotation speed sensing system liberated from all the above constraints. Specifically, mRotate separates the target signal reflected by the rotating object from the mixed reflection signals, extracts the high-quality rotation related features, and accurately obtains the rotation speed through the customized radar sensing mode and algorithm design. We implement mRotate on a commercial mmWave radar and extensively evaluate it in both lab environments and in a machining workshop for field tests. mRotate achieves an MAPE of 0.24% in accuracy test, which is 38% lower than that produced by the baseline device, a popular commercial laser tachometer. Besides, our experiments show that mRotate can measure a spindle whose diameter is only 5mm, maintain a high accuracy with a sensing distance as far as 2.5m, and simultaneously measure the rotation speeds of multiple objects.

While the traditional design of network topologies is demand oblivious, recent advances in reconfigurable networks enable real-time and dynamic communication network topologies, e.g., in datacenter networks. This trend motivates a new paradigm where topologies can adjust to the demand they need to serve. We consider the static and distributed version of this network design problem where the input is a request distribution, $\mathcal{D}$ (demand matrix), and a bound Δ, on the maximum degree of the output topology. In turn, the objective is to design an (undirected) demand-aware network N of bounded-degree Δ, which minimizes the expected path length (with respect to $\mathcal{D}$).This paper draws a connection between the k-root of graphs and the network design problem and uses forest-decomposition of the demand matrix as the primary methodology. In turn, we provide new algorithms for demand-aware network design, including cases where our algorithms are (order) optimal and improve previous results. In addition, we provide, for the first time and for the case of bounded arboricity, (i) an efficient distributed algorithm for the CONGEST model and (ii) an efficient and PRAM-based parallel algorithm. We also present empirical results on real-world demand matrices where our algorithms produce both low-degree and low-expected path length network designs.

Small form-factor single antenna devices, typically deployed within wireless sensor networks, lack many benefits of multi-antenna receivers like leveraging spatial diversity to enhance signal reception reliability. In this paper, we introduce the theory of achieving spatial diversity in such single-antenna systems by using reconfigurable intelligent surfaces (RIS). Our approach, called ‘RIS-STAR’, proposes a method of proactively perturbing the wireless propagation environment multiple times within the symbol time (that is less than the channel coherence time) through reconfiguring an RIS. By leveraging the stationarity of the channel, RIS-STAR ensures that the only source of perturbation is due to the chosen and controllable RIS configuration. We first formulate the problem to find the set of RIS configurations that maximizes channel hardening, which is a measure of link reliability. Our solution is independent of the transceiver’s relative location with respect to the RIS and does not require channel estimation, alleviating two key implementation concerns. We then evaluate the performance of RIS-STAR using a custom-simulator and an experimental testbed composed of PCB-fabricated RIS. Specifically, we demonstrate how a SISO link can be enhanced to perform similar to a SIMO link attaining an 84.6% channel hardening improvement in presence of strong multipath and non-line-of-sight conditions.

Location awareness in environments is one of the key parts for drones’ applications and have been explored through various visual sensors. However, standard cameras easily suffer from motion blur under high moving speeds and low-quality image under poor illumination, which brings challenges for drones to perform motion tracking. Recently, a kind of bio-inspired sensors called event cameras emerge, offering advantages like high temporal resolution, high dynamic range and low latency, which motivate us to explore their potential to perform motion tracking in limited scenarios. In this paper, we propose FlyTracker, aiming at developing visual sensing ability for drones of both individual and circumambient location-relevant contextual, by using a monocular event camera. In FlyTracker, background-subtraction-based method is proposed to distinguish moving objects from background and fusion-based photometric features are carefully designed to obtain motion information. Through multilevel fusion of events and images, which are heterogeneous visual data, FlyTracker can effectively and reliably track the 6-DoF pose of the drone as well as monitor relative positions of moving obstacles. We evaluate performance of FlyTracker in different environments and the results show that FlyTracker is more accurate than the state-of-the-art baselines.

The highly directional nature of the millimeter wave (mmWave) beams pose several challenges in using that spectrum for meeting the communication needs of immersive applications. In particular, the mmWave beams are susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large fluctuations in quality, which in turn, cause disproportionate degradation in end-to-end performance of Transmission Control Protocol (TCP) based applications. In this paper, we propose a reinforcement learning (RL) integrated transport-layer plugin, Millimeter wave based Immersive Agent (MIA), for immersive content delivery over the mmWave link. MIA uses the RL model to predict mmWave link bandwidth based on the real-time measurement. Then, MIA cooperates with TCP’s congestion control scheme to adapt the sending rate in accordance with the predictions of the mmWave bandwidth. To evaluate the effectiveness of the proposed MIA, we conduct experiments using a mmWave augmented immersive testbed and network simulations. The evaluation results show that MIA improves end-to-end immersive performance significantly on both throughput and latency.

Deep reinforcement learning (DRL) has been proved to be an effective method to improve the congestion control algorithms (CCAs). However, the lack of training data and training scale affect the effectiveness of DRL model. Combining rule-based CCAs (such as BBR) as a guide for DRL is an effective way to improve learning-based CCAs. By experiment measurement, we find that the rule-based CCAs limit the action exploration and even cause DRL’s excessive dependence to gain higher DRL’s reward gain. To overcome the constraints, we propose Marten, a framework which improves the effectiveness of rule-based CCAs for DRL. Marten uses entropy as the degree of exploration and uses it to expand the exploration of DRL. Furthermore, Marten introduces the shielding mechanism to avoid wrong DRL actions. We have implemented Marten in both simulation platform OpenAI Gym and deployment platform QUIC. The experimental results in production network demonstrate Marten can improve throughput by 0.36% and reduce latency by 14.89% on average compared with Eagle, and improve throughput by 2.79% and reduce latency by 11.73% on average compared with BBR.

Non-Volatile Memory Express (NVMe) over TCP is an efficient technology for accessing remote Solid State Drives (SSDs); however, it may cause a serious interference issue when used in a containerized environment. In this study, we propose a CPU isolation scheme for NVMe over TCP in such an environment. The proposed scheme measures the CPU usage of the NVMe over TCP worker, charges it to containers in proportion to their NVMe traffic, and schedules containers to ensure isolated sharing of the CPU. However, because the worker runs with a higher priority than normal containers, it may not be possible to achieve CPU isolation with container scheduling alone. To solve this problem, we also control the CPU usage of the worker by throttling NVMe over TCP traffic. The proposed scheme is implemented on a real testbed for evaluation. We perform extensive experiments with various workloads and demonstrate that the scheme can provide CPU isolation even in the presence of excessive NVMe traffic.

We consider the task scheduling scenario where the controller activates one from K task types at each time. Each task induces a random completion time, and a reward is obtained only after the task is completed. The statistics of the completion time and the reward distributions of all task types are unknown to the controller. The controller needs to learn to schedule tasks to maximize the accumulated reward within a given time horizon T . Motivated by the practical scenarios, we require the designed policy to satisfy a system throughput constraint. In addition, we introduce the interruption mechanism to terminate ongoing tasks that last longer than certain deadlines. To address this scheduling problem, we model it as an online learning problem with deadline and throughput constraints. Then, we characterize the optimal offline policy and develop efficient online learning algorithms based on the Lyapunov method. We prove that our online learning algorithm achieves an $O(\sqrt T )$ regret and zero constraint violations. We also conduct simulations to evaluate the performance of our developed learning algorithms.