INFOCOM 2024 论文列表

Encrypted tunnels are increasingly applied to privacy protection, however, a passive eavesdropper can still infer which website a user is visiting via website fingerprinting (WF). State-of-the-art WF suffers from several critical challenges in a realistic multi-tab web browsing scenario, where the number of concurrent tabs is dynamic and uncertain, training a separate model for each website is too overweight to deploy, and the robustness against the packet loss, duplication and disorder caused by dynamic network conditions is rarely considered. To address these challenges, we propose a robust and lightweight multi-tab WF method over the encrypted tunnel, named RobustWF. Due to the causality relationship between user’s request and website’s response, RobustWF employs causality correlation to associate the interactive packets belonging to the same website together, which form a causality chain. Then, RobustWF utilizes context learning to capture the dependencies between the causality chains. The missing of some specific details does not have a significant impact on the overall structure of target web, thus enhancing the robustness of RobustWF. To make the model lightweight enough, RobustWF trains an integrated model to adapt to the dynamic number of concurrent tabs. The experimental results demonstrate that the accuracy of RobustWF improves 14% in dynamic multi-tab WF scenario compared to the State-of-the-art method.

The emergence of 5G technology and edge computing enables the collaborative use of data by mobile users for scalable training of machine learning models. Privacy concerns and communication constraints, however, can prohibit users from offloading their data to a single server for training. Split learning, in which models are split between end users and a central server, somewhat resolves these concerns but requires exchanging information between users and the server in each local training iteration. Thus, splitting models between end users and geographically close edge servers can significantly reduce communication latency and training time. In this setting, users must decide to which edge servers they should offload part of their model to minimize the training latency, a decision that is further complicated by the presence of multiple, mobile users competing for resources. We present Edge-MSL, a novel formulation of the mobile split learning problem as a contextual multi-armed bandits framework. To counter scalability challenges with a centralized Edge-MSL solution, we introduce a distributed solution that minimizes competition between users for edge resources, reducing regret by at least two times compared to a greedy baseline. The distributed Edge-MSL approach improves trained model convergence with a 15% increase in test accuracy.

Cloud networks continuously upgrade softwarized virtual network devices (VNDs) to meet evolving tenant demands. However, such upgrades may result in unexpected failures. An intuitive idea to prevent upgrade failures is to resolve all compatibility issues before deployment, but it is impractical to replicate all deployed VND cases and test them with lots of replayed real traffic for the VND developers. As a result, the operations team takes upgrade risk to test upgrades by gradually deploying them. Although careful upgrade schedule planning is the most common method to minimize upgrade risk, to the best of our knowledge, no VND upgrade schedule planning scheme has been adequately studied for large-scale cloud networks. To fill this gap, we propose CloudPlanner, the first VND upgrade schedule planning scheme aiming to minimize the VND upgrade risk for large-scale cloud networks. CloudPlanner prioritizes upgrading VNDs that are more likely to trigger failures based on expert knowledge and historical failure-trigger VND properties and limits the number of tenants associated with simultaneously upgraded VNDs. We also propose a heuristic solver which can quickly and greedily plan schedules. Using real-world data from production environments, we demonstrate the benefits of CloudPlanner through extensive experiments.

The normal operation of data center network management tasks relies on accurate measurement of the network status. In-band Network Telemetry (INT) leverages programmable data planes to provide fine-grained and accurate network status. However, existing INT-related works have not considered the telemetry data required for dynamic adjustments of INT under uninterrupted conditions, including additions, deletions, and modifications. To address this issue, this paper proposes OpenINT, a lightweight and flexible In-band Network Telemetry system. The key innovation of OpenINT lies in decoupling telemetry operations in the data plane, using three generic sub-modules to achieve lightweight telemetry. Meanwhile, the control plane utilizes heuristic algorithms for dynamic planning to achieve near-optimal telemetry paths. Additionally, OpenINT provides primitives for defining network measurement tasks, which abstract the underlying telemetry architecture’s details, enabling network operator to conveniently access network status. A prototype of OpenINT is implemented on a programmable switch equipped with the Tofino chip. Experimental results demonstrate that OpenINT achieves highly flexible dynamic telemetry and significantly reduces network overhead.

Video stream analytics (VSA) systems fuel many exciting applications that facilitate people’s lives, but also raise critical concerns about exposing too much individuals’ privacy. To alleviate these concerns, various frameworks have been presented to enhance the privacy of VSA systems. Yet, existing solutions suffer two limitations: (1) being scenario-customized, thus limiting the generality of adapting to multifarious scenarios, (2) requiring complex, imperative programming, and tedious process, thus largely reducing the usability of such systems. In this paper, we present X-Stream, a privacy-preserving video transformer that achieves flexibility and efficiency for a large variety of VSA tasks. X-Stream features three major novel designs: (1) a declarative query interface that provides a simple yet expressive interface for users to describe both their privacy protection and content exposure requirements, (2) an adaptation mechanism that dynamically selects the most suitable privacy-preserving techniques and their parameters based on the current video context, and (3) an efficient execution engine that incorporates optimizations for multi-task deduplication and inter-frame inference. We implement X-Stream and evaluate it with representative VSA tasks and public video datasets. The results show that X-Stream achieves significantly improved privacy protection quality and performance over the state-of-the-art, while being simple to use.

In-network computing (INC) is a new paradigm that allows applications to be executed within the network, rather than on dedicated servers. Conventionally, INC applications have been exclusively deployed on the data plane (e.g., programmable ASICs), offering impressive performance capabilities. However, the data plane’s efficiency is hindered by limited resources, which can prevent a comprehensive deployment of applications. On the other hand, offloading compute tasks to the control plane, which is underpinned by general-purpose servers with ample resources, provides greater flexibility. However, this approach comes with the tradeoff of significantly reduced efficiency, especially when the system operates under heavy load. To simultaneously exploit the efficiency of data plane and the flexibility of control plane, we propose Carlo, a cross-plane collaborative optimization framework to support the network-wide deployment of multiple INC applications across both the control and data plane. Carlo first analyzes resource requirements of various INC applications across different planes. It then establishes mathematical models for resource allocation in cross-plane and automatically generates solutions using proposed algorithms. We have implemented the prototype of Carlo on Intel Tofino ASIC switches and DPDK. Experimental results demonstrate that Carlo can compute solutions in a short time while avoiding performance degradation caused by the deployment scheme.

In this study, the modulation of symbols on OFDM subcarriers is classified for transmissions following Wi-Fi 6 and 5G downlink specifications. First, our approach estimates the OFDM symbol duration and cyclic prefix length based on the cyclic autocorrelation function. We propose a feature extraction algorithm characterizing the modulation of OFDM signals, which includes removing the effects of a synchronization error. The obtained feature is converted into a 2D histogram of phase and amplitude and this histogram is taken as input to a convolutional neural network (CNN)-based classifier. The classifier does not require prior knowledge of protocol-specific information such as Wi-Fi preamble or resource allocation of 5G physical channels. The classifier’s performance, evaluated using synthetic and real-world measured over-the-air (OTA) datasets, achieves a minimum accuracy of 97% accuracy with OTA data when SNR is above the value required for data transmission.

Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. As this paradigm continues to spread, wireless systems must also evolve to identify active transmitters and unauthorized waveforms in real time under intentional distortion of preambles, extremely low signal-to-noise ratios and challenging channel conditions. We overcome limitations of correlation-based preamble matching methods in such conditions through the design of T-PRIME: a Transformer-based machine learning approach. T-PRIME learns the structural design of transmitted frames through its attention mechanism, looking at sequence patterns that go beyond the preamble alone. The paper makes three contributions: First, it compares Transformer models and demonstrates their superiority over traditional methods and state-of-the-art neural networks. Second, it rigorously analyzes T-PRIME’s real-time feasibility on DeepWave’s AIR-T platform. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Results reveal nearly perfect (i.e. > 98%) classification accuracy under simulated scenarios, showing 100% detection improvement over legacy methods in low SNR ranges, 97% classification accuracy for OTA single-protocol transmissions and up to 75% double-protocol classification accuracy in interference scenarios.

Although the native RDMA interface allows for high throughput and low latency, its low-level abstraction raises significant programming challenges. Consequently, numerous systems encapsulate the RDMA interface into more user-friendly high-level abstractions such as Socket, MPI, and RPC. However, this ease of development often incurs considerable performance degradation. To address this trade-off, this paper introduces RB2, a high-performance RDMA-based Distributed Ring Buffer (DRB). RB2serves as a middle layer that effectively conceals the low-level details of the RDMA interface while also facilitating extension to other high-level abstractions.Nonetheless, it is non-trivial for DRBs to preserve the RDMA performance. We optimize the performance of RB2in three aspects. First, we perform micro-benchmarks to identify the pointer synchronization methods that are seemingly counter-intuitive but offer optimal performance improvements. Second, we propose an adaptive batching mechanism to alleviate the limitations of conventional fixed batching. Finally, we build an efficient memory subsystem using various optimization techniques. RB2outperforms SOTA designs by achieving 2.5 × to 7.5 × throughput while maintaining comparable tail latency for small messages.

Modern recommendation systems operate entirely on the basis of central servers, requiring users to upload their behavior data from mobile devices to these servers. This practice has raised concerns about data privacy among users. Federated learning (FL), a machine learning technique designed to protect privacy, is becoming the standard solution. By combining federated learning with recommendation systems, Federated Recommendation Systems (FRS) allow users to collaboratively train a shared recommendation model without transmitting their original behavior data. However, existing federated learning solutions disregard the inherent limitations of resource-constrained mobile devices, which include limited storage space, computational overhead, and communication bandwidth. Although deploying a lightweight recommendation model can address the constraints of mobile devices, achieving satisfactory accuracy is difficult even with substantial communication overhead incurred during multiple rounds of federated learning training due to the limitations of the lightweight model’s capabilities and the sparsity of recommendation data. To address this issue, we propose AeroRec, a n e fficient on-device Feder ated Recommendation Framework. In AeroRec, we use federated self-supervised distillation to enhance the global model after each parameter aggregation in each round. This approach not only accelerates the convergence rate but also surpasses the upper limit of the lightweight model’s capability, thereby enabling a higher accuracy. We demonstrate that AeroRec outperforms several state-of-the-art FRS frameworks regarding recommendation accuracy and convergence speed through extensive experiments on three real-world datasets.

Load balancing in data center networks (DCNs) is a crucial and complex undertaking. Multi-path TCP (MPTCP) has been proposed as a cost-effective solution that aims to distribute workloads and improve network resource utilization. However, it can escalate buffer occupancy and undermine burst tolerance, particularly in scenarios involving incast short flows. To address these limitations, we propose a novel multi-path congestion control algorithm, RateMP, to optimize bandwidth utilization efficiency while ensuring burst tolerance in DCNs. RateMP employs a hybrid window and rate control loop with coupled gradient projection adjustment, enabling fast and fine-grained bandwidth allocation and accelerating convergence. Additionally, RateMP eliminates the limitation of cwnd with under-rate pacing to protect incast and busty flows. We prove that RateMP is Lyapunov stable and asymptotically stable, and show the improvement of RateMP through a kernel-based implementation and extended large-scale simulations. RateMP keeps high bandwidth utilization, cuts RTT by 2x and reduces flow completion times (FCT) by 45% in incast scenarios compared to existing algorithms.

The roll out of 5G, coupled with the traffic monitoring capabilities of modern industry-grade networks, offers an unprecedented opportunity to closely observe the impact that the introduction of a new major wireless technology has on the end users. In this paper, we seize such a unique chance, and carry out a first-of-its-kind in-depth analysis of 5G adoption along spatial, temporal and service dimensions. Leveraging massive measurement data about application-level demands collected in a nationwide 4G/5G network, we characterize the impact of the new technology on when, where and how mobile subscribers consume 5G traffic both in aggregate and for individual types of services. This lets us unveil the overall incidence of 5G in the total mobile network traffic, its spatial and temporal fluctuations, its effect on the way 5G services are consumed, the way individual services and geographical locations contribute to fluctuations in the 5G demand, as well as surprising connections between socioeconomic status of local populations and the way the 5G technology is presently consumed.

Time-sensitive applications have become increasingly prevalent in modern networks, necessitating the development of Delay-Guaranteed Routing (DGR) solutions. Finding an optimal DGR solution remains a challenging task due to the NP-hard nature of the problem and the dynamic nature of network traffic. In this paper, we propose Deadline-Driven Routing (DDR), a distributed traffic-aware adaptive routing protocol that addresses the DGR problem. Inspired by online navigation techniques, DDR leverages real-time traffic conditions to optimize routing decisions and ensure on-time packet delivery. By combining network topology-based path generation with real-time traffic knowledge, each router can adjust packet forwarding directions to meet its heterogeneous latency requirements. Comprehensive simulations on real-world network topologies demonstrate that DDR can consistently provide delay-guaranteed service in different network topologies with varying traffic conditions. In addition, DDR ensures backward compatibility with legacy devices and existing routing protocols, making it a viable solution for supporting delay-guaranteed service.

High-precision time synchronization is a vital prerequisite for many modern applications and technologies, including Smart Grids, Time-Sensitive Networking (TSN), and 5G networks. Although the Precision Time Protocol (PTP) can accomplish this requirement in trusted environments, it becomes unreliable in the presence of specific cyber attacks. Mainly, time delay attacks pose the highest threat to the protocol, enabling attackers to diverge targeted clocks undetected. With the increasing danger of cyber attacks, especially against critical infrastructure, there is a great demand for effective countermeasures to secure both time synchronization and the applications that depend on it. However, current solutions are not sufficiently capable of mitigating sophisticated delay attacks. For example, they lack proper integration into the PTP protocol, scalability, or sound evaluation with the required microsecond-level accuracy. This work proposes an approach to detect and counteract delay attacks against PTP based on cyclic path asymmetry measurements over redundant paths. For that, we provide a method to find redundant paths in arbitrary networks and show how this redundancy can be exploited to reveal and mitigate undesirable asymmetries on the synchronization path that cause the malicious clock divergence. Furthermore, we propose PTPsec, a secure PTP protocol and its implementation based on the latest IEEE 1588-2019 standard. With PTPsec, we advance the conventional PTP to support reliable delay attack detection and mitigation. We validate our approach on a hardware testbed, which includes an attacker capable of performing static and incremental delay attacks at a microsecond precision. Our experimental results show that all attack scenarios can be reliably detected and mitigated with minimal detection time.

Emerging integrated space and terrestrial networks (ISTN) built upon low earth orbit (LEO) satellite constellations aim at providing planet-wide Internet services, not only for residential users, but also for mobile users (e.g., in airplane and cruise scenarios). Efficiently managing global mobility and keeping connections active for mobile users is critical for ISTN operators. However, our quantitative analysis identifies that existing mobility management (MM) schemes suffer from frequent connection interruptions and long latency in ISTN scenarios. The fundamental challenge stems from a unique characteristic of ISTNs: not only users are mobile, but also core network infrastructures (i.e., LEO satellites) are frequently changing their locations in the network.To facilitate seamless and low-latency satellite Internet services, this paper presents SkyCastle, a novel network-based global mobility management mechanism. SkyCastle incorporates two key techniques to address frequent connection interruptions in ISTNs. First, to reduce the interruption time, SkyCastle adopts distributed satellite anchors to track the location changes of mobile nodes, manage handovers and avoid routing convergence. Second, SkyCastle leverages an anchor manager to schedule MM functionalities at satellites to reduce deployment costs while guaranteeing low latency. Extensive evaluations combining real constellation information and mobile user trajectories show that: SkyCastle can improve up to 55.8% uninterrupted time and reduce 47.8% latency as compared to other existing MM solutions.

In recent times, gait recognition, a type of biometric identification, has been widely used for area access control and smart homes. It improves convenience, privacy, and personalized experiences. Contemporary academic inquiry centers on privacy-preserving wireless sensing solutions as substitutes for computer vision. Yet, prevailing strategies heavily lean on abstract features, leading to inherent limitations in interpretability and stability. Fortunately, the widespread utilization of smart speakers has opened up opportunities for acoustic sensing, making it possible to extract more interpretable features. In this paper, we further push the limit of acoustic recognition with visual interpretability by sequentially visualizing fine-grained acoustic human gait features. The construction of initial gait profiles involves matrixing and compressing multipath gait echoes, resulting in imperceptible gait indications. Interpretability is then achieved through novel micro-range profiles, incorporating innovations such as clutter elimination using the Mobile Target Detector (MTD), compensation for farther echo strength, and subtraction of macro torso migration. These interpretable gait profiles offer practical benefits by enhancing data utilization, optimizing abnormal data handling, and improving model stability. Extensive evaluations with an open experimental scenario have been conducted to demonstrate accuracy reaching 97.5% in general, and robust performance against impacts from various practical factors.

Network measurement is critical for various network applications, but scaling measurement techniques to the network-wide level is challenging for existing sketch-based solutions. Centralized sketch deployment provides low resource usage but suffers from poor load balancing. In contrast, collaborative measurement achieves load balancing through flow distribution across switches but requires high resource usage. This paper presents a novel lightweight distributed deployment framework that overcomes the limitations above. First, our framework is lightweight such that it splits sketches into segments and allocates them across forwarding paths to minimize resource usage and achieve load balancing. This also enables per-packet load balancing by distributing computations across switches. Second, our framework is also optimized for load balancing by coordinating between flows and enabling finer-grained flow distribution. We evaluate the proposed framework on various network topologies and different sketch deployments. Results indicate our solution matches the load balancing of collaborative measurement while approaching the low resource usage of centralized deployment. Moreover, it achieves superior performance in per-packet load balancing, which is not considered in previous deployment policies. Our work provides efficient distributed sketch deployment to strike a balance between load balancing and resource usage enabling effective network-wide measurement.

Probabilistic maximum coverage (PMC) is an important framework that can model many network applications, including mobile crowdsensing, content delivery, and task repli¬cation. In PMC, an operator chooses nodes in a graph that can probabilistically cover other nodes, aiming to maximize the total rewards from the covered nodes. To tackle the challenge of unknown parameters in network environments, PMC are studied under the online learning context, i.e., the PMC bandit. However, existing PMC bandits lack context-awareness and fail to exploit valuable contextual information, limiting their efficiency and adaptability in dynamic environments. To address this limitation, we propose a novel context-aware PMC bandit model (C-PMC). C-PMC employs a linear structure to model the mean outcome of each arm, effectively incorporating contextual information and enhancing its applicability to large-scale network systems. Then we design a variance-adaptive contextual combinatorial upper confidence bound algorithm (VAC2UCB), which utilizes second-order statistics, specifically variance, to re-weight feedback data and estimate unknown parameters. Our theoretical analysis shows that C-PMC achieves a regret of $\tilde O(d\sqrt {|\mathcal{V}|T} )$, independent of the number of edges $|\mathcal{E}|$ and action size K. Finally, we conduct experiments on synthetic and real-world datasets, showing the superior performance of VAC2UCB in context-aware mobile crowdsensing and user-targeted content delivery applications.

With the development of network-intensive applications like machine learning and cloud storage, there are two growing trends: (i) RDMA has been widely deployed to enhance underlying high-speed networks; (ii) applications are deployed on geographically distributed datacenters to meet customer demands (e.g., low access latency to services or regular data backups). To fully utilize the benefits of RDMA, we desire to support long-haul RDMA transport for cross-datacenter applications. Different from common intra-datacenter communications, the hybrid of long-haul and intra-datacenter traffic complicates the congestion state, and the considerably long control loop makes it more severe. We revisit existing congestion control methods and find they are insufficient to address the hybrid traffic congestion.Note that regional datacenters are connected by dedicated long-haul optical fiber and datacenter interconnection (DCI) switches directly. In this paper, we propose Bilateral Congestion Control (BiCC), a novel solution relying on two-side DCI-switches to bilaterally alleviate the hybrid traffic congestion in the sender-side and receiver-side datacenter while serving as a building block for existing host-driven methods. BiCC can shorten the control loop to a single datacenter scale and aggregate congestion information across the whole datacenter. We implement BiCC on commodity P4-based switches and conduct evaluations using both testbed experiments and NS3 simulations. The extensive evaluation results show that BiCC ensures fast congestion avoidance. Thus, BiCC reduces the average FCT for intra-datacenter and inter-datacenter traffic by up to 53% and 51%, respectively, in large-scale simulations.