INFOCOM 2024 Papers

Blood pressure (BP) measurement is significant to the assessment of many dangerous health conditions. Apart from invasively inserting catheters into arteries, non-invasive approaches typically rely on wearing devices on specific skin areas with consistent pressure. However, this can be uncomfortable and unsuitable for certain individuals, and the accuracy of these methods may significantly decrease due to improper device placements and wearing states. Recently, contactless methods leveraging RF technology have emerged as a potential alternative. However, these methods suffer from the drawback of overfitting deep learning (DL) models without a sound physiological basis, resulting in a lack of clear explanations for their outputs. Consequently, such limitations lead to skepticism and distrust among medical experts. In this paper, we propose hBP-Fi, a contactless BP measurement system driven by hemodynamics acquired via RF sensing. In addition to its contactless convenience, hBP-Fi is superior to other RF sensing approaches in i) grounding on hemodynamics as the key physical process of heart-pulse activities, ii) exploiting beam-steerable RF devices to achieve a super-resolution scan on the fine-grained pulse activities along arm arteries, and iii) ensuring the trustworthiness of system outputs via an explainable (decision-understandable) DL model. Extensive experiments with 35 subjects demonstrate that hBP-Fi can achieve the error of -2.05±6.83 mmHg and 1.99 ± 6.30 mmHg for monitoring systolic and diastolic blood pressures, respectively.

LoRa Localization, a fundamental service in LoRa networks, has garnered significant attention due to its long-range capabilities and low power consumption. However, existing approaches for LoRa localization are either incompatible with commercial devices or highly susceptible to environmental factors. To tackle this challenge, we propose SyncLoc, a TDoA-based LoRa localization framework that integrates a dedicated node for multi-dimensional time-drift correction. Our proposal is built on two key observations: firstly, the nanosecond-level measurement of time differences between gateways, and secondly, the substantial impact of SNR on gateway time drift. To accomplish our objective, we present three progressively enhanced versions of SyncLoc, each intended to comprehensively analyze the factors influencing LoRa time synchronization accuracy across different deployment scenarios involving nodes, carrier frequencies, and spreading factors. In addition to improving accuracy, we identify inefficiencies in LoRa’s multi-node concurrent localization, and introduce SyncLoc-4, a multi-node localization scheduling mechanism that optimizes efficiency with a 2-approximation ratio. Extensive experiments utilizing commercial LoRa devices in real-world demonstrates a 2.44× improvement in accuracy. Furthermore, simulations of large-scale networks exhibit a 2.47× boost in localization scalability (i.e., the number of concurrently located nodes) when employing SyncLoc instead of LoRaWAN.

Transport Layer Security (TLS) connections involve costly cryptographic operations which incur significant resource consumption in the cloud. Hardware accelerators are affordable substitutes of expensive CPU cores to accommodate with the constantly increasing security requirements of datacenters. Existing accelerators virtualization mainly relies on passthrough of Single Root I/O Virtualization (SR-IOV) devices. However, deficiency of service accessibility, functionality and availability make device passthrough not an optimal solution for heterogeneous accelerators with different capabilities. To make up the gap, we propose vCrypto, a unified para-virtualization framework for heterogeneous cryptographic resources. vCrypto supports stateful crypto requests offloading and result retrieval with session lifecycle management and event driven notification. vCrypto transparently integrates virtual crypto device capabilities into the OpenSSL framework to benefit existing applications that are based on crypto library APIs without modification. Multiple physical resources can be partitioned flexibly and scheduled cooperatively to enhance the functionality, performance and robustness of virtual crypto service. Finally, vCrypto achieves an optimized performance with two layers polling and memory sharing mechanism. The comprehensive experiments show that with the same cryptographic resources used, vCrypto framework can provide 2.59x to 3.36x higher AES-CBC-HMAC-SHA1 throughput compared to passthrough SR-IOV device.

Butterfly counting is widely used to analyze bipartite networks, but counting butterflies in original bipartite networks can reveal sensitive data and pose a risk of individual privacy, specifically edge privacy. Current privacy notions do not fully address the needs of both user-user and user-item bipartite networks. In this paper, we propose a novel privacy notion, edge decentralized differential privacy (edge DDP), which preserves edge privacy in any bipartite network. We also design the randomized edge protocol (REP) to perturb real edges in bipartite networks. However, a significant amount of noise in perturbed bipartite networks often leads to an overcount of butterflies. To achieve accurate butterfly counting, we design the randomized group protocol (RGP) to reduce noise. By combining REP and RGP, we propose a two-phase framework called butterfly counting in limitedly synthesized bipartite networks (BC-LimBN) to synthesize networks for accurate butterfly counting. BC-LimBN has been rigorously proven to satisfy edge DDP. Our experiments on various datasets confirm the high accuracy of BC-LimBN in butterfly counting and its superiority over competitors, with a mean relative error of less than 10% at most. Furthermore, our experiments show that BC-LimBN has a low time cost, requiring only a few seconds on our datasets.

We study a Stackelberg game between an attacker and a defender on large Active Directory (AD) attack graphs where the defender employs a set of honeypots to stop the attacker from reaching high-value targets. Contrary to existing works that focus on small and static attack graphs, AD graphs typically contain hundreds of thousands of nodes and edges and constantly change over time. We consider two types of attackers: a simple attacker who cannot observe honeypots and a competent attacker who can. To jointly solve the game, we propose a mixed-integer programming (MIP) formulation. We observed that the optimal blocking plan for static graphs performs poorly in dynamic graphs. To solve the dynamic graph problem, we re-design the mixed-integer programming formulation by combining m MIP (dyMIP(m)) instances to produce a near-optimal blocking plan. Furthermore, to handle a large number of dynamic graph instances, we use a clustering algorithm to efficiently find the m-most representative graph instances for a constant m (dyMIP(m)). We prove a lower bound on the optimal blocking strategy for dynamic graphs and show that our dyMIP(m) algorithms produce close to optimal results for a range of AD graphs under realistic conditions.

Edge native applications usually consist of several dependent tasks encapsulated in containers and started on-demand in the edge cloud. Unfortunately, the application performance is deeply affected by the notorious cold startup problem of containers. Pre-warming Zygote container pre-imported certain common packages has been proven as an effective startup acceleration solution. Since a Zygote can be shared among colocated tasks that require identical common packages, not only the Zygote planning but also the task scheduling decisions shall be carefully made to maximize the benefit of the Zygotes pre-warmed in limited memory. Additionally, task dependency necessitates co-locating highly dependent tasks on the same server, naturally raising a dilemma in task scheduling. To this end, in this paper, we investigate the problem of how to plan Zygote and schedule tasks for application completion time minimization, which is proved to be NP-hard. We further propose a Priority and Popularity (P&P) based edge native application acceleration algorithm. Both theoretical analysis and extensive experiments demonstrate the effectiveness of our proposed algorithm. The experiment results show that P&P can reduce the application completion time by 11.7%.

We present a robust method to analyze a broad range of classical queueing models, e.g., the GI/G/1 queue with renewal arrivals, an AR/G/1 queue with alternating renewals (AR), as a special class of Semi-Markovian processes, and Markovian fluids queues. At the core of the method lies a standard change-of-measure argument to reverse the sign of the negative drift in the underlying random walks. Combined with a suitable representation of the overshoot, we obtain exact results in terms of series. Closed-form and computationally fast bounds follow by taking the series’ first terms, which are the dominant ones because of the positive drift under the new probability measure. The obtained bounds generalize the state-of-the-art class of martingale bounds and can be much sharper by orders of magnitude.

Mobile Crowdsensing is a type of technology that utilizes mobile devices and volunteers to gather data about specific topics at large scales in real-time. However, in practice, limited participation leads to missing data, i.e., the collected data may be sparse, which makes it difficult to perform accurate analysis. A possible technique called sparse crowdsensing incorporates the sparse case with data completion, where unsensed data could be estimated through inference. However, sparse crowdsensing typically suffers from poor performance during the data completion stage due to various challenges: the sparsity of the sensed data, reliance on numerous timeslots, and uncertain spatiotemporal connections. To resolve such few-shot issues, the proposed solution uses the Correlated Data Fusion for Matrix Completion (CDFMC) approach, which leverages a small amount of objective data to retrain an auxiliary dataset-based pre-trained model that can estimate unsensed data efficiently. CDFMC is trained using a combination of the traditional Deep Matrix Factorization and the Kalman Filtering, which not only enables the efficient representation and comparison of data samples but also fuses the objective data and auxiliary data effectively. Evaluation results show that the proposed CDFMC outperforms baseline techniques, achieving high accuracy in completing unsensed data with minimal training data.

We consider a hierarchical inference system with multiple clients connected to a server via a shared communication resource. When necessary, clients with low-accuracy machine learning models can offload classification tasks to a server for processing on a high-accuracy model. We propose a distributed online offloading algorithm which maximizes the accuracy subject to a shared resource utilization constraint thus indirectly realizing accuracy-delay tradeoffs possible given an underlying network scheduler. The proposed algorithm, named Lyapunov-EXP4, introduces a loss structure based on Lyapunov-drift minimization techniques to the bandits with expert advice framework. We prove that the algorithm converges to a near-optimal threshold policy on the confidence of the clients’ local inference without prior knowledge of the system’s statistics and efficiently solves a constrained bandit problem with sublinear regret. We further consider settings where clients may employ multiple thresholds, allowing more aggressive optimization of overall accuracy at a possible loss in fairness. Extensive simulation results on real and synthetic data demonstrate convergence of Lyapunov-EXP4, and show the accuracy-delay-fairness tradeoffs achievable in such systems.

Sharding is one of the most promising techniques for improving blockchain scalability. In blockchain state sharding, account migration across shards is crucial to the low ratio of cross-shard transactions and cross-shard workload balance. Through reviewing state-of-the-art protocols proposed to reconfigure blockchain shards via account shuffling, we find that account migration plays a significant role. From the literature, we only find a related work that utilizes the lock mechanism to realize account migration. We call this method the SOTA Lock, in which both the target account’s state and its associated transactions need to be locked when migrating this account between shards. Thereby, SOTA Lock causes a high makespan to the associated transactions. To address these challenges of account migration, we propose a dedicated Fine-tuned Lock protocol. Unlike SOTA Lock, Fine-tuned Lock enables real-time processing of the affected transactions during account migration. Thus, the makespan of associated transactions can be lowered. We implement Fine-tuned Lock protocol using an open-sourced blockchain testbed (i.e., BlockEmulator) and deploy it in Tencent cloud. The experimental results show that the proposed Fine-tuned Lock outperforms the SOTA Lock in terms of transaction makespan. For example, the transaction makespan of Fine-tuned Lock achieves around 30% the makespan of SOTA Lock.

In this paper, we study an online cost optimization problem for distributed data access. The goal of this problem is to dynamically create and delete data copies in a multi-server distributed system as time goes, in order to minimize the total storage and network cost of serving access requests. We propose an online algorithm with randomized storage periods of data copies in the servers, and derive an optimal probability density function of storage periods, which makes the algorithm achieve a competitive ratio of $1 + \frac{{\sqrt 2 }}{2}$. An example is presented to show that the competitive analysis of our algorithm is tight. Experimental evaluations using real data access traces demonstrate that our algorithm outperforms the best known deterministic algorithm.

Sharding is a prevailing solution to enhance the scalability of current blockchain systems. However, the cross-shard commit protocols adopted in these systems to commit cross-shard transactions commonly incur multi-round shard-to-shard communication, leading to low performance. Furthermore, most solutions only focus on simple transfer transactions without supporting complex smart contracts, preventing sharding from widespread applications. In this paper, we propose LightCross, a novel sharding blockchain system that enables efficient execution of complex cross-shard smart contracts. First, LightCross offloads the execution of cross-shard transactions into off-chain executors equipped with the TEE hardware, which can accommodate execution for arbitrarily complex contracts. Second, we design a lightweight cross-shard commit protocol to commit cross-shard transactions without multi-round shard-to-shard communication between shards. Last, LightCross lowers the cross-shard transaction ratio by dynamically changing the distribution of contracts according to historical transactions. We implemented the LightCross prototype based on the FISCO-BCOS project and evaluated it in real-world blockchain environments, showing that LightCross can achieve 2.6× more throughput than state-of-the-art sharding systems.

Mobile application updates occur frequently, and they continue to add considerable traffic over the Internet. Differencing algorithms, which compute a small delta between the new version and the old version, are often employed to reduce the update overhead. Transforming the old and new files into the decoded similarity spaces can drastically reduce the delta size. However, this transformation is often hindered by two practical reasons: (1) insufficient decoding (2) long recompression time. To address this challenge, we have proposed two general approaches to transforming the compressed files (more specifically, deflate stream) into the full decoded similarity space and partial decoded similarity space, with low recompression time. The first approach uses recompression-aware searching mechanism, based on a general full decoding tool to transform deflate stream to the full decoded similarity space with a configurable searching complexity, even when it cannot be recompressed identically. The second approach uses a novel solution to transform a deflate stream into the partial decoded similarity space with differencing-friendly LZ77 token reencoding. We have also proposed an algorithm called MDiffPatch to exploit the full and partial decoded similarity spaces. The algorithm can well balance compression ratio and recompression time by exposing a tunable parameter. Extensive evaluation results show that MDiffPatch achieves lower compression ratio than state-of-the-art algorithms and its tunable parameter allows us to achieve a good tradeoff between compression ratio and recompression time.

The nature of datacenter traffic is a high volume of bursty tiny flows and standing long flows, which forms the coexistence of transient and persistent congestion. Traditional congestion control (CC) algorithms have inherent limitations in reconciling fast response and high efficiency towards transients with stability and fairness during persistence. In this paper, we provide an insight that re-architects CC with two control laws, tailored to transient and persistent concerns, respectively. Armed with this key insight, we propose bimodal congestion control (BCC), which is founded on two core ideas: (i) Quaternary network state detection, which further distinguishes transient and persistent states in switches, and (ii) Bimodal control law, which is manifested as the transient controller and persistent controller at sources. The transient controller employs a precise control paradigm that pauses flows to drain backlogged packets and ramps down/up flow rates to bottleneck bandwidth directly, striving for high efficiency. The persistent controller grounds itself in traditional CC algorithms, inheriting stability and fairness. We implement BCC in the Linux kernel and P4-programmable switch. In our evaluation, compared to DCQCN, HPCC, PowerTCP, and Swift, BCC reduces flow completion times by 14% ~ 99%.

Network virtualization, software-defined infrastructure, and orchestration are pivotal elements in contemporary networks, yielding new vectors for optimization and novel capabilities. In line with these principles, O-RAN presents an avenue to bypass vendor lock-in, circumvent vertical configurations, enable network programmability, and facilitate integrated artificial intelligence (AI) support. Moreover, modern container orchestration frameworks (e.g., Kubernetes, Red Hat OpenShift) simplify the way cellular base stations, as well as the newly introduced RAN Intelligent Controllers (RICs), are deployed, managed, and orchestrated. While this enables cost reduction via infrastructure sharing, it also makes it more challenging to meet O-RAN control latency requirements, especially during peak resource utilization. For instance, the Near-real-time RIC is in charge of executing applications (xApps) that must take control decisions within one second, and we show that container platforms available today fail in guaranteeing such timing constraints. To address this problem, we propose ScalO-RAN, a control framework rooted in optimization and designed as an O-RAN rApp that allocates and scales AI-based O-RAN applications (xApps, rApps, dApps) to: (i) abide by application-specific latency requirements, and (ii) monetize the shared infrastructure while reducing energy consumption. We prototype ScalO-RAN on an OpenShift cluster with base stations, RIC, and a set of AI-based xApps deployed as micro-services. We evaluate ScalO-RAN both numerically and experimentally. Our results show that ScalO-RAN can optimally allocate and distribute O-RAN applications within available computing nodes to accommodate even stringent latency requirements. More importantly, we show that scaling O-RAN applications is primarily a time-constrained problem rather than a resource-constrained one, where scaling policies must account for stringent inference time of AI applications, and not only how many resources they consume.

Modern communication networks support local fast re-routing (FRR) to quickly react to link failures. However, configuring such FRR mechanisms is challenging as the rules have to be defined ahead of time, without knowledge of the failures, and can depend only on local decisions made by the nodes incident to a failed link. Designing failover protection against multiple link failures is particularly difficult. We present a novel synthesis approach which addresses this challenge by generating FRR rules in an automated and provably correct manner. Our network model assumes that each node maintains a prioritised list of backup links (a.k.a. skipping forwarding)—an FRR method that allows for a memory-efficient deployment. We study the theoretical properties of the model and implement a synthesis method in our tool SyPer that aims to provide perfect resilience: if there are up to k link failures, we can always route traffic between any two nodes as long as they are still connected in the underlying physical network. To this end, SyPer focuses on the synthesis of efficient forwarding rules using the BDD (binary decision diagram) methodology and our empirical evaluation shows that SyPer is feasible, and can synthesize robust network configuration in realistic settings.

In this paper, we consider the problem of approximate membership testing problem on skewed network traffic traces, in which some hot or popular items repeat frequently. Previous solutions suffer from either high false positive rates or low lookup throughput. To address this problem, we propose a variant of the cuckoo filter, enhanced with a hotness-aware suffix cache. We note that a false positive item must have a matched fingerprint in the cuckoo filter, and propose to reduce false positives by memorizing them, but with their suffixes only. For each false positive item, we apply a linear-congruential-based hash function and then divide the hash value into three parts: the bucket index to be accessed in the cuckoo filter, the fingerprint to be stored in the cuckoo filter, and the suffix to be cached. Combining the three parts, we propose RCF that can uniquely identify a hot false positive item, which thus reduces hot false positives. Our evaluation results indicate that RCF significantly outperforms non-adaptive filters on skewed data traces. Given the same memory size, it achieves a much lower false positive ratio without sacrificing its lookup throughput. Compared with adaptive filters, RCF provides a competitive false positive ratio while offering a considerably higher lookup throughput.

The emergence of network function virtualization has enabled network function chaining as a flexible approach for building complex network services. However, the high degree of flexibility envisioned for orchestrating network function chains introduces several challenges to support dynamism in workloads and the environment necessary for their realization. Existing works mostly consider supporting dynamism by re-adjusting provisioning of network function instances, incurring reaction times that are prohibitively high in practice. Existing solutions to dynamic packet scheduling rely on centralized schedulers and a priori knowledge of traffic characteristics, and cannot handle changes in the environment like link failures.We fill this gap by presenting FUMES, a reinforcement learning based distributed agent design for the runtime scheduling problem of assigning packets undergoing treatment by network function chains to network function instances. Our design consists of multiple distributed agents that cooperatively work on the scheduling problem. A key design choice enables agents, once trained, to be applicable for unknown chains and traffic patterns including branching, and different environments including link failures. The paper presents the system design and shows its suitability for realistic deployments. We empirically compare FUMES with state-of-the-art runtime scheduling solutions showing improved scheduling decisions at lower server capacity.

Adaptive video streaming has been studied for over 10 years and has demonstrated remarkable performance. However, adaptive video streaming is not an independent algorithm but relies on other components of the video system. Consequently, as other components undergo optimization, the gap between the traditional simulator and the real-world system continues to grow which makes the adaptive video streaming algorithm must adapt to these variations.In order to address the challenges facing industrial adaptive video streaming, we introduce a novel offline reinforcement learning framework called Backwave. This framework leverages history logs to reduce the sim-real gap. We propose new metrics based on counterfactual reasoning to evaluate its performance and we integrate expert knowledge to generate valuable data to mitigate the issue of data override. Furthermore, we employ curriculum learning to minimize additional errors.We deployed Backwave on a mainstream commercial short video platform, Kuaishou. In a series of A/B tests conducted nearly one month with over 400M daily watch times, Backwave consistently outperforms prior algorithms. Specifically, Backwave reduces stall time by 0.45% to 8.52% while maintaining comparable video quality and Backwave demonstrates improvements in average play duration by 0.12% to 0.16%, and overall play duration by 0.12% to 0.26%.