This paper presents our experience deploying TianYan on Alibaba Cloud's global production WAN, which is, to the best of our knowledge, the first system for verifying non-deterministic convergence on a global production WAN. In daily operation, we rely on simulation-based configuration verifiers that assume a single converged data plane to ensure reliability and performance. However, non-deterministic convergence—where a configuration yields different converged data planes—undermines verification accuracy and has caused a production incident, motivating the need to analyze non-deterministic convergence itself. At scale, this is challenging because the analysis space grows exponentially with the number of routers. TianYan addresses this challenge with a key insight: by leveraging routing similarity among routers within the same group—a common fault-tolerance practice—it reduces exponential complexity from the number of routers to the number of groups, enabling efficient convergence analysis. Over a year of deployment, TianYan identified non-deterministic convergence in ~2% of all prefixes, exposed unnoticed design flaws, and improved simulation-based verification accuracy through integration. We share representative cases and evaluation results from our production WAN, distilling key operational lessons and practical guidelines for managing nondeterminism at scale.

Result reproduction of computer networking research is challenging as the scarcity of open-source implementations and the complexity of heterogeneous system architectures. Even though Large Language Models have demonstrated potential in code generation, existing code generation frameworks often fail to address the long-context constraints and intricate logical dependencies, which are vital in reproducing network systems from academic papers. Thus, we introduce RepLLM, an end-to-end multi-agent framework designed to automate code reproduction from paper content. RepLLM features a collaborative architecture comprising four specialized agents—Content Parsing, Architecture Design, Code Generation, and Audit & Repair, which are coordinated through Shared Memory mechanism to ensure global context consistency. With the enhancement of Structured Chain-of-Thought LLM reasoning and a sandbox-isolated static-dynamic debugging methodology, our framework effectively resolves semantic discrepancies and runtime errors, thereby improving reliable reproductions. Extensive evaluations on representative papers in top conferences demonstrate that RepLLM outperforms state-of-the-art system-level LLM frameworks in generating compile-ready and logically correct systems. Our results show that, with the aid of RepLLM, we can reproduce 95% of the original benchmarks within approximately two hours while reducing token consumption by up to 10% compared with state-of-the-art baselines.

Robotic swarm coordination holds transformative potential for applications such as warehouse automation, search & rescue, and entertainment. However, approaches relying on wearable devices or vision-based systems are often constrained by hardware-intensive, high computational requirements, reliance on line-of-sight, and privacy concerns. Wireless sensing, particularly using Channel State Information (CSI), offers a promising alternative by translating environmental perturbations into CSI variation data. Nevertheless, existing CSI-based systems face significant challenges in domain adaptation, resource limitation, and scalability issues. This paper introduces MaestroBot, a hierarchical motion coordination system that combines distributed CSI-based wireless sensing with domain-adaptive learning to address these limitations. For leader robots, the system features a lightweight hand gesture recognition model, built on a “Hybrid-Single” knowledge distillation framework, achieving up to 95.87% accuracy while maintaining adaptability across diverse domains. For follower robots, the hierarchical motion propagation model leverages localized CSI analysis and dual-layer error correction mechanisms to deliver 97.2% accuracy with a low latency of 0.085 seconds, even in multi-row formations. Additionally, its cost-effective hardware design ensures practical scalability and real-world deployability. These results position MaestroBot as an efficient, robust, and privacy-preserving solution for large-scale robotic swarm coordination in dynamic environments.

With the rapid advancement of mobile technologies, including self-driving cars and drones, the deployment of mobile software has become increasingly complex. In this context, virtualization plays a pivotal role by simplifying service deployment through containers and enabling container orchestration plat forms to efficiently manage an expanding number of container clusters. This is achieved by leveraging standardized interfaces and minimizing resource optimization overhead. However, the use of distributed servers in mobile edge clusters introduces several challenges, such as bandwidth limitations, network performance fluctuations, and resource constraints, which complicate deployment in these dynamic and resource-constrained environments. In this paper, we rethink the layer-based structure, a fundamental container design, and analyze the challenges and potential of real edge platform traces. Consequently, we propose BREAK, an acceleration middleware for efficient container deployment. With the primary insight of enhancing layer-reuse and deriving benefits from it, we develop a co-design approach centered on layer structure for efficient deployment, ensuring backward compatibility: (i) a container image refactoring solution that optimizes efficiency while preserving the stack-of-layers structure, (ii) distributed shared layer-stack caches, dynamically optimized for collaborative container deployment among mobile edge clusters, (iii) a customized Kubernetes (K8s) scheduler extending awareness of network performance, disk space, and container layer cache for container placement, and (iv) a tailored storage-driver of the standard container runtime for efficient layer extraction. Results indicate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to the state-of-the-art approach.

This paper proposes the concept of a “loss-tolerant” RDMA network, instantiating as NüWa. It reveals the fundamental issues under a lossy fabric — packet losses and repetitive retransmission timeouts (RTOs) cause severe performance degradation and even service interruption. The loss-tolerant RDMA must avoid “important” packet losses that trigger RTOs. However, existing loss-protection mechanisms fail to identify these packets precisely. They either generate massive misprotection or ignore selective repeat loss recovery, resulting in buffer overflows and failures of RTO protection. To tackle these issues, NüWa thoroughly analyzes distinct loss-recovery schemes and RTO reasons for commodity NICs. It designsswitch modeandNIC modeto accurately identify and protect all important packets. The switch mode inherently supports the widely deployed non-programmable NICs, while the NIC mode offloads identification complexity to advanced programmable NICs. With effective RTO avoidance, it improves flow completion time (FCT) by 2 ∼ 10× compared to state-of-the-art (SOTA) solutions. In severe incast and large-scale networks, it reduces FCT by 100× compared with a lossless fabric. In storage applications, N¨uWa improves IOPS by ∼ 300% compared to vanilla lossy fabric. For typical AI Workloads, it accelerates AllReduce/AlltoAll communication by 5.5 ∼ 13.7× compared to SOTA lossy network solutions.

Coreset selection is a technique that reduces model training overhead while retaining high accuracy by selecting a representative subset of the training data. The quality of the selected coresets can be assessed by quantifying the coverage of r-radius balls centered at each element to the entire dataset. However, existing methods are limited by primarily optimizing the largest radius or resorting to surrogate approaches derived from importance scores. In this paper, our exploration underscores a new task of optimizing all the coverage radii of coreset elements for the entropy-based method to ensure an effective representation of the underlying data distribution. To this end, we propose the SubPIE algorithm. SubPIE first identifies subpatterns of neighboring samples in the feature space using discrete coordinate descent and then selects a representative sample within each restricted subpattern. Extensive experimental results show that SubPIE can improve the generalization performance of coreset selection compared to 14 baseline methods. More experiments demonstrate the robustness of SubPIE.

Label noise model is a technique to construct controlled noisy datasets for evaluating noise-robust algorithms. However, the quality of the generated noise has not been evaluated thoroughly. In this paper, we propose a novel research question: Do the constructed datasets with the same noise rate have equal effects? We answer this question through a carefully designed experiment: We sequentially generate a series of noisy datasets with equal noise rate by excluding the previous noisy samples while controlling the clean samples. Models trained on these datasets show discrepancy generalization performance, indicating the inequality of noise. Our in-depth analysis reveals that the reasons come from (1) the introduction of non-hard samples and (2) the inequality between hard samples. We propose a primary equal-quality instance-dependent label noise model termed EQIDN, which alleviates both issues based on the identification of hard samples and stratified sampling. We compare the noise quality generated by EQ-IDN and other models. Experimental results demonstrate that the noise generated by EQ-IDN has lower difference and more stable quality.

Runtime traffic analysis on programmable data-plane requires substantial human effort, and the high speed and complexity of dataplane often make human capacity the efficiency bottleneck. While existing work has proposed LLM-based approaches, they typically rely on offline network logs, failing to address the human capacity limitations in real-time environments. This paper explores the potential of leveraging evolving LLMs to mitigate these human-centric challenges in real physical dataplane. It outlines a novel framework called NetSophon, which features an LLM-based brain for efficient decision-making and an effective arm to manipulate and perceive the physical programmable dataplane. Through interactions among the brain, arm, and dataplane, NetSophon acts as a "super-copilot" for human operators, facilitating real-time dataplane traffic analysis at scale. A case study demonstrates NetSophon’s potential to assist human operators in interacting with dataplane.

Traffic engineering (TE) is widely used to optimize network performance in modern networks. Typically, TE is formulated as a multiple-commodity flow (MCF) optimization problem and solved using mathematical solvers or machine learning approaches, but it becomes unscalable as the network size grows. Existing methods often limit available paths for flow allocation to speed up problem-solving, but this compromises TE performance. Achieving both high performance and fast decisionmaking with free path selection remains a significant challenge. This paper proposes TELD, a scalable and high-performance TE framework with free path selection. TELD leverages Graph Neural Networks (GNNs) that are widely proven with high efficiency in capturing network-specific characteristics and enabling faster decision-making than mathematical solvers. Our key idea is to reformulate the MCF problem into a learningfriendly representation and integrate TE constraints directly into GNN training and inference. The key challenge here is how to efficiently combine the problem reformulation with GNN. TELD tackles this with two critical designs. First, observing that GNNs work better with continuous features, TELD relaxes the freepath MCF formulation by treating flow allocation variables as continuous rather than discrete. Second, TELD introduces a multi-constraint hybrid GNN and a result fine-tuning mechanism to further improve GNN efficiency in TE. Extensive experiments show that TELD outperforms the state-of-the-art GNN-based TE framework by$\sim 55\%$and reduces decision latency by three orders of magnitude compared to mathematical solvers.

The fundamental challenge of configuration verification in an interdomain network is privacy because each autonomous system (AS) treats its network configuration files as private information and is not willing to share them with others. In this paper, we present IVeri, a scalable privacypreserving interdomain configuration verification system based on secure multi-party computation. IVeri supports privacypreserving verification and scalable verification via the following designs: (1) a verification algorithm that meets secure multi-party computation security requirements, (2) a data aggregator that reduces communication overhead while preserving privacy, (3) an algorithm that accelerates the simulation process based on routing algebra, and (4) an incremental verification algorithm that handles minor configuration changes. Extensive experiments with open-source datasets demonstrate that IVeri's optimization techniques significantly improve scalability, enabling the verification of large networks with 1000 ASes in under 3 hours, which outperforms state-of-the-art solutions.

Tor is widely used to facilitate anonymous web communication, but it remains vulnerable to Website Fingerprinting (WF) attacks. Although deep learning-based WF attacks have shown promising results, they typically rely on large-scale labeled data and assume consistent conditions between training and deployment. These assumptions limit their practical applicability in real-world scenarios, where data scarcity and domain shifts are common. To address these challenges, recent research has focused on Cross-Domain Few-Shot Website Fingerprinting (CDFSWF), a more realistic yet challenging setting. Existing efforts mainly leverage data augmentation or feature alignment techniques. While data augmentation can mitigate sample scarcity, it often fails to capture true distributional variability. In contrast, many feature alignment WF methods overlook the semantic structure of class relationships, reducing their effectiveness in the target domain. In this paper, we propose CoDA, a novel method designed to improve cross-domain robustness in CDFSWF. CoDA integrates supervised contrastive pre-training, hierarchical flow attention, and prototype-based classification to effectively model semantic traffic structures under domain shifts. Furthermore, a Dual Confidence Alignment (DCA) strategy is introduced during fine-tuning to adaptively align semantic structures. Extensive experiments across various cross-domain scenarios show that CoDA consistently outperforms state-of-the-art baselines in both closed-world and open-world settings.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC proactive reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and global network states, while ensuring efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3 can increase the system throughput by up to 69.6 compared with other alternatives.

There has been a growing interest in applying machine learning to real-world tasks. However, due to the black-box nature of machine learning models, it is crucial to 1) verify important properties of a model and 2) understand the reasons behind a model’s prediction before deploying them in a production environment. Existing approaches typically handle them as two separate and sometimes orthogonal topics. In this paper, we show that the verification and interpretability of machine learning models are tightly related and can be unified by satisfiability modulo theories (SMT). Our key insight is: not only a wide range of properties of machine learning models can be formulated as SMT problems and verified accordingly, but many commonly studied interpretability questions can also be answered by iteratively checking the satisfiability and related properties of multiple SMT problems. Leveraging this insight, we design UINT, a general verification and interpretability framework for learning-based networking systems. UINT 1) allows operators to specify verification and interpretability problems as SMT formulas, 2) encodes the target machine learning models into SMT constraints, and 3) automatically simplifies and solves the corresponding verification and interpretability problems using commodity SMT solvers. We implement a prototype of UINT and evaluate it on real-world learning-based networking systems. Results demonstrate the efficiency and efficacy of UINT in verifying and interpreting key questions for these systems.

Current traffic engineering systems utilize machine learning to optimize traffic distribution but encounter scalability challenges due to the exponential growth of potential paths. They often resort to fixed path approaches, which result in suboptimal traffic distribution compared to unconstrained methods. In this paper, we construct a compact learning model for the free path TE formulation, which can scale to large networks without compromising the feasible region. Additionally, we introduce a Lagrangian-based loss function to drive the solution towards the feasible region. Our preliminary evaluation of real-world topologies demonstrates up to 6000 times speedup and satisfies 97.4% of the total flow compared with the solver.

DNS misconfiguration can result in severe social and financial consequences. Existing DNS configuration verification tools employ a centralized architecture, where all zone files are collected for verification. This architecture faces significant scalability issues (e.g., the verifier becoming the performance bottleneck and not supporting incremental verification). Inspired by the recent proposal of distributed data plane verification and the resemblance between the network data plane and DNS configuration, we propose to rearchitect DNS configuration verification with a distributed design. Our key insight is that by analyzing the query processing behavior of each DNS zone file in parallel and stitching the results in a symbolic way, we can substantially scale up the verification of DNS configuration. Evaluation shows that an up to 9.51× speed up on a dataset with over 410,000 resource records while having small overhead.

Discrete-event simulation (DES) is a prevalent tool for evaluating network designs. Although DES offers full fidelity and generality, its slow performance limits its application. To speed up DES, many network simulators employ parallel discrete-event simulation (PDES). However, adapting existing network simulation models to PDES requires complex reconfigurations and often yields limited performance improvement. In this paper, we address this gap by proposing a parallel-efficient and user-transparent network simulation kernel, Unison, that adopts fine-grained partition and load-adaptive scheduling optimized for network scenarios. We prototype Unison based on ns-3. Existing network simulation models of ns-3 can be seamlessly transitioned to Unison. Testbed experiments on commodity servers demonstrate that Unison can achieve a 40× speedup over DES using 24 CPU cores, and a 10× speedup compared with existing PDES algorithms under the same CPU cores.

Traffic engineering (TE) mechanisms are crucial for achieving optimal levels of network performance over wide-area networks across geographically distributed datacenters. Existing work on traffic engineering formulated the challenges at hand as combinatorial optimization problems, which could take hours to compute on modern wide-area network topologies at the scale of thousands of nodes. To improve the performance of TE mechanisms, we introduce DeepTE, a new TE framework based on machine learning (ML) that is designed for the best possible scalability and performance, capable of completing the computation within milliseconds with networks involving thousands of nodes, and of generating near-optimal TE policies while guaranteeing that all constraints are satisfied. DeepTE is also designed with a distributed ML model architecture, which can be horizontally scaled up to multiple GPUs for even better performance. With real-world traffic matrices, our extensive array of performance evaluations of DeepTE on various network topologies and TE problems show that DeepTE is capable of producing policies within 5% of the optimal results while offering up to 100x performance improvements over state-of-the-art traffic engineering mechanisms.

Satisfiability Modulo Theories (SMT) based network configuration verification tools are powerful tools in preventing network configuration errors. However, their fundamental limitation is efficiency, because they rely on generic SMT solvers to solve SMT problems, which are in general NP-complete. In this paper, we show that by leveraging network domain knowledge, we can substantially accelerate SMT-based network configuration verification. Our key insights are: given a network configuration verification formula, network domain knowledge can (1) guide the search of solutions to the formula by avoiding unnecessary search spaces; and (2) help simplify the formula, reducing the problem scale. We leverage these insights to design a new SMT- based network configuration verification tool called NetSMT. Extensive evaluation using real-world topologies and synthetic network configurations shows that NetSMT achieves orders of magnitude improvements compared to state-of-the-art methods.

Container technology has revolutionized service deployment, offering streamlined processes and enabling container orchestration platforms to manage a growing number of container clusters. However, the deployment of containers in distributed edge clusters presents challenges due to their unique characteristics, such as bandwidth limitations and resource constraints. Existing approaches designed for cloud environments often fall short in addressing the specific requirements of edge computing. Additionally, very few edge-oriented solutions explore fundamental changes to the container design, resulting in difficulties achieving backward compatibility.In this paper, we reevaluate the fundamental layer-based structure of containers. We identify that the proliferation of redundant files and operations within image layers hinders efficient container deployment. Drawing upon the crucial insight of enhancing layer reuse and extracting benefits from it, we introduce BREAK, a holistic approach centered on layer structure throughout the entire container deployment pipeline, ensuring backward compatibility. BREAK refactors image layers and proposes an edge-oriented cache solution to enable ubiquitous and shared layers. Moreover, it addresses the complete deployment pipeline by introducing a customized scheduler and a tailored storage driver. Our results demonstrate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to state-of-the-art approaches.

Open Source Proxy Software (OSPS) provides privacy protection for users accessing the Internet by constructing a private anonymizing network. However, there is a growing concern about whether OSPS can actually prevent privacy leaks as it claims. Researchers have attempted to use AI-based techniques to identify OSPS, but there are two shortcomings in the current studies. First, there is no complete public dataset to support the identification tasks for different requirements. The existing datasets do not cover the most commonly used OSPS tools and their typical configurations. Second, with the introduction of deep learning techniques, the models continue to become complex, resulting in significant computational overhead. Using early flows for identification may make the model lighter, but result in weaker representations and lower classification performance. To address the above shortcomings, we have carried out pioneering work on OSPS traffic identification through early flows. First, we collect the access traffic of three OSPS tools and create a dataset with 8 protocol configurations. Second, we present an innovative Dual-View Identification (DVI) method for OSPS traffic. By considering both static and dynamic views, DVI effectively characterizes early flows and achieves accurate classification through feature fusion. In the static view, spatial distribution features are extracted by representing the early flows as a grayscale picture. In the dynamic view, spatial features and temporal correlations are represented using a flow with multiple packets, similar to a video with multiple frames. Comparative experiments show that DVI achieves over 90% accuracy and F1 scores in all three tasks, which greatly improves its ability to identify different protocol configurations and access sites. Besides, DVI outperforms 5 state-of-the-art methods and achieves low parameters and FLOPS through early flows.

Multiple systems have explored how to use programmable switch ASICs to improve the performance of distributed systems. However, they focus on accelerating read operations and perform poorly under write-intensive workloads. In this paper, we present Gecko, a system that accelerates write operations in distributed key-value store systems using switch ASICs. The core idea of Gecko is to offload the client-side chasing mechanism, a technique deployed by production storage networks, to the programmable switch to simultaneously reduce the perceived and actual write-tail latency in distributed key-value stores. The perceived latency is the interval between the user sending a write request and the user receiving the write success, and there may be replicas that have not yet completed the write, but the actual latency requires all replicas to be successfully written. Gecko not only reduces the perceived write tail latency by deploying the chasing mechanism, but the actual write tail latency is also reduced by utilizing the capabilities of programmable switches. Specifically, Gecko’s in-network chasing design caches a write request at the switch data plane and reports success to the client when only m out of n (usually set to 2 and 3 in production networks, respectively) replicas have been successfully written to the server, and retries the cached write request if the remaining n − m replicas are not successfully written. In addition to caching the write request, Gecko also introduces novel designs to fully implement the chasing controller and a corresponding timer controller in the switch data plane, minimizing the interaction overhead between the switch control and data plane. Extensive experiments on a testbed of Barefoot Tofino switch and commodity servers show that Gecko not only substantially reduces the write tail latency by more than 2.16x caused by transient glitches at servers, but also maintains the same level of reliability as the classic three-replica write operation in distributed key-value stores.

Enabling low-latency end-to-end interdomain communication is critical in edge networks. However, the current network architecture results in unnecessarily long communication paths, leading to high latency between devices. To address this issue, we propose a novel interdomain edge peering framework called Collie. In Collie, edge networks belonging to different network providers collaborate to forward traffic towards destinations, effectively reducing end-to-end communication latency. Importantly, Collie allows network providers to maintain their autonomy in link usage strategy. We also develop a distributed algorithm in Collie that enables edge nodes from different networks to collectively determine optimal routing and traffic assignment, ensuring low-latency delivery while respecting network policies without exposing them. We implement a prototype of Collie and extensively evaluate its performance using real-world topologies. Our results demonstrate that Collie achieves a tight approximation ratio and exhibits scalability in large interdomain edge networks.

Today, more than sixty companies (in the world) are building quantum computers. The natural language of their quantum gates is that of linear algebra in a complex (Hilbert) vector space. Since 2017 it is known that one can replace the linear algebra with some string-rewriting rules no more complicated than the basic rules of arithmetic. The original system was introduced by Terry Rudolph and has been promoted and disseminated in large-scale outreach projects (among others) by Diana Franklin (University of Chicago), Sofia Economou and Ed Barnes (Virginia Tech) and other educators at the high-school level. In this workshop we show how a slightly modified (though still very elementary) system can be used to communicate a visual and entirely operational understanding of key quantum computation concepts such as: superposition, entanglement, phase, interference and unitary state evolution, as they occur in quantum algorithms. Examples include the phase kickback phenomenon, teleportation, and the famous Deutsch-Josza, Bernstein-Vazirani and Grover algorithms along with the GHZ game. We work out concrete examples of proving properties for quantum gates and quantum circuits without resorting at all to complex numbers or matrix multiplication; only simple, abacus-like operations are used, hence the title of the tutorial. We show how this approach can create a genuine bridge to the mathematics of quantum computation, that is, of vector and tensor algebras in complex spaces for students who may have little or no proper mathematical background.

Leveraging the advanced capabilities of Deep Neural Networks (DNNs), attackers can precisely detect users' online activities through traffic monitoring, nullifying the efficacy of current encrypted communication tools/protocols and progressively resulting in privacy leakage. Several defensive methods against DNN-based traffic monitoring (DTM) have been proposed; however, these methods often rely excessively on prior knowledge and incur inevitable additional bandwidth overhead (BWO). Moreover, they frequently generate invalid packets that violate network transmission constraints. To address these drawbacks, in this paper, we propose BYTEFLIPPING, a byte-space grey-box defensive method, which perturbs vulnerable bytes in the transport layer payload to generate adversarial sample packets. We design a Payload Byte Vulnerability Ranking algorithm to pinpoint the most vulnerable bytes and based on this generate adversarial packets to defend DTM. Extensive experiments reveal that ByteFLIPPING performs well in protecting against three DTM methods across two benchmark datasets, significantly decreasing the accuracy of the state-of-the-art ET-BERT by 94%. Compared to baseline defensive methods, BYTEFLIPPING incurs no extra BWO, offers more dependable packet validity, and boasts greater feasibility.

Blockchain and smart contracts are one of the key technologies promoting Web 3.0. However, due to security considerations and consistency requirements, smart contracts currently only support simple and deterministic programs, which significantly hinders their deployment in intelligent Web 3.0 applications. To enhance smart contracts intelligence on the blockchain, we propose SMART, a plug-in smart contract framework that supports efficient AI model inference while being compatible with existing blockchains. To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. To solve the non-determinism brought by model inference, we leverage Trusted Execution Environments (TEEs) to endorse the integrity and correctness of the off-chain execution. We also design distributed attestation and secret key provisioning schemes to further enhance the system security and model privacy. We implement a SMART prototype and evaluate it on a popular Ethereum Virtual Machine (EVM)-based blockchain. Theoretical analysis and prototype evaluation show that SMART not only achieves the security goals of correctness, liveness, and model privacy, but also has approximately 5 orders of magnitude faster inference efficiency than existing on-chain solutions.

Federated learning (FL) is a key solution for datadriven the Artificial Intelligence of Things (AIoT). Although much progress has been made, scalability remains a core challenge for real-world FL deployments. Existing solutions either suffer from accuracy loss or do not fully address the connectivity dynamicity of FL systems. In this article, we tackle the scalability issue with a novel, adaptive FL framework called FedSwarm, which improves system scalability for AIoT by deploying multiple collaborative edge servers. FedSwarm has two novel features: 1) adaptiveness on the number of local updates and 2) dynamicity of the synchronization between edge devices and edge servers. We formulate FedSwarm as a local update adaptation and perdevice dynamic server selection problem and prove FedSwarm‘s convergence bound. We further design a control mechanism consisting of a learning-based algorithm for collaboratively providing local update adaptation on the servers’ side and a bonus-based strategy for spurring dynamic per-device server selection on the devices’ side. Our extensive evaluation shows that FedSwarm significantly outperforms other studies with better scalability, lower energy consumption, and higher model accuracy.

In this paper, we advocate${\sf NetDPI}$, a novel and efficient Deep Packet Inspection (DPI) solution built-in 5G Data Plane for multi-access edge computing, leveraging the unique forwarding while computing capability of emerging programmable switches. As the cornerstone, we propose${\sf FIVE}$, the firstFiltering-plus-Verification algorithm tailored to programmable switches to achieve efficient multiple pattern matching (i.e., the core of DPI). Briefly, the filtering phase introduces a multi-window parallel shift-or algorithm to rapidly screen out all the “suspicious” packet payloads. Meanwhile, the verification phase innovates a level-based state encoding scheme for the Aho–Corasick (AC) algorithm, which substantially increases the number of supported patterns and consequently figures out more “guilty” payloads. We implement the prototype of${\sf NetDPI}$in both software and hardware programmable switches (i.e., BMv2 and Barefoot Tofino2) and make them publicly available. Extensive evaluations indicate that${\sf NetDPI}$provides orders of magnitude improvement in throughput compared to the typical cloud-delivered DPI solutions, and besides${\sf FIVE}$greatly reduces the memory consumption compared to the alternative in-network exact match algorithms under a variety of system settings including different DPI pattern sets and malware-packet percentages.

Interdomain network configuration errors can lead to disastrous financial and social consequences. Although substantial progress has been made in using formal methods to verify whether network configurations conform to certain properties, current tools focus on a single network. The fundamental challenge of configuration verification in an interdomain network is privacy, because each autonomous system (AS) treats its network configuration files as private information and is not willing to share it with others. In this paper, we take a first step toward interdomain network configuration verification and propose InCV, a privacy-preserving interdomain configuration verification system based on data-oblivious computation. Given an interdomain network, InCV allows ASes to collaboratively simulate the running of the network and verify the resulting interdomain routing information base (RIB) without revealing their network configurations to any party. Preliminary evaluation using real-world topologies and synthetic network configurations shows that InCV can verify an interdomain network of 32 ASes within ∼ 52 minutes with reasonable overhead.

In this paper, we show that by capturing the causal relationship among the computation of routers, one can transform the distributed program composed of routing processes into a sequential program, which allows the use of various sequential program analysis theories and tools for diagnosing and repairing routing configuration errors. This insight sheds light on future research on automatic network configuration diagnosis and repair. To demonstrate its feasibility and generality, we give the preliminary design of two methods for routing configuration error diagnosis: (1) data flow analysis using minimal unsatisfiable core and error invariants; and (2) control flow analysis using selective symbolic execution. Using real-world topologies and synthetic configurations, we show that both methods can effectively find errors in routing configurations while incurring reasonable overhead.

At the time of this writing more than 60 (sixty) companies in the world are building quantum computers. These computers, based on quantum physics principles, are radically different from those that operate according to the more familiar principles of classical physics. A quantum algorithm takes a number of classical bits as its input, manipulates them so as to create a superposition of all their possible states, further manipulates this exponentially large superposition to obtain the final quantum result, and then measures the result to get (with the appropriate probability distribution) the same number of output bits as in its input. For the middle phase, there are elementary operations which count as one step and yet manipulate all the exponentially many amplitudes of the superposition. The natural language of these quantum gates is that of linear algebra in a complex (Hilbert) vector space. Since 2017 it is known that it is possible to replace the linear algebra with some string-rewriting rules which are no more complicated than the basic rules of arithmetic. The original system was introduced by Terry Rudolph and has been promoted and disseminated in large-scale outreach projects (among others) by Diana Franklin (University of Chicago) and Sofia Economou and Ed Barnes (Virginia Tech) as well as several other educators at the high-school level. In this paper we show how a slightly modified (though still very elementary) system can be used to communicate a visual and entirely operational understanding of key quantum computation concepts such as: superposition, probability, entanglement, phase, interference and unitary state evolution, as they occur in well-known quantum algorithms. We give concrete examples of proving properties for quantum gates and quantum circuits without resorting at all to complex numbers or matrix multiplication. Only simple, abacus-like operations are used-hence the title of the paper. The system we present allows a novice learner to actually trace a quantum algorithm as if it were a classical computation, which is a rare (and, frankly, borderline incredible) luxury in the area of quantum computation, where traditional debugging is impossible. Examples include the phase kickback phenomenon and the famous Deutsch-Josza algorithm. We end with a discussion (and more examples) of how this approach can create a genuine bridge to the mathematics of quantum computation, that is, of vector and tensor algebras in complex spaces for students who may have little or no proper mathematical background.

Reproducing research results is important for the networking community. The current best practice typically resorts to: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; or (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private ones are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). We first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report our observations and lessons and discuss future open research questions of this proposal.

To protect the confidentiality of communication data, internet users often use encryption protocols (e.g., TLS/SSL) or tools (e.g., VPN, Tor) for network access. Therefore, as a pivotal network management method, encrypted traffic classification technology is vital for guaranteeing the quality of service, the quality of experience, and network security. Researchers have already developed some end-to-end deep learning-based methods to realize encrypted traffic classification. However, given the constrained computational resources available in real-world network measurement scenarios, the existing approaches with high complexity and computation overhead are not appropriate. In this paper, we propose a lightweight model to tackle this issue. Firstly, we propose a base model based on the self-attention mechanism to obtain the key bytes in packets contributing to the classification. Secondly, we leverage these key bytes to reconstruct the input and then streamline the base model, carrying out a lightweight model, i.e.,$\mathcal{L}{-}$ETC. Finally, we implement experiments on three benchmark datasets.$\mathcal{L}-\mathbf{ETC}^{\prime}\mathrm{s}$macro Fl score of the three tasks exceeds 0.92 with only 0.076M (Million) parameters, and the throughput reaches 917 pps, which is also superior to state-of-the-art methods.

People have been leveraging the capabilities of programmable switches, which are programmable in the data plane and process packets at the line rate, to improve the performance of distributed systems. However, few have explored whether programmable switches can speed up problem-solving. In this paper, we select the SAT problem, one of the most fundamental problems in computer science, as a case study to first explore the feasibility and benefits of this line of research. Our intuition is that by exploiting the parallel lookup capability of programmable switches, we can substantially speed up the process of checking whether an assignment is a solution to a SAT problem. Consequently, we design conflict tables using TCAM to quickly check assignment satisfiability. Building on the conflict tables, we propose two SAT solvers, P4-DPLL and Antler. Specifically, P4-DPLL is based on the classical DPLL algorithm and implements a stack data structure using registers and SRAM to efficiently make variable search decisions in the data plane, while Antler utilizes the mirroring capability of the programmable switch to further accelerate the SAT solving, which improves the non-parallel depth-first search-based P4-DPLL into a parallel breadth-first search-based SAT solver. We implement the prototypes of P4-DPLL and Antler on the Tofino switch, and evaluate their performance extensively. Results show that P4-DPLL and Antler improve the solving time by 2x and 169x on 90% quantile of test cases, compared to a CPU-based DPLL implementation. Besides, compared with the most popular SAT solvers, MathSAT and Z3, Antler improves the solving time by 42x and 35x, respectively.

All existing coflow scheduling algorithms compute dynamic-rate schedules that change the rates of flows during transmission. In this paper, we make a crucial finding: although dynamically adjusting the rates of flows could lead to a better coflow completion time (CCT) in theory, it would introduce additional pressures on the congestion control mechanism in the underlying network, which result in poor CCT in practice. This difference between theoretical CCT and practical CCT is further exacerbated in wide-area networks, where the topology does not provide any bisection guarantee as data center networks do. To this end, we designed a fixed-rate coflow scheduling policy called FSCO. Although in theory, the best fixed-rate schedule is usually suboptimal, it keeps the in-flight traffic relatively steady, reducing the risk of triggering congestion control. The core of FSCO is an efficient scheduling algorithm based on the classic network utilization maximization (NUM) framework. We implement a prototype of FSCO and evaluate its performance extensively using real-world topologies and coflow traces. Experimental results show that the total CCT reduces up to 30% compared to baselines while yielding up to 12× speedups compared to the solver.

Low-power wide-area networks (LPWAN) evolve rapidly with advanced communication primitives (e.g., coding, modulation) being continuously invented. This rapid iteration on LPWAN, however, forms a communication barrier between legacy wireless sensor nodes deployed years ago (e.g., ZigBee-based sensor node) with their latest competitor running a different communication protocol (e.g., LoRa-based IoT node): they work on the same frequency band but share different MAC- and PHY-layer regulations and thus cannot talk to each other directly. To break this barrier, we propose LigBee, a cross-technology communication (CTC) solution that enables symbol-level communication from the latest LPWAN LoRa node to legacy ZIGBEE node. We have implemented LigBee on both software-defined radios and commercial-off-the-shelf (COTS) LoRa and ZigBee nodes, and demonstrated that LigBee builds a reliable CTC link from LoRa node to ZigBee node on both platforms. Our experimental results show that i) LigBee achieves a bit error rate (BER) in the order of 10−3with 70 ∼ 80% frame reception ratio (FRR), ii) the range of LigBee link is over 300m, which is 6 ∼ 7.5× the typical range of legacy ZigBee and state-of-the-art solution, and iii) the throughput of LigBee link is maintained on the order of kbps, which is close to the LoRa’s throughput.

Continuing a process that began more than 50 years ago with the publication of Curriculum 68 ACM, IEEE-Computer Society and AAAI have sponsored five efforts to establish international curricular guidelines for undergraduate programs in computing on a roughly 10-year cycle. Over the last 15 years significant advances in quantum technologies have led to a new awareness about their impact on computing (QC). There are now 60 companies worldwide that build quantum computers. In the US the Quantum Economic Development Consortium (QED-C) was created in 2018 to accelerate the quantum industry by establishing a robust supply chain and infrastructure, including workforce and standards. But the continued absence of any serious education in quantum mechanics in a large fraction of traditional US engineering programs, including computer engineering and the closely related CS and data science programs, present many BS degree STEM graduates with the daunting problem of how to get trained quickly and efficiently to pursue the new opportunities in Quantum Information Science and Technology (QIST). To address this issue the ACM Board of Education has teamed up with the QED-C Workforce Development TAC and has developed (for the first time ever and over a period of 18 months) a Quantum Architectures (Q-AR) Knowledge Unit (KU) for CS2023. In November 2022 we asked the QED-C members (industry, academia, national labs, and government agencies) to comment on the proposed competency-based curricular plans along with the selected topics and learning outcomes. We present the analysis of the data we collected during that process.

There has been a growing interest in applying machine learning to real-world tasks. However, due to the blackbox nature of machine learning models, it is crucial to (1) verify important properties of a model and (2) understand the reasons behind a model's prediction before deploying them in a production environment. Existing approaches typically handle them as two separate and sometimes orthogonal topics. In this paper, we show that the verification and interpretability of machine learning models are tightly related and can be unified by satisfiability modulo theories (SMT). Our key insight is: not only a wide range of properties of machine learning models can be formulated as SMT problems and verified accordingly, but many commonly studied interpretability questions can also be answered by iteratively checking the satisfiability and related properties of multiple SMT problems. Leveraging this insight, we design UINT, a general verification and interpretability framework for learning-based networking systems. UINT (1) allows operators to specify verification and interpretability problems as SMT formulas, (2) encodes the target machine learning models into SMT constraints, and (3) automatically solves the corresponding verification and interpretability problems using commodity SMT solvers. We implement a prototype of UINT and evaluate it on real-world learning-based networking systems. Results demonstrate the efficiency and efficacy of UINT in verifying and interpreting key questions for these systems.

People have been leveraging the capabilities of programmable switches, which are programmable in the data plane and process packets at the line rate, to improve the performance of distributed systems. However, few have explored whether programmable switches can speed up problem-solving. In this demonstration, we take a first step to explore the feasibility and benefits of this line of research. Specifically, we select the SAT problem, one of the most fundamental problems in computer science, as a case study. Our intuition is that by exploiting the parallel lookup capability of programmable switches, we can substantially speed up the process of checking whether an assignment is a solution to a SAT problem. In particular, we base on the classical DPLL algorithm and design P4-DPLL [5], which consists of (1) match action tables using TCAM to quickly check assignment satisfiability, and (2) a stack data structure using register and SRAM to efficiently make variable search decisions in the data plane. We implement a prototype of P4-DPLL and evaluate its performance extensively. Results show that P4-DPLL improves the solving time by 101x speedup on 90% quantile of all test cases, compared with a CPU-based DPLL implementation.

Centralized data plane verification (DPV) faces significant scalability issues in large networks (i.e., the verifier being a performance bottleneck and single point of failure and requiring a reliable management network). We tackle this scalability challenge by introducing Tulkun, a distributed, on-device DPV framework. Our key insight is that DPV can be transformed into a counting problem on a directed acyclic graph, which can be naturally decomposed into lightweight tasks executed at network devices, enabling fast data plane checking in networks of various scales and types. With this insight, Tulkun consists of (1) a declarative invariant specification language, (2) a planner that employs a novel data structure DPVNet to systematically decompose global verification into on-device counting tasks, (3) a distributed verification messaging (DVM) protocol that specifies how on-device verifiers efficiently communicate task results to jointly verify the invariants, and (4) a mechanism to verify invariant fault-tolerance with minimal involvement of the planner. Extensive experiments with real-world datasets (WAN/LAN/DC) show that Tulkun verifies a real, large DC in 41 seconds while others tools need minutes or up to tens of hours, and shows an up to 2355× speed up on 80% quantile of incremental verification with small overhead on commodity network devices.

Sixteen years ago, Scott Aaronson remarked (in the presence of Ray Laflamme) that quantum mechanics (QM) resembles an operating system on which the rest of Physics is running its application software (except for general relativity "which has not yet been successfully ported to this particular OS''). Prior to that, it took the insight of an educator and eminent computer scientist (Umesh Vazirani) to realize that a complete and consistent introduction to QM can be given via the language of qubits and quantum gates. Closer to the present, it took the profound intuition of another polymath (Terry Rudolph) to realize that the linear algebra normally at the foundation of such an approach can be replaced with a simple rewriting system accessible to middle school students. Rewriting systems are at the foundation of Computer Science, they are, in fact, the very fabric of it (e.g., Turing machines and lambda calculus), so these are very fortunate developments. Furthermore, a linear algebra prerequisite is now shared firmly in the CS undergraduate curriculum with Machine Learning, a topic that has known a very deep and sudden revival. Quantum Information Science and Technology (QIST) is inherently interdisciplinary and spans physics, computer science, mathematics, engineering, chemistry and materials science. We present three curricular plans for incorporating QIST topics (via Quantum Computing) into the CS undergraduate curriculum. Such plans have been constructed with a preliminary consultation with QED-C members (industry, academia, national labs, and government agencies) asking for comments, suggestions and general input on these three curricular plans.

Along with the development of 5G and IoT technologies in recent years, Inter Data Center (Inter-DC) network is facing an explosive growth of geographically distributed user data, which needs to be duplicated among DCs in a real-time manner. Transmission-based applications require high availability that is going beyond 99.99%. However, with the expansion of Inter-DC network scale, link failures are also growing, which seriously affects data transmission efficiency, so fast link failure recovery is then urgently needed. Many previous works have been done to achieve fast failure recovery, but most of them ignore two key points, 1) the cost of deploying recovery strategies, and 2) the side-effect of re-transmission to network availability. These two factors make the existing failure recovery process too slow to be practical in real-time online industrial environments. To achieve realistic fast recovery from Inter-DC network failures, we propose a failure recovery framework FRAVaR, which achieves high network availability with very little deployment overhead. Particularly, FRAVaR reduces the deployment overhead by a novel incremental routing strategy to isolate link failures. In other words, it only needs to shuffle a tiny amount of traffic within a small failure isolation domain. On this base, FRAVaR further adopts a risk assessment theory named Value-at-Risk (VaR) to control flow re-transmission. We implement a prototype of FRAVaR and conduct a series of experiments on 4 real InterDC network topologies (ATT North America, IBM, GlobalCenter, AGIS). Experiment results show that FRAVaR outperforms state-of-the-art solutions on the recovery speed by 70.2%.1

Vehicle localization service is a fundamental component of intelligent transportation systems. The widely used satellite navigation systems perform poorly in urban areas because the lines of sight to satellites are blocked by complex terrain characteristics, e.g., buildings, elevated streets and interchanges. In this paper, we design RadioLoc, a novel system achieving accurate, efficient, all-terrain vehicle localization with two key design points. First, RadioLoc harvests the frequency modulation (FM) signal, which has higher availability than satellite signal in complex terrains, as the signal source for localization. Second, RadioLoc integrates modern machine learning techniques into the processing of FM signals to efficiently learn the accurate vehicle localization in all-terrain environments. We validate the feasibility of FM-based vehicle localization and corresponding challenges and practical issues via field tests (e.g., signal distortion, signal inconsistency and limited in- vehicle radio bandwidth), and develop a series of advanced techniques in RadioLoc to address them, including adaptive batching, frequency sweeping, a novel multipath delay spread filter, a reconstructive PCA denoiser and a tailored FM feature extractor. We then develop a generic, modular localization module in RadioLoc, and design different learning-based 3D position identification algorithms for this module. We implement a prototype of RadioLoc and perform extensive field experiments to evaluate its efficiency and efficacy. Results show that (1) RadioLoc achieves a real-time localization latency of less than 100 milliseconds; (2) RadioLoc achieves a worst-case localization accuracy of 99.6% even in an underground parking lot, and (3) the horizontal error of RadioLoc is only one sixth of a dedicated GPS device even when the vehicle is moving at a high-speed (i.e., 80 km/h) in a complex highway scenario.

The synchronized Local-SGD(Stochastic gradient descent) strategy becomes a more popular in distributed deep learning (DML) since it can effectively reduce the frequency of model communication and ensure global model convergence. However, it works not well and leads to excessive training time in heterogeneous environments due to the difference in workers' performance. Especially, in some data unbalanced scenarios, these differences between workers may aggravate low utilization of resources and eventually lead to stragglers, which seriously hurt the whole training procedure. Existing solutions either suffer from a heterogeneity of computing resources or do not fully address the environment dynamics.

Current data plane verification (DPV) tools employ a centralized architecture, where a server collects the data planes of all devices and verifies them. This architecture is inherently unscalable (i.e., requiring a reliable management network, incurring a long control path and making the server a single point of failure). In this paper, we tackle this scalability challenge of DPV from an architectural perspective. In particular, we circumvent the scalability bottleneck of centralized design and advocate for a distributed, on-device DPV framework. Our key insight is that DPV can be transformed into a counting problem on DAG, which can be naturally decomposed into lightweight tasks executed at network devices, enabling scalability. Evaluation shows that a prototype of this framework achieves scalable DPV under various settings, with little overhead on commodity network devices.

Data plane verification can be an important technique to reduce network disruptions, and researchers have recently made significant progress in achieving fast data plane verification. However, as we apply existing data plane verification techniques to large-scale networks, two problems appear due to extremes. First, existing techniques cannot handle too-fast arrivals, which we call update storms, when a large number of data plane updates must be processed in a short time. Second, existing techniques cannot handle well too-slow arrivals, which we call long-tail update arrivals, when the updates from a number of switches take a long time to arrive.

New trends in computer architecture include non-general purpose architectures, brain-inspired design, agile hardware development and environmentally responsible design. Computer Science students need to understand computer architecture to develop programs that can achieve high performance through a programmer's awareness of parallelism and latency. In a re-visit of curriculum guidelines, students could develop skills and competencies in less mature yet cutting-edge topics, including quantum computing. For example, should students understand and appreciate quantum processor design's components and characteristics? In a task to revise curricular guidelines, one faces the decision of which topics may be obsolete and should be dropped or modified. Equally important is deciding the set of topics that must be included. Such questions are hard to address, particularly for a curriculum intended to remain fresh under a 10-years horizon. For example, the danger of not properly promoting the discussion of quantum processor design in the new ACM/IEEE-CS/AAAI CS202X curricula is the long waiting cycle (15 years+) for any new revision. This BOF will foster the participants' interactions into a debate of whether new curricular guidelines should contain quantum processor design as part of the Architecture and Organization (AR) Knowledge Area.

The stable path problem (SPP) is a unified model for analyzing the convergence of distributed routing protocols (e.g., BGP), and a foundation for many network verification tools. Although substantial progress has been made on finding solutions (i.e., stable path assignments) for particular subclasses of SPP instances and analyzing the relation between properties of SPP instances and the convergence of corresponding routing policies, the non-trivial challenge of finding stable path assignments to generic SPP instances still remains. Tackling this challenge is important because it can enable multiple important, novel routing use cases. To fill this gap, in this paper we introduce a novel data structure called solvability digraph, which encodes key properties about stable path assignments in a compact graph representation. Thus SPP is equivalently transformed to the problem of finding in the solvability digraph a maximum independent set (MIS) of size equal to the number of autonomous systems (ASes) in the given SPP instance. We leverage this key finding to develop a heuristic polynomial algorithm GREEDYMIS that solves strictly more SPP instances than state-of-the-art heuristics. We apply GREEDYMIS to designing two important, novel use cases: (1) a centralized interdomain routing system that uses GREEDYMIS to compute paths for ASes and (2) a secure multi-party computation (SMPC) protocol that allows ASes to use GREEDYMIS collaboratively to compute paths without exposing their routing preferences. We demonstrate the benefits and efficiency of these use cases via evaluation using real-world datasets.

Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. In BoxOpt, we first model the simple reservation interface adopted in most reservation systems as a resource membership oracle. Second, we develop an efficient algorithm that constructs a resource separation oracle by a linear number of calls on resource membership oracle. Third, we develop a generic framework to construct a resource optimization oracle by iteratively calling the resource separation oracle, and then develop three novel, efficient algorithms under this generic framework, the best of which computes the optimal resource reservation by a linear number of calls on resource separation oracle. As such, BoxOpt can discover the optimal resource reservation with O(n2) calls on the resource membership oracle. We implement a prototype of BoxOpt with and demonstrate its efficiency and efficacy via extensive experiments using real network topology and a 7-day trace from a large operational federation network. Results show that (1) BoxOpt has a 100% correctness ratio by comparing with a state-of-the-art optimization solver, and (2) for 90% of requests, BoxOpt learns the optimal resource reservation within 10 seconds.

End-to-end route control spanning a set of networks can provide opportunities to both end users to optimize interdomain control and network service providers to increase business offering. BGP, the de facto interdomain routing protocol, provides no programmable control. Recent proposals for interdomain control, such as MIRO, ARROW and SDX, provide more mechanisms and interfaces, but they are only either point or incremental solutions. In this paper, we provide the first, systematic formulation of the software-defined internetworking (SDI) model, in which a network exposes a programmable interface to allow clients to define the interdomain routes of the network, just as a traditional SDN switch exposes Openflow or another programmable interface to allow clients to define its next hops, extending SDN from intra-domain control to generic interdomain control. Different from intradomain SDN, which allows complete client control, SDI should also maximize network autonomy, such as by allowing a network to maintain the control of its interdomain export policies, to avoid fundamental violations such as valley routing. We define the optimal end-to-end SDI routing problem and conduct rigorous analysis to show that the problem is NP-hard. We develop a blackbox optimization algorithm, which leverages Bayesian optimization theory and important properties of interdomain routing algebra, to sample end-to-end routes sequentially and find a near-optimal policy-compliant end-to-end route with a small number of sample routes. We implement a prototype of our optimization algorithm and validate its effectiveness via extensive experiments using real interdomain network topology. Results show that in an interdomain network with over 60000 ASes and over 320000 AS-level links, in 80% experiment cases, the blackbox optimization algorithm can find a near-optimal policy-compliant end-to-end route by sampling less than 33 routes.

With the fast proliferation of cloud computing, major cloud service providers, e.g., Amazon, Google, Facebook, etc., have been deploying more and more geographically distributed data centers to provide customers with better reliability and quality of services. A basic demand in such a geo-distributed data center system is to transfer bulk volumes of data from one data center to another. Geographic distribution and large delay-tolerance of such inter-data-center bulk data transfers provide cloud service providers opportunities to optimize the operating cost. Most existing studies on inter-data-center bulk data transfers focus on minimizing the network bandwidth cost. However, the energy-cost of the bulk data transfers, which also accounts for a large proportion of operating cost in the data centers, still remains unexplored. This is an important problem, especially in the multi-electricity-market environment, where the electricity price exhibits both spatial and temporal diversities. In this paper, we systematically study the problem of how to route and schedule inter-data-center bulk data transfers to minimize the energy-cost for geo-distributed data centers. We model this problem as a min-cost multi-commodity flow problem and develop an efficient two-stage optimization method to solve it. Extensive evaluations with real-life inter-data-center network and electricity prices show that our method brings significant energy-cost savings over existing bulk data transfer methods.

Vehicle localization service is a fundamental component of intelligent transportation systems. The widely used satellite navigation systems perform poorly in urban areas because the lines of sight to satellites are blocked by complex terrain characteristics, e.g., buildings, elevated streets and interchanges. In this paper, we design RadioLoc, a novel system achieving accurate, efficient, all-terrain vehicle localization with two key design points. First, RadioLoc harvests the frequency modulation (FM) signal, which has a higher availability than satellite signal in complex terrains, as the signal source for localization. Second, RadioLoc integrates modern machine learning techniques into the processing of FM signals to efficiently learn the accurate vehicle localization in all-terrain environments. We validate the feasibility of FM-based vehicle localization and corresponding challenges and practical issues via field tests (e.g., signal distortion, signal inconsistency and limited in-vehicle radio bandwidth), and develop a series of advanced techniques in RadioLoc to address them, including a new multipath delay spread filter, a reconstructive PCA denoiser, a tailored FM feature extractor, an adaptive batching technique and a frequency sweep technique. We implement a prototype of RadioLoc and perform extensive field experiments to evaluate its efficiency and efficacy. Results show that (1) RadioLoc achieves a real-time localization latency of less than 100 milliseconds; (2) RadioLoc achieves a worst-case localization accuracy of 99.6% even in an underground parking lot, and (3) the horizontal error of RadioLoc is only one sixth of a dedicated GPS device even when the vehicle is moving at a high-speed (i.e., 80 km/h) in a complex highway scenario.

Multi-domain network resource reservation systems are being deployed, driven by the demand and substantial benefits of providing predictable network resources. However, a major lack of existing systems is their coarse granularity, due to the participating networks' concern of revealing sensitive information, which can result in substantial inefficiencies. This paper presents Mercator, a novel multi-domain network resource discovery system to provide fine-grained, global network resource information, for collaborative sciences. The foundation of Mercator is a resource abstraction through algebraic-expression enumeration (i.e., linear inequalities/equations), as a compact representation of multiple properties of network resources (e.g., bandwidth, delay, and loss rate) in multi-domain networks. In addition, we develop an obfuscating protocol, to address the privacy concerns by ensuring that no participant can associate the algebraic expressions with the corresponding member networks. We also introduce a super-set projection technique to increase Mercator's scalability. We implement a prototype Mercator and deploy it in a small federation network. We also evaluate the performance of Mercator through extensive experiments using real topologies and traces. Results show that Mercator 1) efficiently discovers available networking resources in collaborative networks on average four orders of magnitude faster, and allows fairer allocations of network resources; 2) preserves the member networks' privacy with little overhead; and 3) scales to a collaborative network of 200 member networks.

Revealing an abstract view of the network is essential for the new paradigm of developing network-aware adaptive applications that can fully leverage the available computation and storage resources and achieve better business values. In this paper, we introduce ONV, a novel abstraction of flow-based on-demand network view. The ONV models network views as linear constraints on network-related variables in application-layer objective functions, and provides “equivalent” network views that allow applications to achieve the same optimal objectives as if they have the global information. We prove the lower bound for the number of links contained in an equivalent network view, and propose two algorithms to effectively calculate on-demand equivalent network views. We evaluate the efficacy and the efficiency of our algorithms extensively with real-world topologies. Evaluations demonstrate that the ONV can simplify the network up to 80% while maintaining an equivalent view of the network. Even for a large network with more than 25 000 links and a request containing 3000 flows, the result can be effectively computed in less than 1 min on a commodity server.

As many applications today migrate to distributed computing and cloud platforms, their user experience depends heavily on network performance. Software Defined Networking (SDN) makes it possible to obtain a global view of the network, introducing the new paradigm of developing adaptive applications with network views. A naive approach of realizing the paradigm, such as distributing the whole network view to applications, is not practical due to scalability and privacy concerns. Existing approaches providing network abstractions are limited to special cases, such as bottlenecks exist only at networks edges, resulting in potentially suboptimal or infeasible decisions. In this paper, we introduce a novel, on-demand network abstraction service that provides an abstract network view supporting not only accurate end-to-end QoS metrics, which satisfy the requirements of many peer-to-peer applications, but also multi-flow correlation, which is essential for bandwidth-sensitive applications containing many flows to conduct global network optimization. We prove that our abstract view is equivalent to the original network view, in the sense that applications can make the same optimal decision as with the complete information. Our evaluations demonstrate that the abstraction guarantees feasibility and optimality for network optimizations and protects the network service providers' privacy. Our evaluations also show that the service can be implemented efficiently; for example, for an extreme large network with 30,000 links and abstraction requests containing 3,000 flows, an abstract network view can be computed in less than one second.

The rise of cloud computing and the Internet not only bring change on the data center, but also lead to transformation in software development, deployment, operation and maintenance. With the continuous improvement of the current cloud computing and the internet environment, how to make better use of cloud computing platform, and how to serve the users is a popular field of computer software is a big challenge. In recent years, with the further development of concepts like micro-services and containers, software will further step forward to the Cloudware. This paper discusses how to deploy Cloudware in cloud environment, and proposes a new method to construct the PaaS platform which can directly deploy software on the cloud without any modification, while achieving a new model by the browser services. By using micro-service architecture, we achieving good performance of extension, scalable deployment, faults tolerance and flexible configuration. Finally, we evaluate this method by constructing a complete framework and carrying out an interactive delay experiment that directly focuses on users' experience, which also shows the effectiveness of this method.

Providing an interface for network applications to access network state, Software-Defined Networking (SDN) northbound API protocol is the foundation for the development of programmable networks with adaptive applications. However, with the growing network scale and applications' need for routing state at multi-domain level, feeding complete routing states to applications would jeopardize their scalability and network providers' privacy. Thus a good routing state abstraction is needed, which must be on-demand so that different applications can receive customized abstract state suiting their needs. Moreover, it must be minimal and equivalent, i.e., containing all the necessary information for applications to make decisions as the complete state does with no redundancy. Current routing state abstractions are not on-demand, and adopt extreme aggregation approaches (e.g., the big switch) to provide a minimal abstraction with the price of severe information loss. For instance, bottleneck links shared between flows are concealed, leading applications to make sub-optimal decisions. In this paper, we design ORSAP, the first on-demand routing state abstraction protocol, through which network applications can describe their demands while Internet service providers can provide the on-demand minimal equivalent routing state accordingly. ORSAP ensures applications' scalability, protects network providers' privacy, and significantly reduces the traffic to disseminate the information. Experiments show that with ORSAP and the abstraction engine we introduced in this paper, one can achieve a state abstraction ratio of up to 60% with an extremely low computation time even with large networks and complex application queries.

The connected vehicles have been considered as a remedy for modern traffic issues, potentially saving hundreds of thousands of lives every year worldwide. The Dedicated Short-Range Communications (DSRC) technology is an essential building block of this promising vision. DSRC faces volatile vehicular environments, where not only wireless propagation channels but also network topologies vary rapidly. Moreover, traffic congestions during rush hours may lead to an unprecedentedly high density of broadcasting radios, resulting in compromised reliability, efficiency and fairness of DSRC. In order to optimize the performance of DSRC, we develop a novel Online Control Approach of power and Rates (OnCAR). Supported by systematic control theories, OnCAR performs stably even in the dynamic and unpredictable vehicular environments. To the best of our knowledge, OnCAR is the first solution to address the strong coupling between communication variables. It adopts a multi-variable control model to synchronously adjust transmission power and data rates, which are two major variables determining the performance of DSRC. In addition, OnCAR leverages receiver-side measurements of performance metrics to strike a balance between overall performance and fairness. Compared with the state of the art, OnCAR enhances the overall reliability and efficiency of DSRC by 23.7% and 30.1%, respectively. Meanwhile, these numbers are achieved with a 40.1% improvement in fairness.

Software paradigm is a driving force for the evolution of software technology. With the continuous improvement in the current cloud computing and the Internet environment, software will develop further into Cloudware, which is emerging as a new software paradigm. This paper defines the concept of Cloudware, and discusses it in the context of software paradigm. Then, based on a loosely coupled von Neumann computing model, we propose a new method of constructing a Cloudware PaaS system which can directly deploy software into the cloud without any modification. By using micro-service architecture, we can achieve high performance, scalable deployment, faults tolerance and flexible configuration. Finally, we evaluate this method by carrying out an interactive delay experiment that directly focuses on users' experience, which shows the effectiveness of our method.

Video streaming has been dominating the mobile bandwidth, and is still expanding drastically. Its tremendous economic benefits have driven the automobile industry to equip vehicles with video streaming capacity. As a result, the new in-cabin Wi-Fi systems have been deployed, enabling each vehicle as a streaming hotspot on the wheels. A built-in Access Point (AP) bridges the communications between Wi-Fi devices inside and cellular networks outside. Distinct advantages offered by this system include a more powerful antenna array to improve multimedia quality, a constant energy source to power the streaming, etc. However, there exist two challenging features that may jeopardize the system performance. (1) The in-cabin Wi-Fi hotspots are mostly deployed on private vehicles, and thus are completely decentralized. (2) Video packets need to be delivered before their deadlines with small delays. Due to these features, existing algorithms may fail to efficiently schedule the in-cabin Wi-Fi video streaming. To fill the gap, we propose the Delay-awaRe dIstributed Video schedulING (DRIVING) framework. Being fully distributed and delay-aware, DRIVING not only increases the streaming goodput, but also reduces the delivery latency and deadline missing ratio. %In order to optimize this new framework, we establish cross-layer analytical models, which help us tune the framework parameters for better performance. In a typical scenario, DRIVING increases the goodput by up to 27.0%, while reducing the queueing delay and the deadline missing ratio by up to 40.0% and 38.4%, respectively.

The increasing market share of electric vehicles (EVs) makes charging facilities indispensable infrastructure for integrating EVs into the future intelligent transportation systems and smart grid. One promising facility called fast charging reservation(FCR) system was recently proposed. It allows people to reserve fast chargers ahead of time. In this system, fast chargers are the most scarce resource instead of electricity. Thus how to allocate these charging points requires careful designing. A good allocation policy should 1) ensure charging points to be allocated to EV users who really value them, and 2) prevent users' private information, e.g., identity, personal agenda, residing area and etc., from being inferred. A simple combination of classic multi-item auction and user identity anonymization cannot satisfy both criteria simultaneously. To find such an allocation, in this paper we investigate the design of privacy-preserving auctions in FCR systems. Traditional privacy-preserving strategies such as cryptography could incur high computation and communication overhead and hence jeopardize the efficiency of allocation. To this end, we propose Auc2Reserve, a differentially private randomized auction. Auc2Reserve applies an improved approximate sampler and the belief propagation (BP) technique to accelerate the resource allocation and pricing process. As a result, it is much more computationally efficient than generic exponential differentially private mechanisms and other theoretical approximate implementations. Through theoretical analysis, we show that Auc2Reserve is ?-incentive compatible, individual rational and ?-differentially private. And it provides a close-form approximation ratio in social welfare of FCR systems. In addition, we also demonstrate the efficacy of Auc2Reserve in terms of social welfare and privacy leakage via numerical simulation.

Large-scale charging stations become indispensable infrastructure to support the rapid proliferation of electric vehicles. Their operation modes have drawn great attention from both academia and industry. One promising mode called park-and-charge has been recently introduced. This new mode allows customers to park their electric vehicles at a parking lot, where the vehicles are charged during the parking time. Several small-scale experiments, such as the V-Charge project and General Motors' E-Motor plant, have demonstrated its potential. A key enabler for deploying this mode to large-scale stations is effective and efficient charging load scheduling methods. Most existing works confine to the time-driven scheduling policy due to their sole focus on the charging service. Applying their solutions to the park-and-charge mode would jeopardize the unitization of charging resource or cause frequent charging mode switching. This inapplicability motivates us to explore the feasibility and benefits of exploiting the event-driven scheduling policy in park-and-charge systems. Further, to better characterize charging load in this mode, we propose to adopt a metered model, by which a system gains value in proportion to the served charging demand. To be specific, the objective of this paper is to carry out both theoretical and experimental analysis for event-driven algorithms adapted to this metered model. We leverage both the competitive analysis and resource augmentation to demonstrate the non-constant and constant performance bounds for the earliest-deadline-first and highest-value-first algorithms respectively. Moreover, we provide a stronger theoretical result, i.e., the performance bound for the whole class of work-conserving scheduling algorithms. Through extensive simulations, we validate the proposed theoretical results and further provide interesting findings from the in-depth analysis of the simulation results.

Handling state dependencies is a major challenge in modern SDN programming, but existing frameworks do not provide sufficient abstractions nor tools to address this challenge. In this paper, we propose a novel, high-level programming abstraction and implement the *Function Automation SysTem (FAST)*. With the two key features, i.e., *automated state dependency tracking* and *efficient re-execution scheduling*, we demonstrate that FAST substantially simplifies state-dependent SDN programming and boosts the performance.

Vehicle-to-vehicle safety data dissemination plays an increasingly important role in ensuring the safety and efficiency of vehicle transportation. When collecting safety data, vehicles always prefer data generated at a closer location over data generated at a distant location, and prefer recent data over outdated data. However, these data preferences have been overlooked in most of existing safety data dissemination protocols, preventing vehicles getting more precise traffic information. In this paper, we explore the feasibility and benefits of incorporating the data preferences of vehicles in designing efficient safety data dissemination protocols. In particular, we propose the concept of packet-value to quantify these data preferences. We then design PVCast, a packet-value-based safety data dissemination protocol in VANET. PVCast makes the dissemination decision for each packet based on its packet-value and effective dissemination coverage in order to satisfy the data preferences of all the vehicles in the network. In addition, PVCast is lightweight and fully distributed. We evaluate the performance of PVCast on the ns-2 platform by comparing it with three representative data dissemination protocols. Simulation results in a typical highway scenario show that PVCast provides a significant improvement on per-vehicle throughput, per-packet dissemination coverage with small per-packet delay. Our findings demonstrate the importance and necessity of comprehensively considering the data preferences of vehicles when designing an efficient safety data dissemination protocol for VANET.

Network coding (NC) based opportunistic routing has been well studied, but the impact of routing diversity on the performance of NC-based routing remains largely unexplored. Towards understanding the importance of routing diversity in NC-based routing, we study the problems of estimating and minimizing the data delivery cost in NC-based routing. In particular, we propose an analytical framework for estimating the total number of packet transmissions for NC-based routing in arbitrary topologies. We design a greedy algorithm that minimizes the total transmission cost of NC-based routing and determines the corresponding forwarder set for each node. We prove the optimality of this algorithm and show that 1) nodes on the shortest path may not always be favored when selecting forwarders for NC-based routing and 2)the minimal cost of NC-based routing is upper-bounded by the cost of shortest path routing. Based on the greedy, optimal algorithm, we design and implement ONCR, a distributed minimal cost NC-based routing protocol. Using the NetEye sensor testbed, we comparatively study the performance of ONCR and existing approaches such as the single path routing protocol CTP and the NC-based opportunistic routing protocols MORE and CodeOR. Results show that ONCR achieves close to 100% delivery reliability while having the lowest delivery cost among all the protocols and 25-28% less than the second best protocol CTP. This low delivery cost also enables ONCR to achieve the highest network goodput, i.e., about two-fold improvement over MORE and CodeOR. Our findings demonstrate the significance of optimizing data forwarding diversity in NC-based routing for data delivery reliability, efficiency, and goodput.

Real-time routing is a basic element of closed-loop, real-time sensing and control, but it is challenging due to dynamic, uncertain link/path delays. The probabilistic nature of link/path delays makes the basic problem of computing the probabilistic distribution of path delays NP-hard, yet quantifying probabilistic path delays is a basic element of real-time routing and may well have to be executed by resource-constrained devices in a distributed manner; the highly-varying nature of link/path delays makes it necessary to adapt to in-situ delay conditions in real-time routing, but it has been observed that delay-based routing can lead to instability, estimation error, and low data delivery performance in general. To address these challenges, we propose the Multi-Timescale Estimation (MTE) method; by accurately estimating the mean and variance of per-packet transmission time and by adapting to fast-varying queueing in an accurate, agile manner, MTE enables accurate, agile, and efficient estimation of probabilistic path delay bounds in a distributed manner. Based on MTE, we propose the Multi-Timescale Adaptation (MTA) routing protocol; MTA integrates the stability of an ETX-based directed-acyclic-graph (DAG) with the agility of spatiotemporal data flow control within the DAG to ensure real-time data delivery in the presence of dynamics and uncertainties. We also address the challenges of implementing MTE and MTA in resource-constrained devices such as TelosB motes. We evaluate the performance of MTA using the NetEye and Indriya sensor network testbeds. We find that MTA significantly outperforms existing protocols, e.g., improving deadline success ratio by 89% and reducing transmission cost by a factor of 9.7.

As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Toward understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and reaggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.

As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Towards understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and re-aggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol \emph{tPack} that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.