The Domain Name System (DNS) plays a crucial role in modern web applications; however, manipulations such as hijacking, tampering, and censorship can disrupt domain resolution, posing significant privacy and security risks. While such manipulations are prevalent across global DNS infrastructures, their scope and mechanisms remain poorly understood. Existing studies focus on country-level censorship or rely on authoritative data and passive traffic from selected domains, which prevents a comprehensive understanding. Moreover, the dynamic nature of modern DNS resolution, in which a single domain may resolve to thousands of edge servers, further complicates the detection of manipulated responses.

There are over a million open DNS servers in the wild. However, not all servers perform recursive queries directly. Instead, many DNS forwarders forward queries to upstream recursive servers or other DNS forwarders for name resolving on their behalf. The groups of open servers that have such dependencies on each other form ODNS Clusters. The dependencies can result in vulnerabilities; yet we have little knowledge of the ODNS cluster structure. In this work, we measure the inter-dependence of open DNS resolvers and find that 1.9 million open DNS servers form only 81,636 ODNS clusters. We further analyze the characteristics of the clustered ODNS structure. The key observations include biased cluster size distribution, discrepancy of ODNS infrastructures among countries, concentration in major public DNS server providers, and potential security and resilience risks due to the dependence.

Network-wide heavy hitter detection is usually performed by sampling on several network measurement points (NMPs) and merging the measurement results in the centralized controller to get a network-wide view. However, a packet may pass several NMPs and be counted multiple times when measurement results are merged, which causes the double-counting problem and leads to incorrect detection. Existing studies either overlook this problem or require significant memory usage. This paper proposes SAROS, a self-adaptive routing oblivious sampling method for accurate network-wide heavy hitter detection. Specifically, SAROS exploits a sampling mechanism in the data plane, where the sampling threshold on each measurement point is predicted and adaptively set by the control plane. Such guidance from the control plane greatly reduces the memory usage in the data plane, while mitigating the double-counting problem. Experimental results show that, compared with existing solutions, SAROS improves the F1-Score of heavy hitter detection by 10 ∼ 40%.

Distributed machine learning (DML) has recently experienced widespread application. A major performance bottleneck is the costly communication for gradients synchronization. Recently, researchers have explored the use of programmable switches for in-network synchronous aggregation of gradients to mitigate the communication overhead. Nevertheless, the performance of in-network synchronous aggregation is significantly impacted by the stragglers. Unfortunately, the schedulers in existing DML systems are no longer effective in dealing with stragglers because of the ignorance of the aggregation progress that is offloaded from the parameter servers to the programmable switches. To address this gap, this paper presents VAKY, an adaptive scheduler specifically designed for in-network aggregation. At the heart of VAKY is the variable K-block sync method, where the aggregators stop waiting for updates from more workers once having received updates from the fastest K workers for each block of gradients. We propose an efficient solution that can dynamically choose the optimal values of K during the training process, in order to minimize the expected training completion time. We have integrated VAKY into PyTorch, and our experiments show that compared to the state-of-the-art in-network aggregation systems, VAKY improves the aggregation throughput by up to $40 \%$ and reduces the training time by $25 \%$.

Large cloud service providers have built an increasing number of geo-distributed data centers (DCs) connected by Wide Area Networks (WANs). These DC-WANs carry both high-priority traffic from interactive services and low-priority traffic from bulk transfers. Given that a DC-WAN is an expensive resource, providers often manage it via traffic engineering algorithms that rely on accurate predictions of inter-DC high-priority (delay-sensitive) traffic. In this article, we perform a large-scale measurement study of high-priority inter-DC traffic from Baidu. We measure how inter-DC traffic varies across their global DC-WAN and show that most existing traffic prediction methods either cannot capture the complex traffic dynamics or overlook traffic interrelations among DCs. Building on our measurements, we propose theInterrelated-TemporalGraph ConvolutionalNetwork(IntegNet) model for inter-DC traffic prediction. In contrast to prior efforts, our model exploits both temporal traffic patterns and inferred co-dependencies between DC pairs. IntegNet forecasts the capacity needed for high-priority traffic demands by accounting for the balance between resource provisioning (i.e., allocating resources exceeding actual demand) and QoS losses (i.e., allocating fewer resources than actual demand). Our experiments show that IntegNet can keep a very limited QoS loss, while also reducing overprovisioning by up to 42.1% compared to the state-of-the-art and up to 66.2% compared to the traditional method used in DC-WAN traffic engineering.

Large cloud service providers have built an increasing number of geo-distributed data centers (DCs) connected by WAN to host their diverse services. While we have seen a large body of work on traffic engineering of WAN, the WAN traffic characteristics of production DC networks remain not well understood. In this paper, we report on the network traffic observed in Baidu's DC network (DCN) that consists of tens of geo-distributed DCs. Baidu hosts both traditional services like Web and Computing, as well as emerging services, such as Analytics, AI, and Map. We analyze WAN traffic characteristics in Baidu's DCN from the perspectives of traffic demands, traffic communication among DCs, and traffic characteristics of diverse services. Specifically, we focus on the disparity that might exist among different types of services. We also discuss the implications of our findings for WAN traffic engineering, fabric design, and service deployment.

The tremendous development of smart devices and mobile network services has stimulated the popularity of mobile apps in recent years. Meanwhile, large amounts of traffic data are generated when users access mobile apps in cellular networks, which provides much information to characterize user behaviors. Based on such big mobile data collected from a large 4G cellular network, this paper takes an in-depth look at diverse smartphone users' mobility patterns. Our dataset tracks millions of users over tens of thousands of base stations. We first utilize three mobility metrics based on both moving range and moving frequency to characterize user mobility. We then provide a deep insight into the relationships between user mobility and user behaviors in terms of traffic usage, app usage interests, and app usage uniqueness. Finally, we discuss the implications of our findings to various stakeholders in mobile networks.

Name lookup is a core function of the Named Data Networking (NDN) forwarding plane. It performs a name-based longest prefix match lookup against a large amount of variable-length, hierarchical name prefixes. NDN name lookup suffers a scalability challenge and needs to satisfy three key requirements: high-speed lookups, low memory cost, and fast updates. However, no existing work satisfies these requirements. Hash-based linear search schemes achieve fast updates and low memory cost but not high-speed lookups. Binary search (BS) schemes achieve high-speed lookups but slow updates with high memory cost. In this paper, we propose CBS, a hash-based counting binary search scheme for scalable high-speed NDN name lookup. CBS achieves fast updates and low memory cost, while sustaining high-speed lookups. The key to CBS is using a counter for each slot in a hash table to keep track of the number of markers that direct a search to find longer matching prefixes. This design not only allows fast updates by incrementing and decrementing counters, but also reduces the additional memory cost. Our experimental results demonstrate that CBS outperforms BS in update throughput, memory cost, and lookup throughput.

Name prefix lookup is a core function in Named Data Networking (NDN). It is challenging to perform high-speed name-based longest prefix match lookups against a large amount of variable-length, hierarchical name prefixes in NDN. However, prior work concentrates on software-based name prefix lookup, and can't satisfy the scalability demands of high-speed lookups, low memory cost, and fast incremental updates. In this paper, we propose a hybrid approach to scalable name prefix lookup with hardware and software. We propose SACS, a shape and content search framework with ternary content addressable memories (TCAMs) and static random memory access memories (SRAMs). SACS aims to achieve high-speed lookups and low memory cost, while sustaining fast incremental updates. In SACS, a TCAM-based shape search module is first used to determine a subset of possible matching prefixes, and then a SRM-based content search module is used on the subset to find the longest matching prefix. For SACS, we propose a first shrinking least load algorithm to pack large amounts of shapes of name prefixes in a small TCAM. A shape of a name prefix is a sequence of its component lengths. We also propose a dual fingerprint-based hash table to improve the content search performance in SRAMs. Experimental results demonstrate that SACS outperforms state-of-the-art schemes by achieving up to 2.4X higher lookup throughput, up to 53% lower memory cost, and up to 96% higher insert throughput.

Crack evaluation is a vital component in the pavement condition survey. However, researchers have had difficulty automatically mimicking the in-field evaluation practice to achieve a fully automated pavement crack evaluation due to the diversity of real-world crack patterns and the complexity of crack definitions in an agency's survey protocol. The multi-scale crack analysis model based on a Crack Fundamental Element is developed to topologically represent complex crack patterns and provide systematic crack properties to support crack classification. Utilizing this model, this study implements an automatic crack classification and quantification method for use on the state highways in Georgia, United States. Both image-based validation and field validation are conducted with the pavement experts from the Georgia Department of Transportation (GDOT) on State Route 236 in Atlanta, and the proposed method achieves a classification accuracy of 92.2% for load cracking and 98.1% for B/T cracking.

Combining with the characteristic of hazardous waste, this paper develops a multi-objective mathematic model for the location of treatment sites and transfer sites for hazardous wastes. Based on the fuzzy satisfactory levels of objectives, it proposes a two-phase fuzzy algorithm. Through solving the model, it conducts an analysis on the locations and numbers of these sites and how to assign the generation sites to transfer sites. Therefore, a reverse network for hazardous waste is constructed Finally, it takes Tianjin Economic-technological Develop Area (TEDA) in Tianjin city in China as a case to prove the availability of the fuzzy model.

Automatic target recognition (ATR) based on synthetic aperture radar (SAR) imagery (denoted as SAR ATR for simplicity) is very important for battlefield awareness. Since SAR images are very sensitive to pose variation of targets, SAR ATR is a well-known very challenging problem. An efficient SAR ATR algorithm is given, which uses KFD (kernel Fisher discriminant) as feature extractor and linear SVM (support vector machine) as classifier. Experimental results evaluated with the MSTAR (moving and stationary target automatic recognition) public data sets provided by the DARPA/AFRL (Defence Advanced Research Project Agency/Air Force Research Laboratory) show that the proposed scheme performs much better than the conventional template matching and SVM methods, especially when the target pose uncertainty is large, which is desirable for SAR ATR.