Alerts are critical for detecting anomalies in large-scale cloud systems, ensuring reliability and user experience. However, current systems generate overwhelming volumes of alerts, degrading operational efficiency due to ineffective alert life-cycle management. This paper details the efforts of Company-X to optimize alert life-cycle management, addressing alert fatigue in cloud systems. We propose AlertGuardian, a framework collaborating large language models (LLMs) and lightweight graph models to optimize the alert life-cycle through three phases: Alert Denoise uses graph learning model with virtual noise to filter noise, Alert Summary employs Retrieval Augmented Generation (RAG) with LLMs to create actionable summary, and Alert Rule Refinement leverages multi-agent iterative feedbacks to improve alert rule quality. Evaluated on four real-world datasets from Company-X’s services, AlertGuardian significantly mitigates alert fatigue (94.8% alert reduction ratios) and accelerates fault diagnosis (90.5% diagnosis accuracy). Moreover, AlertGuardian improves 1,174 alert rules, with 375 accepted by SREs (32% acceptance rate). Finally, we share success stories and lessons learned about alert life-cycle management after the deployment of AlertGuardian in Company-X.

Recently, the limited capacity of the Urban Rail Transit (URT) has failed to meet passenger travel demands, especially in peak hours, which leads to crowded stations and oversaturated metro networks. Considering the diverse scenarios of the multimodal public transportation network and the need for rapid generation of strategies, this study proposes a deep reinforcement learning (RL) algorithm that guides passenger route selection in the multimodal transit network to operate better. The proposed RL algorithm, consisting of graph representation learning, convolution neural network, and self-attention mechanism, is used to generate the route guidance strategy for passengers, aiming to alleviate the congestion of the multimodal transit network, improve passengers’ travel experience, and reduce CO$_{2}$emissions. Based on the multimodal transit network in Beijing, the simulation results demonstrate that the RL algorithm can well perceive the states and generate adaptive route guidance strategies that can decrease the section load rates, improve network-wide passengers’ travel experience, and reduce CO$_{2}$emissions. Even in cases where passengers are not fully compliant with the route guidance, the proposed algorithm remains effective.

We present PerfTrace, an end-to-end tool for efficient, real-time, and multi-metric network performance monitoring. PerfTrace provides a high integration of different existing measurement functions, supporting the measurement of essential metrics such as latency, jitter, packet loss, and available bandwidth. More importantly, innovative schemes and algorithms are proposed to address the weaknesses of existing tools.After conducting comprehensive evaluations, we find that (i) PerfTrace measures one-way and two-way latency, jitter, and packet loss ∼9.4× faster and ∼3.6× more data-efficiently; (ii) PerfTrace measures available bandwidth in our testbed with minimal mean relative error (5.22%), outperforming all the tools compared (ranging from 8.17% to 37.24%). Meanwhile, PerfTrace consumes a more constant percentage of bandwidth resources than other tools when monitoring available bandwidth. PerfTrace’s data overhead is always only about 1/600 of the total bandwidth for a measurement frequency once per minute.

One-way delay (OWD) is one of the essential network performance metrics. In large-scale resilient overlay networks (RONs), OWD measurements can be used for shortest path selection and troubleshooting. However, OWD measurements remain difficult because of the need for precise time synchronization. Especially in large-scale networks, clock synchronization of all nodes has always been a considerable challenge. Therefore, in many cases, people use half of the round-trip time (RTT/2) as a rough substitute for the OWD. This paper presents an efficient and easy-to-deploy scheme for large-scale OWD measurements with the algorithm ClockConverger at its core. The scheme consists of three steps: Firstly, we perform low-precision time synchronization for all the measured nodes relying on network time protocol daemons (ntpd); Then, we use the open-source tool OWPing to perform OWD measurements; Finally, we correct the errors of the measured OWDs with our proposed ClockConverger. The theory and experiments show that our scheme's accuracy is significantly better than RTT/2. Meanwhile, the complexity of ClockConverger is$O(n^{2})$, which is much lower than the exponential complexity of the existing Maximum-Entropy algorithm.

In global communications, severe one-way delay (OWD) asymmetry often occurs. Due to the difficulties of OWD measurement (need to control both ends and synchronize their clocks), now RTT/2 is commonly used to estimate OWD. However, OWD asymmetry can lead to large errors in the halving RTT method, which in turn affects the end-to-end quality of service (QoS) guarantees. In this paper, we investigate OWD asymmetry through large-scale OWD measurements on a global scale. The measurements show that more than 11% of network paths have OWDs with a relative difference of more than 10% compared to RTT/2. By analyzing the measurement results in depth, we try to explain why the delay asymmetry occurs. We find that 67% is caused by hop inflation or a significant increase in propagation distance, and 33% is caused by variable queuing delays. We also find AS-level paths between node pairs with significant delay asymmetry are much more likely (~ 10 ×) to violate the well-known valley-free rule.

Abstract Just‐in‐time defect prediction can remind software developers and managers to verify and fix bugs at the moment they appeared, thus improving the effectiveness and validity of bug fixing. Existing studies mainly focus on just‐in‐time prediction for software files (JIT‐F). JIT‐F is a binary classification problem, which classifies (hence predicts) a file change as buggy or clean. This article provides a detailed analysis of just‐in‐time defect prediction for software hunks (JIT‐H), which predicts bugs at a finer level of granularity, and hence further improves the efficiency of bug fixing. Classification is performed using the ensemble technique of bagging—aggregated combinations of random under sampling plus multiple classifiers (J48 and Random Forest). An empirical study with 10 open source projects was conducted to validate the effectiveness of JIT‐H. Experimental results show that JIT‐H is effective at predicting defects in software hunk changes. Compared with JIT‐F, JIT‐H is more cost effective. Additionally, analysis on the change features indicates that Text Vector features and hunk change level features are of more importance than features in other groups and levels.

Fast IPv4 scanning significantly improves network measurement and security research. Nevertheless, it is infeasible to perform brute-force scanning of the IPv6 address space. Alternatively, one can find active IPv6 addresses through scanning the candidate addresses generated by state-of-the-art algorithms. However, the probing efficiency of such algorithms is often very low. In this paper, our objective is to improve the probing efficiency of IPv6 addresses. We first perform a longitudinal active measurement study and build a high-quality dataset, hitlist, including more than 1.95B IPv6 addresses distributed in 58.2K BGP prefixes and collected over 17 months period. Different from the previous works, we probe the announced BGP prefixes using a pattern-based algorithm. This results in a dataset without uneven address distribution and low active rates. Further, we propose an efficient address generation algorithm, DET, which builds a density space tree to learn high-density address regions of the seed addresses with linear time complexity and improves the active addresses’ probing efficiency. We then compare our algorithm DET against state-of-the-art algorithms on the public hitlist and our hitlist by scanning 50M addresses. Our analysis shows that DET increases the de-aliased active address ratio and active address (including aliased addresses) ratio by 10%, and 14%, respectively. Furthermore, we develop a fingerprint-based method to detect aliased prefixes. The proposed method for the first time directly verifies whether the prefix is aliased or not. Our method finds that 10.64% of the public aliased prefixes are false positive.

Social Network Analysis (SNA) is a powerful tool for analyzing social phenomena that is based on studying how actors are connected or interact with each other. All Social Networks (SNs) are inherently embedded in particular cultures. However, the effect of cultural influence is often missing from SNA techniques. Moreover, to incorporate culture, modeling approaches have to deal with inaccurate, unrealistic, and incomplete cultural data. In order to address this problem, we propose a generic approach to systematically represent culture in the form of relevant factors and relationships, while leveraging relevant social theories, and to infuse them into SNs in order to obtain more realistic and complete analyses. Using two sets of experiments, we validate the effectiveness of our approach and demonstrate the significant advantages obtained through culturally infused SNA.

Social network analysis (SNA) is a set of broadly used techniques designed for analyzing structural information contained in interactions. However, current SNA tools have a poor ability to handle large and dynamic social networks. One particular problem of interest is that of maximal clique enumeration used for studying modularity/community. Critical challenges for this problem include limited scalability and poor ability for handling dynamism. In this paper, we design and implement an anytime anywhere approach for maximal clique enumeration problem in SNA. Through a set of experiments on random graphs, we validate and demonstrate the effectiveness and efficiency of our approach.

With the broad application of electronic communication monitoring tools and data-sharing techniques, the size of networks to be studied by Social Network Analysis (SNA) has grown rapidly. However, current SNA techniques are not particularly scalable. For example, even centrality, which is one of the most frequently used SNA parameters, cannot be measured by most current SNA software when the network is large. This paper presents the design of an effective and scalable anytime anywhere parallel methodology for SNA with large-scale networks emphasizing centrality measurement algorithms. The efficiency and effectiveness of the methodology is validated by experiments of centrality analysis for large networks.

Robots are developed to perform myriad kinds of complex tasks. To enhance the system performance, tasks should be optimally distributed among robots or subsystems of a single robot. Applied to mobile manipulators, optimal task distribution can be obtained by implementing an optimal redundancy resolution approach. A lot of work has been done in this area with a priori-specified tasks. However, for tele-operated mobile manipulator systems, tasks are generated on-line by the operator, and task requirements vary significantly and are not known a priori. This renders static task distribution schemes unsuitable for achieving optimal performance and mandates the use of online modification of optimal task distribution algorithms. This paper proposes a new optimal task distribution method for tele-operated mobile manipulators. In this method, task dexterity indices, which describe the task requirements are generated online. Based on these indices, the criterion function for optimal performance is constructed using physical programming. The solution obtained by this algorithm is more suitable for varying tasks, and has a better defined physical meaning. The effectiveness of the proposed method is verified by experimental results.