124 citations · 224 across the 96 of their papers we have counts for
15 papers · 1 filter
Xpert: Empowering Incident Management with Query Recommendations via Large Language Models
Yuxuan Jiang, Chaoyun Zhang, Shilin He +8
Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect us…
Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective
Lukas Wutschitz, Boris Köpf, Andrew Paverd +6
Modern machine learning systems use models trained on ever-growing corpora. Typically, metadata such as ownership, access control, or licensing information is ignored during traini…
TaskWeaver: A Code-First Agent Framework
Bo Qiao, Liqun Li, Xu Zhang +16
Large Language Models (LLMs) have shown impressive abilities in natural language understanding and generation, leading to their widespread use in applications such as chatbots and…
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
Ruomeng Ding, Chaoyun Zhang, Lu Wang +7
Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as "th…
TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems
Ruomeng Ding, Chaoyun Zhang, Lu Wang +11
Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be chall…
PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis
Dylan Zhang, Xuchao Zhang, Chetan Bansal +3
Major cloud providers have employed advanced AI-based solutions like large language models to aid humans in identifying the root causes of cloud incidents. Despite the growing prev…
Diffusion-based Time Series Data Imputation for Microsoft 365
Fangkai Yang, Wenjie Yin, Lu Wang +10
Reliability is extremely important for large-scale cloud systems like Microsoft 365. Cloud failures such as disk failure, node failure, etc. threaten service reliability, resulting…
Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction
Menglin Xia, Xuchao Zhang, Camille Couturier +3
Large language models (LLMs) enhanced with retrieval augmentation has shown great performance in many applications. However, the computational demands for these models pose a chall…
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
Zhangchi Zhu, Lu Wang, Pu Zhao +7
Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU lea…
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
Yuhang Chen, Chaoyun Zhang, Minghua Ma +7
Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accuratel…
Assess and Summarize: Improve Outage Understanding with Large Language Models
Pengxiang Jin, Shenglin Zhang, Minghua Ma +13
Cloud systems have become increasingly popular in recent years due to their flexibility and scalability. Each time cloud computing applications and services hosted on the cloud are…
Introspective Tips: Large Language Model for In-Context Decision Making
Liting Chen, Lu Wang, Hang Dong +9
The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks. In this study, we em…
Automatic Root Cause Analysis via Large Language Models for Cloud Incidents
Yinfang Chen, Huaibing Xie, Minghua Ma +15
Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual inve…
Incident-aware Duplicate Ticket Aggregation for Cloud Systems
Jinyang Liu, Shilin He, Zhuangbin Chen +10
In cloud systems, incidents are potential threats to customer satisfaction and business revenue. When customers are affected by incidents, they often request customer support servi…
Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models
Toufique Ahmed, Supriyo Ghosh, Chetan Bansal +3
Incident management for cloud services is a complex process involving several steps and has a huge impact on both service health and developer productivity. On-call engineers requi…