一句话：提出 iPACK，融合客户工单与云端事件的故障信息，实现云系统重复工单的精准聚合。
在云系统中，事故（incident）会直接影响客户满意度与业务收入，客户通常通过提交支持工单来寻求客服支持。由于工单以分布式、无协调的方式提交，其中存在大量重复工单，因此聚合重复工单对高效工单管理至关重要。已有研究主要依赖工单文本相似度来检测重复，但云系统复杂的服务依赖关系会导致重复工单的文本描述在语义上差异很大，从而降低检测效果。针对这一问题，作者提出 iPACK，一种事件感知（incident-aware）的重复工单聚合方法，它融合客户侧（工单）与云侧（事件）两方面的故障信息进行判定。作者在大规模云平台 Azure 生产环境采集的三个数据集上进行了广泛评估，实验结果表明 iPACK 能够精准且全面地聚合重复工单，F1 分数达到 0.871~0.935，较当前最先进方法提升 12.4%~31.2%。
In cloud systems, incidents are potential threats to customer satisfaction and business revenue. When customers are affected by incidents, they often request customer support service (CSS) from the cloud provider by submitting a support ticket. Many tickets could be duplicate as they are reported in a distributed and uncoordinated manner. Thus, aggregating such duplicate tickets is essential for efficient ticket management. Previous studies mainly rely on tickets' textual similarity to detect duplication; however, duplicate tickets in a cloud system could carry semantically different descriptions due to the complex service dependency of the cloud system. To tackle this problem, we propose iPACK, an incident-aware method for aggregating duplicate tickets by fusing the failure information between the customer side (i.e., tickets) and the cloud side (i.e., incidents). We extensively evaluate iPACK on three datasets collected from the production environment of a large-scale cloud platform, Azure. The experimental results show that iPACK can precisely and comprehensively aggregate duplicate tickets, achieving an F1 score of 0.871~0.935 and outperforming state-of-the-art methods by 12.4%~31.2%.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2302-09520,
title = {Incident-aware Duplicate Ticket Aggregation for Cloud Systems},
author = {Jinyang Liu and Shilin He and Zhuangbin Chen and Liqun Li and Yu Kang and Xu Zhang and Pinjia He and Hongyu Zhang and Qingwei Lin and Zhangwei Xu and Saravan Rajmohan and Dongmei Zhang and Michael R. Lyu},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00193}
}