Name
Title | ||
|---|---|---|
Efficient incident identification from multi-dimensional issue reports via meta-heuristic search |
Abstract | ||
|---|---|---|
In large-scale cloud systems, unplanned service interruptions and outages may cause severe degradation of service availability. Such incidents can occur in a bursty manner, which will deteriorate user satisfaction. Identifying incidents rapidly and accurately is critical to the operation and maintenance of a cloud system. In industrial practice, incidents are typically detected through analyzing the issue reports, which are generated over time by monitoring cloud services. Identifying incidents in a large number of issue reports is quite challenging. An issue report is typically multi-dimensional: it has many categorical attributes. It is difficult to identify a specific attribute combination that indicates an incident. Existing methods generally rely on pruning-based search, which is time-consuming given high-dimensional data, thus not practical to incident detection in large-scale cloud systems. In this paper, we propose MID (Multi-dimensional Incident Detection), a novel framework for identifying incidents from large-amount, multi-dimensional issue reports effectively and efficiently. Key to the MID design is encoding the problem into a combinatorial optimization problem. Then a specific-tailored meta-heuristic search method is designed, which can rapidly identify attribute combinations that indicate incidents. We evaluate MID with extensive experiments using both synthetic data and real-world data collected from a large-scale production cloud system. The experimental results show that MID significantly outperforms the current state-of-the-art methods in terms of effectiveness and efficiency. Additionally, MID has been successfully applied to Microsoft's cloud systems and helped greatly reduce manual maintenance effort.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3368089.3409741 | ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Virtual Event
USA
November, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7043-1 | 1 |
PageRank | References | Authors |
0.41 | 29 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiazhen Gu | 1 | 1 | 1.09 |
| Chuan Luo | 2 | 496 | 41.38 |
| Si Qin | 3 | 3 | 4.16 |
| Bo Qiao | 4 | 33 | 9.09 |
| Qingwei Lin | 5 | 285 | 27.76 |
| Hongyu Zhang | 6 | 864 | 50.03 |
| Ze Li | 7 | 184 | 20.82 |
| Yingnong Dang | 8 | 537 | 26.92 |
| Shaowei Cai | 9 | 402 | 36.58 |
| Wei Wu | 10 | 2 | 1.10 |
| Yangfan Zhou | 11 | 232 | 29.72 |
| Murali Chintalapati | 12 | 33 | 3.40 |
| Dongmei Zhang | 13 | 1439 | 132.94 |