@inproceedings{ding2014mining,
author = {Ding, Rui and Fu, Qiang and 林庆维, Qingwei Lin and Zhang, Dongmei},
title = {Mining Historical Issue Repositories to Heal Large-Scale Online Service Systems},
booktitle = {DSN},
year = {2014},
month = {July},
abstract = {Online service systems have been increasingly popular and important nowadays. Reducing the MTTR (Mean Time to Restore) of a service remains one of the most important steps to assure the user-perceived availability of the service. To reduce the MTTR, a common practice is to restore the service by identifying and applying an appropriate healing action. In this paper, we present an automated mining-based approach for suggesting an appropriate healing action for a given new issue. Our approach suggests an appropriate healing action by adapting healing actions from the retrieved similar historical issues. We have applied our approach to a real-world and large-scale product online service. The studies on 243 real issues of the service show that our approach can effectively suggest appropriate healing actions (with 87% accuracy) to reduce the MTTR of the service. In addition, according to issue characteristics, we further study and categorize issues where automatic healing suggestion faces difficulties.},
publisher = {DSN 2014},
url = {http://approjects.co.za/?big=en-us/research/publication/mining-historical-issue-repositories-heal-large-scale-online-service-systems-2/},
}