较低的网络服务响应时间对提升用户体验至关重要.以搜索引擎这一典型的网络服务场景为例,服务提供商应确保网络服务(搜索)响应时间在 1 s以内.在实践中,服务响应时间会受到用户浏览器、运营商、页面加载方式等诸多服务属性的影响.为了进行针对性的优化,服务提供商需要找出使服务响应时间过长的规则,即一些属性的组合.然而现有研究工作遇到了3方面挑战:1)搜索日志数据量大;2)搜索日志数据分布不平衡;3)要求泛化度高的规则.因此设计了Miner(multi-dimensional extraction of rules),一种新型服务响应时间异常诊断框架.Miner使用自步采样机制应对第 1个挑战和第 2个挑战.针对第 3个挑战,Miner使用Corels算法挖掘出泛化率高且召回率高的规则.使用 2家国内顶级搜索引擎服务提供商的响应时间日志数据评估了Miner性能,结果显示Miner的泛化率和召回率均高于现有方法,并证明了Miner挖掘出的规则可被运维人员采纳并做针对性的优化.
Abstract
The timely response of network services is crucial to improving user experience.Taking search engine as a typical example of network services,service providers need to ensure that the search response time is within one second.In practice,the search response time can be affected by many service attributes,such as user browsers,ISPs,and page loading methods.To optimize effectively,service providers need to identify the rules that cause high search response time,which are combinations of the above attributes.However,existing work encounters three challenges.First,the amount of search logs is large.Second,the search logs are unevenly distributed.Third,the rules with high generality are needed.Therefore,we propose a framework called Miner(multi-dimensional extraction of rules).Miner takes advantage of self-paced sampling to overcome the first and second challenges.To address the third challenge,Miner employs Corels to generate rules with high generality and recall.Our experiments use search logs provided from two top-tier search engine companies in China.The results show that Miner outperforms the state-of-the-art methods in terms of generality and recall.Operators adopt rules generated by Miner and optimize the performance of the search engine.
关键词
网络服务质量/自步采样/搜索引擎/搜索响应时间/数据挖掘
Key words
quality of networking service/self-paced sampling/search engine/search response time/data mining
维普
万方数据