1. 毕业设计（论文）主要内容：

随着互联网特别是移动互联网的高速发展，web服务已经深入到社会的各个领域，人们使用互联网搜索、购物、付款和娱乐等。

因此，保障web服务的稳定已经变的越来越重要。

web服务的稳定性主要靠运维来保障，运维人员实时监控各种各样的运维数据。

2. 毕业设计（论文）主要任务及要求

主要任务要求：
1.查阅15篇相关文献（含2篇外文），并每篇书写200—300字文献摘要（装订成册，带封面）；
2.认真填写周记，完成800字开题报告；
3.完成5000中文字以上的相关英文专业文献翻译，并装订成册（中英文一起，带封面）；
4.完成系统的编码与调试；
5.完成10000字以上的毕业论文；
6.进行论文答辩。

3. 毕业设计（论文）完成任务的计划与安排

毕业设计时间节点：
(1)2018/1/14—2018/2/22：确定选题，查阅文献，外文翻译和撰写开题报告；
(2)2018/2/23—2018/4/30：系统架构、程序设计与开发、系统测试与完善；
(3)2018/5/1—2018/5/25：撰写及修改毕业论文；
(4)2018/5/26—2018/6/6：准备答辩。

4. 主要参考文献

[1] Time Series Decomposition: Yingying Chen, Ratul Mahajan, Baskar Sridharan, and Zhi-Li Zhang. A provider-side view of web search response time. In Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM, pages 243–254. ACM, 2013.
[2] Holtwinters: He Yan, Ashley Flavel, Zihui Ge, Alexandre Gerber, Daniel Massey, Christos Papadopoulos, Hiren Shah, and Jennifer Yates. Argus: End-to-end service anomaly detection and localization from an isp’s point of view. In INFOCOM, 2012 Proceedings IEEE, pages 2756–2760. IEEE, 2012.
[3] 静态阈值: Amazon cloudwatch alarm.
http://docs.aws.amazon.com/AmazonCloudWatch/latest/DeveloperGuide/ConsoleAlarms.html.
[4] Moving Average: David R. Choffnes, Fabián E. Bustamante, and Zihui Ge. Crowdsourcing service-level network event monitoring. In Proceedings of the ACM SIGCOMM 2010 Conf.
[5] Weighted Moving Average: Balachander Krishnamurthy, Subhabrata Sen, Yin Zhang, and Yan Chen. Sketch-based change detection: methods, evaluation, and applications. In Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement, pages 234–247. ACM, 2003.
[6] Exponentially Weighted Moving Average: Balachander Krishnamurthy, Subhabrata Sen, Yin Zhang, and Yan Chen. Sketch-based change detection: methods, evaluation, and applications. In Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement, pages 234–247. ACM, 2003.
[7] ARIMA: Yin Zhang, Zihui Ge, Albert Greenberg, and Matthew Roughan. Network anomography. In Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, IMC’05, pages 30–30, Berkeley, CA, USA, 2005. USENIX Association.
[8] Extreme Value Theory: Siffer A, Fouque P A, Termier A, et al. Anomaly Detection in Streams with Extreme Value Theory[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017: 1067-1075.
[9] Wavelet: Paul Barford, Jeffery Kline, David Plonka, and Amos Ron. A signal analysis of network traffic anomalies. In Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment, pages 71–82. ACM, 2002.
[10] Fernando Silveira, Christophe Diot, Nina Taft, and Ramesh Govindan. Astute: Detecting a different class of traffic anomalies. In Proceedings of the ACM SIGCOMM 2010 Conference, SIGCOMM ’10, pages 267–278. ACM, 2010.
[11] Anukool Lakhina, Mark Crovella, and Christophe Diot. Mining anomalies using traffic feature distributions. In Proceedings of the 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM ’05, pages 217–228. ACM, 2005.
[12] Anukool Lakhina, Mark Crovella, and Christophe Diot. Diagnosing network-wide traffic anomalies. In Proceedings of the 2004 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM ’04, pages 219–230. ACM, 2004