QWatch: Detecting and Locating QoE anomaly for VoD in the Cloud

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Wang, C (Author) Other; Kim, H (Author) Other; Ricardo Morla (Author) FEUP

Conference proceedings International

Title: 2016 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, December 12-15, 2016

Pages: 126-133

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00M-DEB

DOI: 10.1109/cloudcom.2016.31

Abstract (EN): Commercial large-scale VoD systems such as Netflix and Hulu rely on CDNs to deliver videos to users around the world. Various anomalies occur often and degrade users' Quality of Experience (QoE). Detecting and locating such anomalies are highly complex due to a large number of different entities involved in the end-to-end video delivery. These entities include VoD provider, CDN/Cloud providers, transit ISPs, access ISPs, and end user devices. QoE perceived by the users is a critical metric for VoD providers. We propose QWatch, a scalable monitoring system, which detects and locates anomalies based on the end user QoE in real-time. We evaluate QWatch in a controlled VoD system and production Microsoft Azure Cloud and CDN. QWatch effectively detects and locates QoE anomalies in our extensive experiments. We discuss insights obtained from running VoD system with 200 worldwide users in production Cloud(1).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8