Complex Interactions in Content Distribution Ecosystem and QoE

Transcription

1 Complex Interactions in Content Distribution Ecosystem and QoE Zhi-Li Zhang Qwest Chair Professor & Distinguished McKnight University Professor Dept. of Computer Science & Eng., University of Minnesota Partly in collaboration with Don Towsley, Ramesh Sitaraman, Chris Hollot of UMASS, Amherst

2 Content is the King! A dominant function of today s Internet is delivery of various types of information or content contributing to a large portion of Internet traffic esp. video streaming Netflix alone was estimated to be ~ 1/3 of Internet traffic in North America Content distribution systems: evolved from a simple clientserver file download to a vastly complex, distributed system with 100s or 1000s of servers & other appliances dispersed over large geo-areas: data centers, content distribution networks,

3 Content Distribution Ecosystem Multiple major entities involved! content providers (CPs), content distribution networks (CDNs), ISPs and of course, end systems & users some entities may assume multiple roles Complex business relationships: sometimes cooperative, but often competitive CP2 data centers CDN2 & its servers CDN1 & its servers CP1 ISP data centers media players ISP ISP users

4 Content Delivery Systems & QoE Content delivery system is a complex distributed system! Not only networks, but also streaming servers, storage,. Backend data centers, distributed cache/edge server infrastructure User interests and demand drive the system: socio-technical interplay, business models & economics User quality-of-experience (QoE): one of many concerns -- Reliability, Availability, Scalability and Performance How does it handle geographically diverse user requests? Where should edge servers or caches be placed? How does it handle cache misses? How does it perform load-balancing and handle/avoid hotspots? How does it meet user performance expectations such as quality of video or low-latency How does it adapt to network conditions, e.g. bw changes? Ø System architecture & interactions among entities matter! Ø It is simpler to ensure QoE if one entity (e.g., CP) controls all or most system components (e.g., Google)!

5 Case Study I: Bing vs. Google Web search as (dynamic) content delivery Response contains both static content (e.g., banner, css files) and dynamically generated search response (dynamic content) User QoE metric: end-to-end search response time (SRT) Ø key to ensure user engagement for clicks & ad revenue Google Latency +0.5 sec revenue -20% Bing Latency +2 sec revenue -4.3%

6 Case Study I: Bing vs. Google Web search as (dynamic) content delivery Response contains both static content (e.g., banner, css files) and dynamically generated search response (dynamic content) User QoE metric: end-to-end search response time (SRT) Generic Web Search System Architecture backend data centers processing search queries & generating responses front-end edge servers (CDN) handling search query delivery Ø Google: deploy its own CDN Ø Bing: utilize Akamai CDN (it now also builds its own CDN) Back-end (BE) Data Centers Front-End (FE) Servers (Edge Cloud/CDN)

7 Search Response Time (SRT): a Tale of Two Search Engines Google Bing SRTs for simple text queries conducted via various nodes on PlanetLab

8 Search Response Time (SRT): a Tale of Two Search Engines In fact, Akamai deploys more edge servers than Google, and generally closer to users But other factors also play major roles: a) Google has a dedicated network between its CDNs and backend data centers; b) Akamai uses public Internet for connections between its CDN servers and original servers of CPs

9 Case Study II: (Aggr.) Search Response Time Paradox: from a provider s view

10 Case Study II: (Aggr.) Search Response Time Paradox: from a provider s view Edge and Backend Servers: for processing & generating search responses Networks: for both sending the query to search engine and delivering the response to the user/user agent Client Web Browser (or other User Agent): for generating the search query (in HTTP request) and for rendering and displaying the search response Type of Search Query (User Behavior): may affect all the three factors above Ø Slower home networks + rich media search (e.g., images, videos) despite faster browser + lower server loads during off-peak & weekend hours è longer aggregate SRT response times & high variability

11 Case Study III: Netflix & DASH Netflix has its own data center for certain crucial operations (e.g., user registration, billing, ) Most web-based user-video interaction, computation/storage operations are cloud-sourced to Amazon AWS Video delivery is out/cloud-sourced to 3 CDNs Desktop users use MS Silverlight player for video streaming Netflix Architecture More recently, Netflix is also building its own CDN via Netflix OpenConnect

12 QoE: CDN Selection & Rate Adaption Given Netflix employs three CDNs for video delivery, there are multiple possibilities for ensuring QoE for users, e.g., per user request: select best CDN, or best CDN edge server, or select best quality of video based on network bandwidth when network/system condition changes, dynamically adjust as above Netflix: use a static CDN ranking per user (independent of location) when net bw decreases, Netflix sticks to the same CDN (server), perform rate adaption by fetching lower quality chunk Why does Netflix use a static CDN ranking for each user? Is it based upon performance? Is there a single best CDN? Does CDN performance remain unchanged over time? Does this strategy give users the best possible experience? Ø Answers are No to all the above

13 Adapting to Bandwidth Changes Lower quality levels in response to lower bandwidth Switch CDN only when minimum quality level cannot be supported Netflix seems to use multiple CDNs only for scalability & failover purposes! And priority plausibly selected based on business concerns

14 Intelligent CDN Selection & QoE We can improve user QoE by performing more intelligent CDN selection One strategy: selecting best CDN using initial measurements! Another: use multiple CDNs simultaneously for parallel download Average improvement is 54% and 70% for residential hosts and PlanetLab nodes over the best CDN!

15 Complex Interaction in Content Distribution Ecosystem & QoE Need to account for the system architecture & complex interactions among entities in content distribution ecosystem User quality-of-experience (QoE): one of many concerns -- Reliability, Availability, Scalability and Performance; & Content Ø Four dimensions for considering the complex interactions in content distribution ecosystem & QoE enhancement Control Points where are control points located: end system, ISP, CDN edge, CP? Control Information what information is available or can be inferred, granularity, fidelity? Control Framework: Global vs. Local Decision Making (logically) centralized, globally coordinated, or locally independent? Time Scales In collaboration with Profs. Don Towsley, Ramesh Sitaraman & Chris Hollot under a NSF CNS Collaborative Large Project