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SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network

SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network. Yan Chen, Randy H. Katz, John D. Kubiatowicz {yanchen, randy, kubitron}@CS.Berkeley.EDU EECS Department UC Berkeley. Outlines. Motivation Goal and Challenges Previous Work SCAN Architecture and Components

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SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network

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  1. SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network Yan Chen, Randy H. Katz, John D. Kubiatowicz {yanchen, randy, kubitron}@CS.Berkeley.EDU EECS Department UC Berkeley

  2. Outlines • Motivation • Goal and Challenges • Previous Work • SCAN Architecture and Components • Evaluation Methodology • Results • Conclusions

  3. Motivation Scenario: World Cup 2002

  4. Goal and Challenges Provide content distribution to clients with good latency and staleness, while retaining efficient and balanced resource consumption of the underlying infrastructure • Dynamicchoice ofnumber and location of replicas • Clients’ QoS constraints: latency, staleness • Servers’ capacity constraints • Efficient resource consumption • Small delay, bandwidth consumption for replica update • Small replica management cost • Scalability: millions of objects, clients and servers • No global network topology knowledge

  5. Previous Work • Replica Placement • Research communities: optimal static replica placement • Assume clients’ distributions, access patterns & IP topology • No consideration for clients’ QoS or servers’ capacity constraints • CDN operators: un-cooperative, ad hoc placement • Centralized CDN name server cannot record replica locations – place many more than necessary (ICNP ’02) • Update Multicast • No inter-domain IP multicast • Most application-level multicast (ALM) unscalable • Split root as common solution, suffers consistency overhead

  6. replica cache always update adaptive coherence CDN server client DOLR mesh SCAN: Scalable Content Access Network Dynamic replica placement & d-tree construction data source data plane Web content server network plane DOLR with locality

  7. Components of SCAN • Decentralized Object Location & Routing (DOLR) • Properties needed • Scalable location with guaranteed success • Search with locality • Improve the scalability of d-tree: each member only maintains states for its parent and direct children • Simultaneous Dynamic Replica Placement and d-tree Construction • Replica search: Singular, Localized or Exhaustive • Replica placement on DOLR path: Lazy or Eager

  8. parent candidate proxy DOLR path Replica Search • Singular Search data plane s c network plane DOLR mesh

  9. Greedy load distribution parent candidates DOLR path Replica Search • Localized search data plane client child s parent proxy sibling c server child DOLR mesh network plane

  10. first placement choice Replica Placement: Eager data plane s proxy c network plane DOLR mesh DOLR path

  11. first placement choice DOLR path Replica Placement: Lazy data plane client child s proxy c network plane DOLR mesh

  12. Evaluation of Alternatives • Two dynamic overlay approaches • Overlay_naïve: Singular search + Eager placement • Overlay_smart: Localized search + Lazy placement • Compared with static placement + IP multicast • Overlay_static: With global overlay topology • IP_static: With global IP topology (ideal) • Metrics • Number of replicas deployed, load distribution • Multicast performance: Relative Delay Penalty (RDP) and bandwidth consumption • Tree construction traffic (packets and bandwidth)

  13. Methodology • Network Topology • 5000-node network with GT-ITM transit-stub model • SCAN nodes placed randomly or on transit nodes • NS-like Packet-level Network Simulations • Workloads • Synthetic flash crowd: all clients access a hot object in random order • Real Web server traces: NASA and MSNBC

  14. Methodology: Sensitivity Analysis • Various Client/Server Ratio • Various Server Density • Various Latency & Capacity Constraints • Various Network Topologies • Average over 5 topologies with different setup • All Have Similar Trend of Results • Overlay_smart has close-to-optimal (IP_static) number of replicas, load distribution, multicast performance with reasonable amount of tree construction traffic

  15. Number of Replicas Deployed and Load Distribution • Overlay_smart uses only 30-60% of replicas than overlay_naïve and very close to IP_static • Overlay_smart has two times better load distribution than od_naïve, overlay_static and very close to IP_static

  16. Multicast Performance • 85% of overlay_smart Relative Delay Penalty (RDP) less than 4 • Bandwidth consumed by overlay_smart is very close to IP_static, and is only 1/3 of bandwidth by overlay_naive

  17. Tree Construction Traffic Including “join” requests, “ping” messages, replica placement and parent/child registration • Overlay_smart consumes 3 - 4 times of traffic than overlay_naïve, and the traffic of overlay_naïve is quite close to IP_static • Far less frequent event than access & update dissemination

  18. Conclusions • P2P networks can be used to construct CDNs • SCAN: Scalable Content Access Network with good QoS, efficiency and load balancing • Simultaneous dynamic replica placement & d-tree construction • Leverage DOLR to improve scalabilityandlocality • In particular, overlay_smart recommended • Localized search + Lazy placement • Close to optimal number of replicas, good load distribution, low multicast delay and bandwidth penalty at the price of reasonable construction traffic

  19. Results on Web Server Traces • Limited simulations, most URLs have very few requests • Overlay_smart uses only one third to half replicas than overlay_naïve for hot objects

  20. replica cache always update adaptive coherence CDN server client DOLR mesh SCAN: Scalable Content Access Network Dynamic replica placement & d-tree construction data source data plane Web content server network plane DOLR with locality

  21. parent candidate proxy DOLR path Replica Search • Singular Search data plane s c network plane DOLR mesh

  22. Localized search • Greedy load distribution parent candidates DOLR path Replica Search data plane client child s parent proxy sibling c server child network plane

  23. first placement choice Dynamic Replica Placement: naïve • Singular Search • Eager Placement data plane parent candidate s proxy c network plane Tapestry mesh Tapestry overlay path

  24. first placement choice Tapestry overlay path Dynamic Replica Placement: smart • Localized search • Lazy placement • Greedy load distribution data plane parent candidates client child s parent proxy sibling c server child network plane

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