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

The SCAN (Scalable Content Access Network) framework addresses challenges in efficient content distribution across clients by dynamically managing replica placement and leveraging decentralized object location and routing (DOLR). Designed for environments with high client variability such as large-scale events, SCAN ensures low latency and staleness while optimizing resource consumption. Our evaluation shows that SCAN achieves close-to-optimal load distribution and multicast performance with significantly lower bandwidth requirements. This work presents a viable approach for building robust, scalable Content Delivery Networks.

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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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