Dynamic Replica Placement for Scalable Content Delivery

1. Dynamic Replica Placement for Scalable Content Delivery Yan Chen, Randy H. Katz, John D. Kubiatowicz {yanchen, randy, kubitron}@CS.Berkeley.EDU EECS Department UC Berkeley

2. replica client Motivation Scenario data source data plane Web content server CDN server network plane

3. Goal and Challenges Provide content distribution to clients with good Quality of Service (QoS) while retaining efficient and balanced resource consumption of the underlying infrastructure • Dynamically choose the number and placement of replicas while satisfying clients’ QoS and servers’ capacity constraints • Good performance for update dissemination • Delay - Bandwidth consumption • Without global network topology knowledge • Scalability for millions of objects, clients and servers

4. Outline • Goal and Challenges • Previous Work • Our Solutions: Dissemination Tree • Peer-to-peer Location Service • Replica Placement and Tree Construction • Evaluation • Conclusion and Future Work

5. Previous Work • Focused on static replica placement • Clients’ distributions and access patterns known in advance • Assume global IP network topology • DNS-redirection based CDNs highly inefficient • Centralized CDN name server cannot record replica locations • No inter-domain IP multicast • Application-level multicast (ALM) unscalable • Root maintains states for all children or handle all “join” requests

6. replica cache always update adaptive coherence client Tapestry mesh Solution: Dissemination Tree • Dynamic replica placement use close-to-minimum number of replicas to satisfy QoS and capacity constraints with local network topology • Adaptive cache coherence for efficient coherence notification • Use Tapestry location service to improve the scalability & locality data source data plane root server server network plane

7. Peer-to-peer Routing and Location Services • Properties Needed by d-tree • Distributed, scalable location with guaranteed success • Search with locality • P2P Routing and Location Services: • CAN, Chord, Pastry, Tapestry, etc. • Http://www.cs.berkeley.edu/~ravenben/tapestry

8. Integrated Replica Placement and d-treeConstruction • Dynamic Replica Placement + Application-level Multicast • Naïve approach - Smart approach • Static Replica Placement + IP Multicast • Modeled as capacitated facility location problem • Design a greedy algorithm with logN approximation • Optimal case for comparison • Soft-state Tree Maintenance

9. parent candidate Tapestry overlay path Dynamic Replica Placement: naïve data plane s surrogate c network plane

10. Dynamic Replica Placement: naïve data plane parent candidate s surrogate c network plane Tapestry overlay path first placement choice

11. Aggressive search • Greedy load distribution parent candidates Tapestry overlay path Dynamic Replica Placement: smart data plane client child s parent surrogate sibling c server child network plane

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

13. Evaluation Methodology • Network Topology • 5000-node network with GT-ITM transit-stub model • 500 d-tree server nodes, 4500 clients join in random order • Network Simulator • NS-like packet-level priority-queue based event simulator • Dissemination Tree Server Deployment • Random d-tree • Backbone d-tree (choose backbone routers and subnet gateways first) • Constraints • 50 ms latency bound and 200 clients/server load bound

14. Performance of Dynamic Replica Placement • Compare Four Approaches • Overlay dynamic naïve placement (dynamic_naïve) • Overlay dynamic smart placement (dynamic_smart) • Static placement on overlay network (overlay_static) • Static placement on IP network (IP_static) • Metrics • Number of replicas deployed and load distribution • Dissemination multicast performance • Tree construction traffic

15. Number of Replicas Deployed and Load Distribution • Overlay_smart uses much less replicas than overlay_naïve and very close to IP_static • Overlay_smart has 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 much less than overlay_naive

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

18. Conclusions and Future Work • Dissemination Tree: dynamic Content Distribution Network on top of a peer-to-peer location service • Dynamic replica placement satisfy QoS and capacity constraints and self-organize into an app-level multicast tree • Use Tapestry to improve the scalability and locality • Simulation Results Show • Close to optimal number of replicas, good load distribution, low multicast delay and bandwidth penalty at the price of reasonable construction traffic • Future Work • Evaluate with more diverse topologies and workload • Dynamic replica deletion/migration to adapt to the shift of users’ interests

19. 5712 7510 4510 3210 0880 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 4 4 4 4 4 5 5 5 5 5 7 7 7 7 7 3 3 3 3 3 6 6 6 6 6 Neighbor Map For “5712” (Octal) 0712 x012 xx02 xxx0 1712 x112 5712 xxx1 2712 x212 xx22 5712 3712 x312 xx32 xxx3 4712 x412 xx42 xxx4 5712 x512 xx52 xxx5 6712 x612 xx62 xxx6 7712 5712 xx72 xxx7 4 3 2 1 Routing Levels Routing in Detail Example: Octal digits, 212 namespace, 5712  7510 5712 0880 3210 4510 7510

20. Dynamic Replica Placement • Client c sends “join” request to statistically closest server s with object o through nearest representative server rs • Naïve placement only checks s before placing new replicas while smart algorithm in addition checks parent, free siblings and free server children of s • Remaining capacity info piggybacked in soft-state messages • If unsatisfied, s place new replica on one of the Tapestry path nodes from c to s (path piggybacked in the request) • Naïve one always chooses the closest qualified node (to c), while smart one puts on the furthest qualified node

21. Static Replica Placement • Without Capacity Constraints: Minimal Set Cover • Most cost-effective method: Greedy algorithm [Grossman & Wool 1994] • With Capacity Constraints: Variant of Capacitated Facility Location Problem • C demand locations, S locations to build facilities, the capacity installed at each location is an integer multiple of u • Facility building cost f_i, service cost c_ij • Objective function: minimize sum of f_i and c_ij • Mapped to our problem: f_i = 1 and c_ij = 0 if location i cover demand j and infinity otherwise

22. Static Replica Placement Solutions • Best theoretical one: use Primal-dual schema and Lagrangian relaxation [Jain & Varizani 1999] • Approximation ratio 4 • Variant of greedy algorithm • Approximation ratio ln|S| • Choose s with the largest value of min(cardinality |C_s|, remaining capacity RC_s) • If RC_s < |C_s|, choose those least-covered clients to cover first • With Global IP topology vs. with Tapestry overlay path topology only

23. Soft State Tree Maintenance • Bi-directional Messaging • Heartbeatmessage downstream & refresh message upstream • Scalability • Each member only maintains states for direct children+parent • “Join” request can be handled by any member • Fault-tolerance through “rejoining”

24. Performance Under Various Conditions • With 100, 1000 or 4500 clients • With or without load constraints • Od_smart performs consistently better than od_naïve and close to IP_s as before • Load constraint can avoid hot spot

25. Performance Under Various Conditions II • With 2500 random d-tree servers • Merit of scalability: maximal number of server children is very small compared with total number of servers • Due to the randomized and “search with locality” properties of Tapestry

26. data plane parent candidates data source replica cache client child s parent surrogate root server sibling server server child c client network plane Tapestry mesh Tapestry overlay path first placement choice