Benefit-based Data Caching in Ad Hoc Networks

1. Benefit-based Data Caching in Ad Hoc Networks Bin Tang, Himanshu Gupta and Samir Das Department of Computer Science Stony Brook University ICNP'06

2. Outline • Motivation • Problem Statement • Algorithm and Protocol Design • Performance Evaluation • Conclusions and future work ICNP'06

3. Motivation • Ad hoc networks are resource constrained • Bandwidth scarcity of network • Battery energy, memory limitation • Cache can save access/communication cost, and thus, energy and bandwidth • Our work is the first to present a distributed caching implementation based on an approximation algorithm ICNP'06

4. Problem Statement • Given: • Ad hoc network graph G(V,E) • Multiple data items P, each stored at its server node • Access frequency of each node for each data item • Memory constraint of each node • Goal: • Select cache nodes to minimize the total access cost: ∑i є V ∑j є P (access frequency of i to j x distance to nearest cache of j) • Under memory constraint ICNP'06

5. Algorithm Design Outline • Centralized Greedy Algorithm (CGA) • Delivers a solution whose benefit is at least 1/2 of the optimal benefit (for uniform size data) • Distributed Greedy Algorithm (DGA) • Purely localized ICNP'06

6. Centralized Greedy Algorithm (CGA) • Benefit of caching a data item in a node: the reduction of total access cost • CGA iteratively caches data items into memory pages of nodes that maximizes the benefit at each step • Theorem: CGA delivers a solution whose total benefit is at least 1/2 of the optimal benefit for uniform data item • 1/4 for non-uniform size data item ICNP'06

7. Proof Sketch • L: greedy solution, C: total benefit in greedy • L’: optimal solution, O: total benefit in optimal • G’: modified network of G, each node • has twice memory capacity as that in G • contains the data items selected by CGA and optimal • O’: benefit for G’ = sum of the benefits of adding L and L’ in that order • O < O’ = C + ∑ benefit of L’ w.r.t L < C + ∑ benefit of L’ w.r.t. {} < C + C ICNP'06

8. Distributed Greedy Algorithm (DGA) • Nearest-cache table • maintains nearest cache node for each data • If node caches a data, also maintains second-nearest cache • Maintenance of nearest-cache and second-nearest cache and its correctness • Assume distances values are available from underlying routing protocol • Localized caching policy ICNP'06

9. Node i cache data Dj notify server of Dj (server maintains cache list Cj for Dj) broadcast (i, Dj) to neighbors On recv (i, Dj) if i is nearer than current nearest-cache of Dj, update and broadcast to neighbors else send it to nearest-cache of i i delete Dj get Cj from server of Dj broadcast (i, Dj, Cj) to neighbors On recv (i, Dj, Cj) if i is current nearest-cache for Dj, update using Cj, broadcast else send it to nearest- cache of i Maintenance of Nearest-cache Table ICNP'06

10. Mobility • Servers periodically broadcasts cache list ICNP'06

11. Localized caching policy • Observe local traffic and calculate the local benefit of caching or removing a data item • Cache the most “beneficial” data items • Local benefit/data item size for cache replacement • Benefit threshold to suppress traffic ICNP'06

12. Performance Evaluation • CGA vs. DGA Comparison • DGA vs. HybridCache Comparison ICNP'06

13. “Supporting Cooperative caching in Ad Hoc Networks” (Yin & Cao infocom’04): • CacheData – caches passing-by data item • CachePath – caches path to the nearest cache • HybridCache – caches data if size is small enough, otherwise caches the path to the data • Only work of a purely distributed cache placement algorithm with memory constraint ICNP'06

14. CGA vs. DGA - Random network of 100 to 500 nodes in a 30 x 30 region • Parameters: • topology-related -- number of nodes, transmission radius • application-related -- number of data items, number of clients • problem constraint -- memory capacity • Summary of simulation results: • CGA performs slightly better by exploiting global info • DGA performs quite close to CGA • The performance difference decreases with increasing memory capacity ICNP'06

15. Varying Number of Data Items and Memory Capacity – Transmission radius =5, number of nodes = 500 ICNP'06

16. Varying Network Size and Transmission Radius - number of data items = 1000, each node’s memory capacity = 20 units ICNP'06

17. DGA vs. HybridCache • Simulation setup: • Ns2, routing protocol is DSDV • 2000m x 500m area • Random waypoint model, 100 nodes move at a speed within (0,20m/s) • Tr=250m, bandwidth=2Mbps • Simulation metrics: • Average query delay • Query success ratio • Total number of messages ICNP'06

18. Server Model: Two servers, 1000 data items: even-id data items in one server, odd-id the other Data size:[100, 1500] bytes Client Model: A single stream of read-only queries Data access model Spatial access pattern: access frequency depends on geographic location Random pattern: Each node accesses 200 data items randomly from the 1000 data items Naïve caching: caches any passing-by item if there is free space, uses LRU for cache replacement

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20. Summary of Simulation Results • Both HybridCache and DGA outperform Naïve approach • DGA outperforms HybridCache in all metrics • For frequent queries and small cache size, DGA has much better average query delay and query success ratio • For high mobility, DGA has slight worse average delay, but much better query success ratio ICNP'06

21. Conclusions and Future work • Data caching problem under memory constraint • Provable approximation algorithm • Feasible distributed implementation • Future work: • Reduce nearest-cache table size • Node failure • Benefit?…Mm…Game theoretical analysis? ICNP'06

22. Questions? ICNP'06

23. Correctness of the maintenance • Nearest-cache table is correct • For node k whose nearest-cache table needs to change in response to a new cache i, every intermediate nodes between k and i needs to change its table • Second-nearest cache is correct • For cache node k whose second-nearest cache should be changed to i in response to new cache i, there exist two distinct neighboring nodes i1, i2 s.t. nearest-cache node of i1 is k and nearest-cache node of i2 is i ICNP'06

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