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Benefit-based Data Caching in Ad Hoc Networks

Bin Tang, Himanshu Gupta and Samir Das

Department of Computer ScienceStony Brook University

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Outline Motivation Problem Statement Algorithm and Protocol Design Performance Evaluation Conclusions and future work

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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

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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

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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

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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

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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

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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

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Maintenance of Nearest-cache Table

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

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Mobility Servers periodically broadcasts cache

list

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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

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Performance Evaluation

CGA vs. DGA Comparison

DGA vs. HybridCache Comparison

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“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

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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

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Varying Number of Data Items and Memory Capacity – Transmission radius =5, number of nodes = 500

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

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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

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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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

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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?

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Questions?

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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

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