Improving On-demand Data Access Efficiency with Cooperative Caching in MANETs

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Improving On-demand Data Access Efficiency with
Cooperative Caching in MANETs
Phd Dissertation Defense 11.21.05_at_CSE.ASU Yu Du
Chair Dr. Sandeep Gupta
Committee Dr. Partha Dasgupta Dr. Arunabha Sen Dr. Guoliang Xue
Supported in part by NSF grants ANI-0123980,
ANI-0196156, and ANI-0086020, and Consortium for
Embedded Systems.
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Roadmap

• 1. Introduction
• 2. Cooperative caching
• 3. Related works
• 4. Proposed approach COOP
• 5. Performance evaluation
• 6. Conclusions and future works

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1.1 Problems of data access in MANETs
1. Introduction

• MANETs Mobile Ad hoc Networks
• Wireless medium
• Multi-hop routes
• Dynamic topologies
• Resource constraints
• On-demand data access client/server model.

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1.2. Reducing data access costs in MANETs
1. Introduction

• The locality principle Denning
• Computer programs tend to repeat referencing a
  subset of data/instructions.
• Used in processor caches, storage hierarchies,
  Web browsers, and search engines.
• Zipfs law Zipf
• P(i) ? 1/ia(aclose to unity), common interests in
  popular data.
• 80-20 rule 80 data accesses happen on 20 data.
• Cooperative caching
• Multiple nodes share and cooperatively manage
  their cached contents.

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1.3. Cooperative caching
1. Introduction

• Cooperative caching
• A caching node not only serves its own data
  requests but also the requests from others.
• A caching node not only stores data for its own
  needs but also for others.
• Shorter path, less expensive links, less
  conflictions, lower risks of route breakage.
• save time, energy, and bandwidth consumption as
  well as improves data availability.
• Why?
• Data locality and commonality in users
  interests.
• Client/Server communication Vs. inter-cache
  communication.
• Users around the same location tend to have
  similar interests.
• People gathered around the food court menus.
• Exploration team environmental information.

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Roadmap

• 1. Introduction
• 2. Cooperative caching
• 2.1. Overview
• 2.2. Cache resolution
• 2.3. Cache management
• 2.4. Cache consistency control
• 3. Related works
• 4. Proposed approach COOP
• 5. Performance evaluation
• 6. Conclusions and future works

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2.1 Overview
2. Cooperative caching

• Cooperative caching
• Multiple nodes share and cooperatively manage
  their cached contents.
• Cache resolution
• Cache management
• Cache consistency control
• Used in Webcache/Proxy servers on Internet.
• To alleviate server overloading and response
  delay.
• Did not consider special features of MANETs.

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2.2 Cache resolution
2. Cooperative caching

• How to find a cache storing the requested data?

Hierarchical
Directory-based
Hash table based





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2
3
4
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Caching node Data items
Node 1 Node 2 Item1 Item2
Node 1
Node 2
Node 3
Harvest Chank96
Summary Fan00
Squirrel Lyer02
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2.3 Cache management
2. Cooperative caching

• What to cache?
• Admission control.
• Cache replacement algorithm.
• LRU
• Extended LRU (Squirrel)
• any access has same impact, whether it is from
  the local node or other nodes.

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2.4 Cache consistency control
2. Cooperative caching

• How to maintain the consistency between server
  and cache?
• Strong/Weak consistency whether consistency is
  always guaranteed.
• Pull/Push-based who (client/server) initiates
  the consistency verification.
• TTL is used in this research.
• Each data item has a Time-To-Live field allowed
  caching time.
• TTL is popularly adopted in real applications
  HTTP.
• Lower cost than strong-consistency protocols.

Pull-based Push-based
Weak TTL Synchronous Invalidation
Strong Lease Asynchronous Invalidation
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3. Related works
Schemes Cache Resolution Cache management Consistency control Network model
Harvest Chank96 Hierarchically No specification TTL WAN
Summary Fan00 Directory-based LRU TTL WAN
Squirrel Lyer02 Hash-based Extended LRU TTL LAN
Cao04 Cao04 CacheData, CachePath, HybridCache LRU TTL MANET
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Roadmap

• 1. Introduction
• 2. Cooperative caching
• 3. Related works
• 4. Proposed approach COOP
• 4.1. System architecture
• 4.2. Cache resolution
• 4.3. Cache management
• 5. Performance evaluation
• 6. Conclusions and future works

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4.1 System architecture
4. Proposed approach COOP

• Each node runs a COOP instance.
• The running COOP instance
• Receives data requests from users applications.
• Resolves requests using the cocktail cache
  resolution scheme.
• Decides what data to cache using COOP cache
  management scheme.
• Uses the underlying protocol stack.

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4.2. Cache Resolution
4. Proposed approach COOP

• 4.2.1. Hop-by-Hop
• 4.2.2. Zone-based
• 4.2.3. Profile-based
• 4.2.4. COOP cache resolution a cocktail approach

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4.2.1 Hop-by-Hop cache resolution
4. Proposed approach COOP, 4.2 Cache resolution

• The forwarding nodes try to resolve a data
  request before relaying it to the next hop.
• Reduces the travel distance of requests/replies.
• Helps to avoid expensive/unreliable network
  channels.

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4.2.2 Zone-based cache resolution
4. Proposed approach COOP, 4.2 Cache resolution

• Users around the same location tend to share
  common interests.
• Cooperation zone the surrounding nodes within
  r-hop range.
• r the radius of the cooperation zone
• To find an item within the cooperation zone
• Reactive approach flooding within the
  cooperation zone.
• Proactive approach record previous heard
  requests.

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4.2.3 Profile-based cache resolution
4. Proposed approach COOP, 4.2 Cache resolution

• Records received request to assist future cache
  resolution
• RRT Recent Request Table.
• Entry is deleted when if the recorded requester
  fails to reply the corresponding data item.
• When the table is full, use LRU to decide
  replacement.

Requester Time Requested Data ID
192.168.0.11 15265908162005 D1
192.168.0.15 15255908162005 D2
192.168.0.18 15205908162005 D3
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4.2.4 COOP cache resolution a cocktail approach
4. Proposed approach COOP, 4.2 Cache resolution
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4.3. Cache Management
4. Proposed approach COOP

• 4.3.1. Primary and secondary data
• 4.3.2. Inter-category and intra-category rules

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4.3.1. Primary and secondary data
4. Proposed approach COOP, 4.3 Cache management

• Different cache misses may introduce different
  costs.
• Example cache miss cost for X is higher than
  cache miss cost for Y.
• Primary data and secondary data.
• Primary data not available within cooperation
  zone.
• Secondary data available within cooperation
  zone.

Y has to be obtained from the server.
X can be obtained from a neighbor.
Data Server
Data Server
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4.3.2. Inter-category and intra-category rules
4. Proposed approach COOP, 4.3 Cache management

• Inter-category rule
• when replacement decision is to be made between
  different categories.
• Primary data have precedence over secondary data
• Intra-category rule
• when replacement decision is to be made within
  the same category.
• LRU
• Example A1 A5 (Primary) B1 B6 (Secondary)

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Roadmap

• 1. Introduction
• 2. Cooperative caching
• 3. Related works
• 4. Proposed approach COOP
• 5. Performance evaluation
• 5.1. The impact of different zone radius
• 5.2. The impact of data access pattern
• 5.3. The impact of cache size
• 5.4. Data availability
• 5.5. Time cost average travel distance
• 5.6. Cache miss ratio
• 5.7. Energy cost message overhead
• 6. Conclusions and future works

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5.1 The impact of different zone radius
5. Performance evaluation

• (1) Average probability of finding a requested
  item d within the zone.
• (2) Average time cost
• assuming time cost is proportional to the number
  of covered hops
• (3) Average energy cost
• assuming time cost is proportional to the number
  of messages.

(1)
(2)
(3)
Pd average probability of a node caches d.
? the average node density.
L the distance (hops) between the requesting node and the server.
r the cooperation zone radius.
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5.1 The impact of different zone radius
5. Performance evaluation

• If an item is not found within a certain size
  cooperation zone, it is unlikely to find it
  within a larger size zone.
• The saturation point.

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5.2 The impact of access pattern
5. Performance evaluation

• a
• Cache miss ratio - -
• CT-3, CT-2, CT-1, HBH, SC
• Average travel distance - -
• CT-3, CT-2, CT-1, HBH, SC
• Average messages - -
• HBH CT-1, SC CT-2, CT-3

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5.3 The impact of cache size
5. Performance evaluation

• Cache size
• Cache miss ratio - -
• CT-3, CT-2, CT-1, HBH, SC
• Average travel distance - -
• CT-3 CT-2, CT-1, HBH, SC
• Average messages - -
• HBH CT-1, SC CT-2, CT-3

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5.4 Data availability
5. Performance evaluation

• Varied factors
• node number
• pause time
• node velocity
• Data availability
• CT-2, CT-1, HBH, SC

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5.5 Time cost average travel distance
5. Performance evaluation

• Varied factors
• node number
• pause time
• node velocity
• Average travel distance
• CT-2, CT-1, HBH, SC

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5.6 Cache miss ratio
5. Performance evaluation

• Varied factors
• node number
• pause time
• node velocity
• Cache miss ratio
• CT-2, CT-1, HBH, SC

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5.7 Energy cost average messages
5. Performance evaluation

• Varied factors
• node number
• pause time
• node velocity
• Average messages
• CT-1 HBH, SC, CT-2

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6. Conclusions and future works

• Cooperative caching is supported by data locality
  and the commonality in users interests.
• Proposed approach COOP
• Higher data availability
• Less time cost
• Smaller cache miss ratio
• The tradeoff is message overhead
• Tradeoff is dependent the cooperation zone
  radius.
• Future works
• Adapt cooperation zone radius based on users
  requirements.
• Explore different cooperation structure.
• Enforce fairness in cooperative caching.

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References

• Cao04 L. Yin and G. Cao, Supporting
  cooperative caching in ad hoc networks, INFOCOM,
  2004.
• Chank96 A. Chankhunthod et al. A Hierarchical
  internet object cache, USENIX Annual Technical
  Conference, 1996.
• Denning P. Denning, The locality principle,
  Communications of the ACM, July 2005.
• Fan00 L. Fan et al. Summary cache A scalable
  wide-area web cache sharing protocol, Sigcomm,
  1998.
• Lyer02 S. Lyer et al. Squirrel A
  decentralized peer-to-peer web cache, PODC,
  2002.
• Zipf G. Zipf, Human behavior and the principle
  of least effort, Addison-Wesley, 1949.

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Q A
Thank You!