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Progressive Caching in CCN

Progressive Caching in CCN. Authors: Jason Min Wang, Brahim Bensaou Publisher: GLOBECOM 2012 Presenter: Chai-Yi Chu Date : 2013/05/08. Outline. Introduction Proposed Caching Management Scheme Caching Decision Policy Replacement Strategies Simulation Experimental Methodology

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Progressive Caching in CCN

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  1. Progressive Caching in CCN Authors: Jason Min Wang, Brahim Bensaou Publisher:GLOBECOM 2012 Presenter: Chai-Yi Chu Date: 2013/05/08

  2. Outline • Introduction • Proposed Caching Management Scheme • Caching Decision Policy • Replacement Strategies • Simulation • Experimental Methodology • Experiment Results

  3. Introduction • propose a new caching scheme for such CCN networks and evaluate the in-network caching performance of this policy by comparing it with that of the default proposed policy via simulation.

  4. Characteristics that have crucial influence on the caching performance • Locality of references • Content popularity distribution • One-time referencing • Heavily-tailed object size distribution

  5. Proposed Caching Management Scheme • Caching Decision Policy • Resemblance to the LCD algorithm (Leave Copy Down) • Choosing the immediate downstream node of the cache hit point as the primary candidate place to replicate the data packet.

  6. : the number of interfaces saved in the PIT entry, that is, from how many distinct interfaces requests for the same namedchunkare aggregated. • : the actual number of individual requests for p at an edge node.

  7. Replacement Strategies • Edge nodes • A modification of the Greedy Dual-size algorithm. • Each cached chunk of data is associated with a value . • : the hop count needed to fetch the packet. • An “inflation” value .

  8. Intermediate nodes • Each cached chunk of data is associated with a value . • Interface • Diversity information will be recorded in and is used to leave breadcrumbs on the access statistics of after it has been cached.

  9. Simulation • Implemented a simplified CCN model on top of Omnet++ • simulation model includes three basic components of CCN i.e., CS, PIT and FIB • other features of CCN (e.g., hierarchical naming, routing, security issues and so on) are not taken into account.

  10. Experimental Methodology • Network topology

  11. Workloads • The synthetic Web workload generator ProWGen is used to generate workloads for the two content servers.

  12. Performance metric • systematic hit gain • :the distance between node and the original content server. • : the amount of pending requests at edge nodes for the hitting data. • : the size of object (chunks). • : the hop distance between node and the original content server of object . • The closer the value of G is to 1, the better the in-network caching system performs.

  13. Methodology • cache size • varied uniformly from 100 to 8,000 chunks for all nodes. • The chunk size is set 10KB • request aggregation • request aggregation time can change the observed access pattern and thus impact the hit rates of the nodes. • cache management scheme • alwayscache+LRU(the initial proposal of CCN • proposed PCP+heterogeneousreplacement algorithms

  14. Experiment Results • Impacts of cache size and content popularity

  15. Impact of request aggregation

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