Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Lippo Group Digital, profile picture
Uploaded byLippo Group Digital
PPT, PDF945 views

Caching in Information Centric Network (ICN)

This document discusses caching in Information Centric Networks (ICN). It outlines some key characteristics of caching in ICN, including that it is transparent, ubiquitous, and can be done at a fine granularity level such as caching individual files or chunks. The document also discusses techniques for optimizing caching performance in ICN, focusing on cache dimensioning to determine optimal cache sizes, and cache decision policies to determine which objects are cached at which nodes. Both explicit coordination policies and implicit coordination policies are covered. Challenges for future research on caching in ICN are also presented.

Technology◦
Downloaded 47 times
Caching in Information Centric Network (ICN) Presented by: Priagung Khusumanegara
Outline  Introduction  Characteristics of caching in ICN  Transparent  Ubiquitous  Fine Granularity  Caching optimization in ICN  Dimensioning  Decision policy  Challenges and future research2
Introduction One of the important features of ICN is caching  Speed up content distribution  Improve network resource utilization Characteristics of caching in ICN  Transparent  Ubiquitous  Fine granularity3
Transparent  Making its routing and caching decisions on unified content names, essentially making these names network aware.  Several Challenges:  Inconsistency between caching objective  ICN should make reasonable choice of its caching objective to balance between diverse traffic types.  Cross-application competitive sharing of cache space  Different types of traffic differ significantly in their population scale, object size and object popularity.  ICN have to be able to efficiently share cache resources between different traffic types.  Line rate operation of caches  The cache management quite different from traditional disk- based management. 4
Ubiquitous  Topology of the cache network evolves from hierarchical trees to arbitrary graphs.  ICN more dynamics because its general cache network topology, ubiquity of in network caches and volatility of cached content. 5 Traditional Caching System Focus on Hierarchical Tree Evolution Information Centric Network Focus on Arbitrary Graph
Fine Granularity  Different options for the granularity of caching:  File-level o Caching individual files as transmitted through the network o Typical size 1.5 Kb each – as proposed by CNN  Chunk-level o Caching information chunks o An information object is split into a number of fixed-size information chucks – as proposed by ICN 6
Fine Granularity (Cont’d) Change of cache unit raises the following issues:  Change of popularity o Different chunks of a single file can have different access frequencies.  Failure of independent reference assumption o Traditional file-based caches are based on independent reference model o Requests for different chunks of the same file are often correlated, e.g. in sequential order  Opportunity for more efficient use of the cache space o It possible to retrieve different parts of the same file from different nodes, which speeds up the retrieval rate and improves the space utilization. 7
Techniques for ICN performance optimization  Focus on: – Cache dimensioning – Cache decision policy 8
Cache Dimensioning - Since ICN cache should operate at line rate, the cache size that can be installed at each caching node is thus limited. - There are two issues that remain to be addressed:  How large the cache space should be to have noticeable performance improvement? o Preferred to configure the cache size based on the router’s performance disparity.  How to allocate the storage resource across different cache nodes? o Degree based allocation: the cache capacity allocated to a node is proportional to its node degree. 9
Cache Decision Policy  Cache decision policy – It determines which objects are to be placed at which cache nodes. – Two kinds of cache decision policy  Explicit cache coordination decision  Implicit cache coordination 10
Explicit Cache Coordination  Object access pattern, cache network topology and each cache’s state as input for the calculation of the placement position of each object.  Common approaches can be classified into three categories: – Global – Path – Neighborhood 11
Explicit Cache Coordination (Cont’d)  Global coordination – Involves all cache nodes – Object placement based on network distance between cache nodes and object access frequencies at each cache node  Path coordination – Only involves the cache nodes along the path from the request hit place to the requesting client – e.g.: en-route web caching 12
Explicit Cache Coordination (Cont’d)  Neighborhood coordination – Coordination takes place among a node’s neighborhood. – E.g.: Cooperative In-Network Caching (CINC) 13 Figure: The operation of coordination in network caching (CINC)
Implicit Cache Coordination  Each node does not need to know the state information of other cache nodes  LCE (leave copy everywhere) – Copy the object at each node along the downloading path – Disadvantage: Degrade the performance of the network and underutilize some of the network resources. 14
Implicit Cache Coordination (Cont’d)  Leave Copy Down (LCD): – When a cache hit occurs, this scheme only caches the object at the direct downstream node – Avoid a large number of copies of the same object15 Figure: LCD (Leave Copy Down)
Implicit Cache Coordination (Cont’d)  Move Copy Down (MCD): – This scheme moves the object from the hit node to its direct downstream node, and deletes the object from the hit node. 16 Figure: MCD (Move Copy Down)
Implicit Cache Coordination (Cont’d)  Copy with Probability - The requested object is copied with a given probability p at each node along the returning path 17 Figure: Copy with Probability
Implicit Cache Coordination (Cont’d)  Random Copy One - The requested object is copied at one random node along the returning path 18 Figure: Random Copy One
Implicit Cache Coordination (Cont’d)  Probability Cache - The requested object is copied at each node with a probability. But, for each node, the probability varies. 19 Figure: Probability Cache
Correlation Between Cache Decisions - WAVE adjusts the number of chunks cached at each node based on the file’s popularity - When the number of requests for a file increases, WAVE reacts with exponential increase in the number of chucks cached for this file. - A content router in WAVE explicitly sets the cache indication mark - Once the chunk is cached, the cache indication mark is cleared. 20 Figure: Operation of WAVE
Challenges and Future Direction  Cache object popularity – Establish the chunk- level object popularity model from prior knowledge – Measure the chunk-level object popularity directly  Correlation between requests and correlation-based cache decision – What is the inherent correlation between different requests, – How to model this correlation, and – How to optimize the cache decision policy based on the request correlation,  ICN friendly network topology – What kind of network topology is suitable for ICN network. 21