This document discusses how big data can help accelerate content delivery network (CDN) services. CDNs face challenges in optimizing their complex, multi-tier systems and correlating data from different sources and technologies. Big data approaches could collect and analyze log data from CDN sources to provide a 360-degree view. This would create a scalable tier for storing, processing, and deriving analytics from the data. Standardized data channels and flexible programming models could extract value from the data through content consumption profiling, predictions, and other analytics to accelerate new CDN services and business insights.
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CDN networks
● Content delivery network is an ecosystem
that comprises of the Content hosting orchestration- mashup - transport - edge access system
● They are essentially robust , reliable with
highest level of performance
● They are an aggregation of various
technologies
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CDNs are complex multi tier systems
VOD Services
DVR Services
Application/Content Tiers
Distribution
Services
Distribution Tiers
Edge Tiers
Edge Media
Services
Load balancer
Services
Streaming
Services
Management Tiers
Performance
management
System
management
Fault
management
Configuration
management
● CDN is an aggregation of technologies
● Each technology has an FCAPS system
● There can be multiple vendors within the same technology
tiers
● The Content - nature , organization, needs are very agile
5. Business challenges with CDN service
offering stacks
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Contents evolve and the
service stacks need to adapt
based on content profiles
Service stack optimization is
profit ! , but difficult
Service stack optimization
needs 360 degree view of
entire ecosystems, which is
challenging
Service management is
achieved by multiple
complex bespoke systems
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›Correlation Analytics of “operational data “ for
usage visibility is challenging because of bespoke
model implementations of data islands
›Lack of standardization of raw data formats
from various data sources
›Need of heavy processing/storing capabilities
to store and analyse data from various CDN
elements
›Need of future proof implementations to adapt
to new content organization and distribution
scenarios is limited
›Lesser Flexibility to implement and adapt new
services
6. BigData approaches for solving CDN
operational optimization challenges
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● Deploy BigData mechanism to collect and analyse log data from
various sources of CDN network
● Offload CDN sources from correlation & analytics overheads
● Use a push based mechanism from sources for optimal performance
to upload data to BigData engines
● Define a standardized channel to get data from various CDN data
sources
● Create a Scalable & Efficient BigData Tier for storing , processing
and deriving analytics
● Create a flexible programming model that can be extended for new
value engine creations
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BigData approaches over CDN
VOD Services
Streaming
Services
DVR Services
Performance
management
Application/Content Tiers
Distribution
Services
Distribution Tiers
Edge Tiers
Management Tiers
Edge Media
Services
Fault
management
Load balancer
Services
Configuration
management
Push Channels
Data Sync Service
HDFS
MAP Reduce
(Programming model)
- Content consumption profiling
- KPI generation
- Prediction engines
HBASE
Analytics Portal
System
management
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BigData Acceleration for CDNs
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BigData enables Value Models over CDN assets which provides
○ Content consumption analytics
○ User to Content consumption analytics
○ Content Ranking
○ Server consumption analytics
○ Traffic consumption analytics
User profiling based on content consumption
Create Foundation models over which Business services can be built
The Value Models also give accurate insights to business operations
Accelerate the definition and rollout of new CDN business services