CCN Computation Engine Pipeline for Data Traffic Reduction
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Solution Overview
Problem
Content Centric Networking (CCN) lacks efficient mechanisms for retrieving and processing collections of content objects, requiring separate requests for each object and not supporting convenient function execution, which leads to increased data traffic and storage needs, and is impractical due to limitations in lambda-calculus execution and URI encoding.
Innovation Solution
Implementing a method and system that allows computational transformations in CCN requests by decoding computation fields, executing computation pipelines, and encapsulating results, using a computation engine to process content objects and return transformed results, enabling efficient processing and caching of functions within the CCN network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate CCN interests are sent for each content object, then content objects can be retrieved individually with precise naming, but data traffic and storage requirements increase significantly
Solution Approach 1:
The patent combines multiple content object retrievals into a single CCN interest packet by incorporating computation fields that specify multiple URIs and computation functions. Instead of sending separate interest packets for each content object, the system merges them into one packet with computation instructions that will process multiple URIs and return aggregated results, thereby reducing data traffic and storage requirements while maintaining precise content object identification.
2Adaptability or versatility
If lambda-calculus execution is implemented for function computation, then computational transformations can be performed on content objects, but the system becomes impractical due to execution limitations and URI encoding issues
Solution Approach 1:
The patent replaces complex lambda-calculus execution with simpler, disposable computation functions that are encoded directly in the CCN interest packet. Instead of requiring a full lambda-calculus execution environment with its associated complexity and practicality issues, the system uses lightweight computation fields that can be evaluated directly by CCN routers using basic arithmetic and logical operations, making the system practical while retaining computational transformation capabilities.
3Adaptability or versatility
If computation fields are added to CCN requests, then computational transformations can be conveyed, but the device complexity increases
Solution Approach 1:
The patent segments the computation field into distinct, manageable components including URI lists, computation function identifiers, and parameter fields. This segmentation allows CCN routers to process only the relevant portions of the computation field based on their capabilities, reducing overall device complexity while maintaining support for computational transformations. Each segment can be independently parsed and evaluated.
Data Source
AI summary
A method is implemented by a node for implementing computational transformations conveyed in a content centric networking (CCN) request by a computation engine. The method includes receiving a CCN request containing a unique resource identifier (URI) and a computation field, decoding the computation field to determine a computation pipeline, retrieving a first content object identified by the URI, executing the computation pipeline with the first content object as input, and encoding a result of the computation pipeline as a second content object.