Network Caching via Monotonic Queue Cost Functions
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Solution Overview
Problem
Existing approaches to caching content in computer networks face inefficiencies due to non-deterministic polynomial time (NP)-hard problems, leading to high computational complexity and costs associated with delay, queue size, and queuing probability.
Innovation Solution
A method that determines caching decisions based on monotonic, non-decreasing cost functions related to queue sizes, using a greedy approach and approximations via Taylor series, to efficiently manage content storage and reduce traffic-dependent costs in network queues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing caching approaches are used, then content can be stored and retrieved, but computational complexity becomes NP-hard and costs increase due to delay, queue size, and queuing probability
Solution Approach 1:
The patent transforms the caching optimization problem by changing the parameters from arbitrary cost functions to monotonic non-decreasing functions of queue sizes. This parameter transformation converts the NP-hard problem into a polynomial-time solvable problem using greedy algorithms, directly resolving the contradiction between caching effectiveness and computational complexity
Solution Approach 2:
The patent replaces complex deterministic optimization mechanisms with probabilistic greedy algorithms that use monotonic cost functions. This substitution transforms the mechanical system of exact NP-hard optimization into a more efficient probabilistic approach that achieves polynomial-time solutions while maintaining caching effectiveness
2Loss of energy
If caching decisions consider multiple parameters (delay, queue size, queuing probability), then cost reduction is achieved, but computational complexity increases
Solution Approach 1:
The patent simplifies the caching decision process by changing the cost function parameters to monotonic non-decreasing functions of queue sizes only. This transformation reduces the complexity of caching decisions from considering multiple independent parameters to considering a single parameter family, enabling polynomial-time solutions while achieving cost reduction
Solution Approach 2:
The patent segments the cost function into separate monotonic components associated with each queue size. This segmentation allows the complex multi-parameter optimization to be broken down into simpler single-parameter decisions that can be made independently, reducing overall computational complexity while maintaining effectiveness
3Device complexity
If greedy algorithms with monotonic cost functions are used, then computational complexity is reduced and provable optimality guarantees are achieved, but the solution space is limited
Solution Approach 1:
The patent maintains flexibility in caching strategies by allowing the cost functions to be any monotonic non-decreasing functions of queue sizes. This parameter flexibility enables the greedy algorithm to adapt to different network conditions and caching scenarios while maintaining polynomial-time complexity and provable optimality guarantees
Solution Approach 2:
The patent introduces dynamic adaptability through the use of monotonic cost functions that can change based on network conditions. The greedy algorithm dynamically adjusts caching decisions based on current queue sizes and cost parameters, maintaining versatility while achieving efficient computational complexity
Data Source
AI summary
The invention relates to a computer-implemented method, a corresponding a computer program product and a corresponding apparatus for distributing cached content in a network, the computer-implemented method comprising: collecting statistics regarding requests made and paths taken by the requests from source nodes to server nodes via intermediate nodes, the source nodes, intermediate nodes, and server nodes interconnected by edges having queues with respective queue sizes associated therewith, the requests including indications of content items to be retrieved; storing the content items at the server nodes; caching, by the intermediate nodes, the content items up to a caching capacity; and performing caching decisions that determine which of the content items are to be cached at which of the intermediate nodes, based upon costs that are monotonic, non-decreasing functions of the sizes of the queues.