Joint Routing and Caching Optimization for Content Delivery
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Solution Overview
Problem
Existing approaches to routing in computer networks are unable to effectively jointly handle routing and caching, leading to inefficiencies and high computational complexity due to the NP-hard nature of jointly optimizing these decisions.
Innovation Solution
The implementation of a method that jointly determines caching and routing decisions using a convex relaxation approach, allowing for adaptive and distributed network management that considers caching and routing parameters to optimize content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If joint optimization of routing and caching decisions is implemented, then content delivery efficiency is improved, but computational complexity increases due to NP-hard nature of the problem
Solution Approach 1:
The patent transforms the discrete, combinatorial routing and caching optimization problem into a continuous convex optimization problem by changing the parameter space. Instead of making discrete decisions about which nodes to cache at and which paths to route through, the invention uses continuous variables representing caching probabilities and routing fractions, enabling the use of efficient convex optimization algorithms while maintaining provable performance guarantees接近 the optimal discrete solution
Solution Approach 2:
The patent introduces Lagrange multipliers as intermediary variables to handle the coupling between routing and caching decisions. These multipliers act as mediators that coordinate the joint optimization by enforcing consistency constraints between cache placement and path selection, allowing the complex joint problem to be decomposed into manageable subproblems that can be solved iteratively
2Measurement precision
If centralized control is used to adapt caching and routing decisions, then optimality guarantees are achieved, but system complexity and communication overhead increase
Solution Approach 1:
The patent segments the centralized optimization problem into distributed subproblems solved by individual network nodes. Each node independently computes its own caching and routing decisions based on local information and Lagrange multiplier signals from neighbors, eliminating the need for a single centralized controller while maintaining the optimality guarantees of the joint optimization approach
Solution Approach 2:
The patent implements feedback mechanisms where nodes exchange Lagrange multiplier information with neighboring nodes to coordinate their decisions. This feedback loop allows distributed nodes to converge to the optimal solution by adjusting their caching and routing strategies based on the dual variables received from the network, achieving centralized-like optimality through distributed feedback
3Ease of operation
If caching decisions are made independently from routing decisions, then system simplicity is maintained, but content delivery performance deteriorates
Solution Approach 1:
The patent merges the previously independent caching and routing decision-making processes into a unified joint optimization framework. By combining these decisions and optimizing them simultaneously using convex optimization, the system achieves superior content delivery performance compared to independent optimization, while the convex formulation keeps the implementation tractable and the system manageable
Data Source
AI summary
Embodiments solve a problem of minimizing routing costs by jointly optimizing caching and routing decisions over an arbitrary network topology. Embodiments solve an equivalent caching gain maximization problem, and consider both source routing and hop-by-hop routing settings. The respective offline problems are non-deterministic polynomial time (NP)-hard. Nevertheless, embodiments show that there exist polynomial time approximation methods producing solutions within a constant approximation from the optimal. Embodiments herein include distributed, adaptive networks, computer methods, systems, and computer program products that provide guarantees of routing cost reduction. Simulation is performed over a broad array of different topologies. Embodiments reduce routing costs by several orders of magnitude compared to existing approaches, including existing approaches optimizing caching under fixed routing.