Bandwidth Allocation Algorithm for Content Distribution Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current bandwidth allocation methods for content distribution networks are inefficient, relying on ad-hoc heuristics and requiring significant computational effort, especially when dealing with multiple trees and tree-like ordering constraints, which hinders equitable service provision across nodes.
Innovation Solution
A polynomial-time algorithm that solves single-link maximin optimization problems while relaxing tree-like ordering constraints, identifying bottleneck links and fixing variables at their lexicographic maximin value, providing an ordered list of links for capacity expansion planning and efficient bandwidth allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ad-hoc heuristics and experience-based methods are used for bandwidth allocation, then implementation is simple, but allocation efficiency and equity are poor
Solution Approach 1:
The patent transforms the bandwidth allocation problem into a lexicographic maximin optimization problem by changing the allocation parameters and objective function. Instead of using heuristic rules, it defines a mathematical framework where bandwidth allocation variables are optimized to maximize the minimum service quality across all programs, subject to link capacity constraints. This parameter transformation enables systematic optimization while maintaining computational tractability through polynomial-time algorithms.
2Measurement precision
If optimization-based methods with bisection search are used, then allocation accuracy is improved, but computational effort becomes non-polynomial
Solution Approach 1:
The patent segments the complex network bandwidth allocation problem into independent single-link optimization subproblems. By decomposing the overall problem into smaller, manageable units (one for each link in the network), it avoids the need for non-polynomial bisection search while maintaining allocation accuracy. Each subproblem can be solved efficiently in polynomial time, and the solutions are combined to achieve the global optimal allocation.
Solution Approach 2:
The patent enables each link to determine its own optimal bandwidth allocation independently through the lexicographic maximin approach. Each link's allocation decision is self-contained and does not require iterative coordination with other links, eliminating the computational overhead of bisection search while ensuring that the overall network allocation achieves equity and optimality.
3Reliability
If equitable bandwidth allocation is achieved through lexicographic maximin optimization, then service fairness is improved, but computational complexity increases
Solution Approach 1:
The patent changes the optimization parameters from traditional utility-maximization variables to lexicographic maximin variables that directly represent service quality levels. By parameterizing the problem in terms of minimum service guarantees and using a lexicographic ordering of programs, it achieves service fairness while maintaining polynomial-time solvability through closed-form solutions for each link's allocation.
Data Source
AI summary
A method is presented for determining bandwidth allocations in a content distribution network that comprises multiple trees, where the root of each tree has a server that broadcasts multiple programs throughout the tree. Each network link has limited capacity and may be used by one or more of these trees. The allocation problem is formulated as an equitable resource allocation problem with a lexicographic maximin objective function that attempts to provide equitable service performance for all requested programs at the various nodes. The constraints include link capacity constraints and tree-like ordering constraints imposed on each of the programs. The algorithm provides an equitable solution in polynomial time for wide classes of performance functions. At each iteration, the algorithm solves single-link maximin optimization problems while relaxing the ordering constraints, selects a bottleneck link and fixes various variables at their optimal value.