Optimization Service for Multi-Session Network Congestion
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Solution Overview
Problem
Multi-session content delivery applications, such as P2P networks, face challenges in optimizing content source selection and session throughput, leading to network congestion and suboptimal performance due to misalignment with ISP network environments, resulting in increased bandwidth consumption and costs.
Innovation Solution
An optimization service discovery method that includes caching and acceleration, content origination, network prioritization, peer discovery, bootstrapping, QoS arbitration, and NAT traversal services to manage network resources and improve application performance by optimizing content delivery and session management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-session applications use application algorithms for content source selection and session throughput optimization, then application performance and fault tolerance are improved, but network congestion occurs in ISP network parts and resource utilization is suboptimal
Solution Approach 1:
The patent introduces an optimization service as an intermediary between multi-session applications and ISP networks. This service receives application algorithms, translates them into network-friendly formats, and mediates the interaction to prevent network congestion while maintaining application performance. The optimization service acts as a buffer that converts application-level optimization requests into network-aware decisions.
Solution Approach 2:
The patent changes the parameters of content delivery by introducing network-aware selection criteria. Instead of purely application-based algorithms, the system incorporates network conditions, ISP policies, and bandwidth allocation parameters into the content source selection process. This parameter transformation enables alignment between application optimization goals and network capacity constraints.
2Productivity
If multi-session applications optimize content source selection according to application algorithms, then application performance is improved, but misalignment with ISP network environment leads to increased bandwidth consumption and costs
Solution Approach 1:
The optimization service implements feedback mechanisms that monitor network conditions, bandwidth usage, and ISP policies. This feedback is fed back into the content source selection process, enabling dynamic adjustment of delivery parameters. The system continuously learns from network behavior patterns and optimizes bandwidth allocation to match actual network conditions rather than relying on static application algorithms.
Solution Approach 2:
The patent introduces dynamic adaptation to the content delivery system. The optimization service continuously adjusts content source selection and bandwidth allocation based on real-time network conditions, ISP policies, and traffic patterns. This dynamic approach replaces static application algorithms with flexible, context-aware decision-making that adapts to changing network environments.
3Productivity
If P2P networks scale to very large number of participating peers, then delivery of very large content objects to large number of subscribers is enabled, but storage and bandwidth resources require significant scaling
Solution Approach 1:
The patent segments the content delivery function by separating application logic from network resource management. The optimization service handles resource allocation and bandwidth management independently from the peer-to-peer content delivery mechanism. This segmentation allows the P2P network to scale peer count without proportionally increasing the burden on individual peers for resource management.
Solution Approach 2:
The optimization service serves as an intermediary that manages the resource allocation between large numbers of peers and content sources. It consolidates bandwidth management tasks and provides optimized routing decisions that reduce the total storage and bandwidth resources required across the network, enabling efficient scaling to very large peer counts.
Data Source
AI summary
An optimization service discovery method for optimizing data transmission by multi-session applications, includes: receiving an optimization service lookup query from one of a plurality of user clients in a network, each of said user clients executing a multi-session application; and identifying an optimization service and responding to the query with a network address of one or more servers providing said optimization service.