Dynamic Load Distribution Controller for Multi-Tier PoP Platforms
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Solution Overview
Problem
Content Delivery Networks (CDNs) and distributed platforms face suboptimal resource usage and task performance due to equal distribution of requests across Points-of-Presence (PoPs), leading to high priority tasks being compromised by low priority tasks, and uneven resource utilization across different PoPs.
Innovation Solution
Implementing a dynamic load distribution system controlled by a controller that uses artificial intelligence and machine learning to adjust request distribution based on resource availability, pricing, performance requirements, and task priority, allowing for real-time reclassification of tasks and distribution across different tiers of the platform to maximize high priority task performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If requests are distributed equally across all PoPs, then resource utilization is simplified to manage, but high priority tasks compete with low priority tasks for resources leading to performance degradation
Solution Approach 1:
The patent applies local quality by differentiating request distribution strategies based on task priority. High priority tasks are routed to PoPs with available resources while low priority tasks are directed to PoPs with excess resources. This creates localized optimization at each PoP based on its resource state and the priority of incoming tasks, resolving the contradiction between simplified management and reliable performance.
Solution Approach 2:
The patent implements dynamic request distribution where the routing decision changes based on real-time resource availability at each PoP. The system continuously monitors resource states and adjusts task routing accordingly, allowing high priority tasks to receive preferential treatment when resources are constrained while utilizing excess resources at other PoPs. This dynamic adaptation resolves the contradiction by making the distribution mechanism flexible rather than static.
2Speed
If requests are routed to the closest PoP, then task completion speed is improved, but resource availability becomes insufficient to respond to all incoming requests
Solution Approach 1:
The patent extends the traditional single-dimension routing (closest PoP) by adding a second dimension of resource availability. Instead of routing solely based on geographic proximity, the system considers both distance and resource state, creating a two-dimensional routing decision space. This allows the system to maintain speed benefits of close PoPs while avoiding resource exhaustion by redirecting excess load to other PoPs with available capacity.
Solution Approach 2:
The patent introduces an intermediary routing mechanism that mediates between the requestor's preference for fast response and the PoP's resource constraints. This intermediary layer evaluates both proximity and resource availability, making intelligent routing decisions that balance speed and resource utilization. The intermediary prevents direct routing to overwhelmed PoPs while still directing requests to geographically close PoPs when resources permit.
3Productivity
If different PoPs are configured with different resources, then resource utilization efficiency is improved, but user experience varies across different locations
Solution Approach 1:
The patent dynamically changes the routing parameter from static geographic proximity to a composite parameter that includes both location and resource availability. This parameter change allows the system to leverage heterogeneous PoP configurations for improved efficiency while maintaining user experience consistency through intelligent task routing. Tasks are directed to PoPs where resource capabilities match task requirements, ensuring consistent performance regardless of user location.
Solution Approach 2:
The patent implements feedback mechanisms that monitor task completion performance and resource utilization across different PoPs. This feedback information is used to continuously optimize routing decisions, ensuring that users experience consistent performance even when accessing services from different locations with varying resource configurations. The system learns from performance data and adjusts routing to maintain quality of service standards.
Data Source
AI summary
A controller provides dynamic load distribution in a multi-tier distributed platform. The controller may receive a request at a first Point-of-Presence (“PoP”) with a first set of resources. The first PoP may be part of a distributed platform with several distributed PoPs at different network locations. The controller may classify the requested task with a priority, may determine resource availability, and may dynamically distribute the request by (i) providing the request to the first set of resources in response to classifying the task with a high first priority, and determining the availability of the first set of resources to be less than a threshold, and (ii) providing the request to a second PoP in response to classifying the task with a lower second priority, and determining the availability of the first set of resources to be less than the threshold.