Dynamic Load Balancing via Flow Characteristic Scoring
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Solution Overview
Problem
Traditional load balancing techniques struggle to efficiently handle the varying processing needs of different data communications in telecommunications networks, leading to issues such as overloading, unbalanced load distribution, and increased latency.
Innovation Solution
A system for dynamic load balancing among processors based on flow characteristics of data communications, which performs deep packet inspection to determine flow characteristic scores and uses these scores in conjunction with current load scores to dynamically select the most suitable processor for processing data flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional load balancing techniques (hash function, round-robin, low resource utilization identification) are used, then the system can maintain basic load distribution, but the system cannot efficiently handle the varying processing needs of different data communications types
Solution Approach 1:
The patent applies local quality by assigning different processing characteristics to different data flows based on their specific requirements. The load balancer evaluates individual flow characteristics (bandwidth, latency, packet loss sensitivity) and routes each flow to processors best suited for those specific requirements, rather than treating all flows uniformly.
Solution Approach 2:
The patent implements dynamics by continuously monitoring processor performance metrics and flow characteristics, then dynamically adjusting load distribution decisions in real-time. The system adapts to changing conditions by updating routing based on current processor utilization, queue depths, and flow requirements, rather than using static load balancing rules.
2Ease of operation
If uniform load distribution is applied across all processors, then the system can simplify load balancing logic, but the system causes overloading of some processors and underutilization of others when handling diverse data communications
Solution Approach 1:
The patent applies feedback by continuously monitoring processor performance metrics (utilization, queue depth, processing speed) and using this information to make informed load balancing decisions. The load balancer receives feedback from processors about their current state and adjusts routing accordingly, creating a closed-loop system that maintains optimal load distribution.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting load distribution based on varying processor and flow parameters. The system monitors multiple parameters (processor utilization, flow bandwidth requirements, latency sensitivity) and changes routing decisions based on the combined state of these parameters, rather than using a single static load balancing rule.
3Stability of the object's composition
If the system processes all data communications with the same resource allocation, then the system can maintain consistent processing policies, but the system increases latency for time-sensitive communications and wastes resources on low-priority communications
Solution Approach 1:
The patent applies local quality by tailoring processing resource allocation to the specific requirements of each data flow. Time-sensitive flows receive priority routing to processors with lower current loads and faster processing capabilities, while less time-sensitive flows can be routed to processors with higher utilization, ensuring each flow receives appropriate resource allocation for its specific needs.
Data Source
AI summary
Systems and methods are provided for dynamically balancing workloads among data flow processors based on flow characteristics of data flows. The system determines a flow characteristic score associated with the data flow based on results from deep packet inspection of data packets in the data flow. The system further determines load scores associated with a plurality of processors. A load balancer of the system selects a processor among the plurality of processors for processing the data flow based on the flow characteristic score associated with the data flow. The system monitors workload levels of respective processors by periodically receiving performance data associated with the respective processors. The performance data includes flow characteristic scores of data flows being processed. Based on the current status of the workload distribution among the plurality of processors, the system regenerates load scores and re-balancing the workloads by re-distributing data flows to the respective processors.