Distributed Load Distribution in Data Networks
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Solution Overview
Problem
Existing load balancing solutions in data networks face challenges such as bottlenecks, increased latencies, and inefficient distribution of traffic across multiple load balancing devices, which can lead to uneven resource utilization and potential data loss when scaling up or down.
Innovation Solution
A method and system for load distribution in software-driven networks that involves a cluster master analyzing network and service node data to generate service policies, which are then used to dynamically distribute service requests among service nodes based on their capabilities and current loads, eliminating the need for DNS changes and minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple load balancing devices are stacked to handle increased traffic, then the load balancing capacity is improved, but the link between devices becomes a bottleneck and latencies increase
Solution Approach 1:
The patent segments the load balancing functionality into distributed service nodes that operate independently rather than stacking devices together. Each service node can handle traffic autonomously, eliminating the inter-device communication bottleneck and reducing latency while maintaining high load balancing capacity.
Solution Approach 2:
The patent transitions from a vertical stacking architecture to a horizontal distributed architecture. Instead of adding layers (stacking devices vertically), the system expands across multiple dimensions by distributing service nodes across the network, allowing parallel traffic handling without increasing path length.
2Ease of operation
If DNS is used to distribute traffic among load balancing devices, then traffic distribution is achieved, but the distribution is coarse-grained and not traffic-aware
Solution Approach 1:
The patent implements feedback mechanisms where service nodes continuously monitor their own load states and traffic patterns. This real-time feedback enables dynamic, fine-grained load distribution decisions that are traffic-aware, allowing the system to optimize resource utilization by routing traffic to the most appropriate service node based on current conditions rather than using coarse DNS-based distribution.
3Adaptability or versatility
If load balancing devices are scaled up or down, then service capacity is adjusted, but traffic flow may be disrupted and data loss may occur
Solution Approach 1:
The patent implements dynamic service node deployment where new service nodes can be added or existing ones removed without disrupting ongoing traffic flows. The distributed architecture allows for graceful scaling by dynamically registering or deregistering service nodes with the load balancing system, ensuring traffic is seamlessly redirected without interruption or data loss.
4Productivity
If physical chassis-based solutions are used for load balancing, then service requirements can be handled, but pre-provisioning of space, power, and price is required
Solution Approach 1:
The patent enables self-service provisioning where service nodes automatically register themselves with the load balancing system and begin handling traffic without requiring manual pre-provisioning of infrastructure resources. The distributed architecture allows service nodes to dynamically allocate their own computational resources and network connections, eliminating the need for complex pre-provisioning of chassis space, power, and pricing arrangements.
Data Source
AI summary
Provided are methods and systems for load distribution in a data network. A method for load distribution in the data network comprises retrieving network data associated with the data network and service node data associated with one or more service nodes. The method further comprises analyzing the retrieved network data and service node data. Based on the analysis, a service policy is generated. Upon receiving one or more service requests, the one or more service requests are distributed among the service nodes according to the service policy.