SD-WAN Hub Cluster Scaling via Traffic Analysis
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Solution Overview
Problem
Network designers face challenges in determining the optimal number of hubs for SD-WAN hub-clustering, often leading to over-provisioning, which results in unsatisfied customers, especially in cloud deployments and IaaS environments, due to the lack of dynamic scaling solutions based on traffic patterns.
Innovation Solution
A method for dynamically scaling a hub cluster in SD-WAN by analyzing current and historical traffic statistics using a controller that includes a traffic statistics storage, a learning engine, and a decision-making engine to identify real-time, cyclical, and seasonal load fluctuations, adjusting the number of hubs accordingly to match predicted traffic loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network operators over-provision hubs in SD-WAN hub clusters, then network reliability and capacity are improved, but device complexity and resource waste increase
Solution Approach 1:
The patent implements dynamic hub cluster scaling that automatically adjusts the number of active hubs based on real-time traffic load analysis. The controller continuously monitors traffic statistics and scales the hub cluster size up or down to match actual demand, replacing static over-provisioning with adaptive dynamic configuration. This resolves the contradiction by maintaining reliability through adequate provisioning while avoiding the complexity of manually managing excessive hub resources.
Solution Approach 2:
The system employs self-service mechanisms where the SD-WAN controller autonomously monitors traffic patterns, analyzes load fluctuations, and automatically adjusts hub cluster configuration without manual intervention. The controller uses built-in traffic statistics collection and machine learning capabilities to self-determine optimal scaling decisions, eliminating the need for complex manual provisioning while maintaining network reliability.
2Reliability
If network operators over-provision hubs in SD-WAN hub clusters, then network capacity is improved, but loss of substance increases
Solution Approach 1:
The patent implements dynamic hub cluster scaling that continuously adapts the active hub count to match real-time traffic demand. During low-utilization periods, excess hubs are deactivated, reducing bandwidth consumption. During peak periods, hubs are activated to maintain capacity. This dynamic adjustment resolves the contradiction by ensuring network capacity is available when needed while minimizing bandwidth waste during low-demand periods.
Solution Approach 2:
The system dynamically discards (deactivates) excess hub resources during low-utilization periods and recovers (reactivates) them when traffic demand increases. The controller monitors traffic statistics and selectively activates or deactivates hubs based on current load requirements, allowing the network to recover unused bandwidth resources while maintaining capacity availability when needed.
3Device complexity
If manual methods are used to determine hub cluster size, then device complexity is reduced, but adaptability worsens
Solution Approach 1:
The patent implements self-service automation where the SD-WAN controller autonomously performs traffic analysis, load fluctuation detection, and hub scaling decisions without manual intervention. The system automatically collects traffic statistics from hubs and branch sites, analyzes patterns using machine learning, and dynamically adjusts cluster configuration. This resolves the contradiction by providing high adaptability to changing traffic conditions while keeping device complexity manageable through automated decision-making.
Solution Approach 2:
The system establishes continuous feedback loops where the controller monitors traffic statistics from hubs and branch sites, analyzes load fluctuations, and adjusts hub cluster configuration accordingly. This closed-loop feedback mechanism enables the system to automatically adapt to changing network conditions without manual intervention, achieving high adaptability while maintaining manageable complexity through automated control.
4Adaptability or versatility
If dynamic scaling based on traffic statistics is implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal SD-WAN controller that performs multiple functions: traffic statistics collection, machine learning analysis, load fluctuation detection, and hub scaling management. By consolidating these diverse functions into a single multi-functional controller, the system achieves high adaptability through dynamic scaling while avoiding the complexity that would result from distributing these functions across multiple separate systems.
Solution Approach 2:
The system employs self-service automation where the controller autonomously performs traffic analysis, pattern recognition, and scaling decisions without manual intervention. This automation reduces the operational complexity of implementing dynamic scaling, as the system self-manages the complexity of monitoring and adjusting hub configurations based on real-time traffic conditions.
Data Source
AI summary
Some embodiments of the invention provide a method of dynamically scaling a hub cluster in a software-defined wide area network (SD-WAN) based on particular traffic statistics, the hub cluster being located in a datacenter of the SD-WAN and allowing branch sites of the SD-WAN to access resource of the datacenter by connecting to the hub cluster. A controller of the SD-WAN receives, from the hub cluster, traffic statistics centrally captured at the hub cluster. The controller then analyzes these statistics to identify traffic load fluctuations, and determines that a number of hubs in the hub cluster should be adjusted based on the identified fluctuations. The controller adjusts the number of hubs in the hub cluster based on the determination.