Stateful Edge Services Dynamic Scaling via Consistent Hashing
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Solution Overview
Problem
Existing network services at the edge struggle with dynamic scaling, as adding or removing nodes disrupts traffic flows and requires manual intervention, leading to inefficiencies and potential service interruptions.
Innovation Solution
A dynamically scalable cluster of network nodes that can add or remove nodes while maintaining flow affinity through stateful services, utilizing a consistent hash function to distribute flows evenly across nodes and ensure continuous operation during cluster resizing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If nodes are added or removed from the cluster, then the processing power and scalability are improved, but the traffic flow continuity and service reliability deteriorate
Solution Approach 1:
The system dynamically adjusts the cluster configuration by allowing nodes to be added or removed while maintaining operational continuity. The consistent hash function dynamically remaps flows to new node configurations, enabling the cluster to adapt its structure without interrupting service.
Solution Approach 2:
The consistent hash function acts as an intermediary mechanism that bridges the gap between changing cluster topology and stable flow processing. It provides a deterministic mapping that ensures flows are continuously routed to appropriate nodes even as the cluster composition changes.
2Reliability
If manual intervention is used for node changes, then the flow affinity is maintained, but the operational complexity and time consumption increase
Solution Approach 1:
The system performs automatic flow remapping through the consistent hash function without requiring manual intervention. When nodes are added or removed, the system self-adjusts by computing new flow-to-node mappings, eliminating the need for operators to manually reconfigure traffic routing while maintaining flow affinity.
Solution Approach 2:
The system changes the operational parameters from manual configuration to automated computation. The consistent hash function automatically computes optimal flow distributions based on current cluster state, replacing manual affinity management with algorithmic parameter adjustment.
3Reliability
If nodes operate in active-standby mode, then the service reliability is improved, but the resource utilization and processing power decrease
Solution Approach 1:
The system transitions from static active-standby configuration to dynamic active-active operation. All nodes continuously process traffic flows based on consistent hash mapping, allowing the cluster to fully utilize available resources while maintaining reliability through distributed flow processing.
Solution Approach 2:
All nodes in the cluster continuously perform useful work by processing traffic flows rather than remaining in standby state. The consistent hash function ensures continuous distribution of flows across all available nodes, maximizing resource utilization while maintaining service continuity.
Data Source
AI summary
In order to enable dynamic scaling of network services at the edge, novel systems and methods are provided to enable addition of add new nodes or removal of existing nodes while retaining the affinity of the flows through the stateful services. The methods provide a cluster of network nodes that can be dynamically resized to handle and process network traffic that utilizes stateful network services. The existing traffic flows through the edge continue to function during and after the changes to membership of the cluster. All nodes in the cluster operate in active-active mode, i.e., they are receiving and processing traffic flows, thereby maximizing the utilization of the available processing power.