Consistent Hashing for Stateful Flow Autoscaling
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Solution Overview
Problem
Existing systems face challenges in efficiently scaling infrastructure to manage varying network traffic flows without disrupting existing connections, particularly in multi-path and stateful processing environments, where forward and reverse flows can land in different nodes, leading to functional breakdowns.
Innovation Solution
Implementing a system with a consistent hashing engine that computes S-Node indices using the Consistent_Hash function, coupled with a cloud resource inventory engine and orchestration service, to ensure that forward and reverse flows are steered to the same service node, enabling rapid and seamless autoscaling of nodes and services to match traffic demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If infrastructure is scaled by adding new nodes to handle increasing network traffic flows, then the system capacity and handling capability are improved, but existing flows may be disrupted or dropped when load balancing distributes them to different nodes
Solution Approach 1:
The system implements feedback mechanisms where nodes report their current flow assignments and capacity status to a centralized coordinator. This feedback loop enables the system to dynamically adjust flow distribution as nodes are added or removed, ensuring that existing flows are redirected to appropriate nodes without disruption while maintaining optimal system capacity utilization.
Solution Approach 2:
A centralized flow coordinator acts as an intermediary between network nodes and flow traffic. This mediator maintains a global view of flow assignments and node capacities, orchestrating flow redistribution when infrastructure changes occur. The coordinator ensures that forward and reverse flows remain paired by managing the assignment state across all nodes, preventing flow disruption during scaling operations.
2Productivity
If maximum capacity infrastructure is deployed to handle peak traffic demands, then the system can accommodate high traffic volumes, but resource utilization efficiency decreases during low traffic periods
Solution Approach 1:
The system dynamically adjusts its operational configuration based on real-time traffic conditions and node availability. When traffic demand increases, additional nodes are activated and integrated into the flow distribution system. When demand decreases, nodes can be deactivated or repurposed, allowing the system to scale its resource consumption matching actual workload requirements rather than maintaining static maximum capacity.
Solution Approach 2:
The system changes operational parameters such as the number of active nodes, flow assignment policies, and load balancing configurations based on traffic demand patterns. During peak periods, parameters are adjusted to utilize maximum capacity across all available nodes. During low traffic periods, parameters are modified to reduce active resources, optimizing the balance between peak handling capability and ongoing resource efficiency.
3Adaptability or versatility
If load balancing is used to distribute flows across multiple nodes, then system scalability is improved, but forward and reverse flows may land in different nodes causing functional breakdowns
Solution Approach 1:
The system performs preliminary actions by pre-establishing flow pairing information and node assignment states before traffic flows are distributed. When a flow is assigned to a node, the system proactively records the pairing relationship and ensures that both forward and reverse directions are routed through the same node. This preliminary tracking prevents flow pairing issues even as the system scales to multiple nodes.
Solution Approach 2:
Nodes provide feedback about their current flow assignments and capacity status to the centralized coordinator. This feedback mechanism enables the system to maintain awareness of flow pairing relationships across the distributed infrastructure. When scaling operations occur, the coordinator uses this feedback information to ensure that new flows are assigned to appropriate nodes and that existing flow pairs remain intact, preserving reliability during scalability operations.
Data Source
AI summary
Disclosed is a rapid node provisioning system comprising a cloud resource inventory engine, an orchestration service, a metrics engine, and a node provisioning engine coupled to the orchestration service through a datapath. The metrics engine collects metrics for components of the datapath and provides the metrics to the cloud resource inventory engine, which informs communications to the orchestration service, and the node provisioning engine autoscales components of the datapath.