Layer 4 Load Balancing with Passive Server Load Inference
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Solution Overview
Problem
Layer 4 load balancers in data centers struggle with suboptimal workload distribution due to lack of awareness of instantaneous server load states, leading to resource overload or starvation, especially in heterogeneous virtualized environments, and require manual weight configuration, increasing management overhead.
Innovation Solution
Implementing a load balancing strategy that passively learns server processing capacities using network features and Kalman filters to infer instant load states, dynamically distributing workloads based on Shortest Expected Delay (SED) scores without manual configuration, using network observations like flow durations and queue lengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If layer 4 load balancers use traditional load balancing methods, then the system is simple to operate, but the workload distribution is suboptimal and does not reflect instantaneous server load states
Solution Approach 1:
The load balancer passively learns server processing capacities and infers instant load states by observing network features (flow durations, queue lengths) without requiring manual configuration or additional control planes. The system serves itself by automatically adapting to heterogeneous virtualized environments through passive learning and inference mechanisms
Solution Approach 2:
The load balancer continuously observes network features from data plane traffic and uses this feedback to infer server load states and update its understanding of server processing capacities. This closed-loop feedback mechanism enables dynamic adaptation to changing workload conditions without manual intervention
2Productivity
If manual weight configuration is used for load balancing, then workload distribution can be controlled, but management overhead increases
Solution Approach 1:
The system eliminates manual weight configuration by automatically learning server processing capacities through passive observation of network features. The load balancer infers appropriate weighting based on observed flow durations and queue lengths, completely removing the need for manual intervention in workload distribution control
Solution Approach 2:
The patent replaces manual mechanical configuration (setting weights) with an automated inference system that uses Kalman filters and network feature observation. This substitution transforms a manual control mechanism into an automated intelligent system that adapts dynamically
3Device complexity
If layer 4 load balancers lack awareness of instantaneous server load states, then the system is simpler, but resource overload or starvation occurs
Solution Approach 1:
The load balancer observes network features (flow durations, queue lengths) from data plane traffic and uses this feedback to infer instantaneous server load states. This enables the system to make reliability-aware load balancing decisions while maintaining layer 4 simplicity
Solution Approach 2:
The patent introduces an intermediary inference layer that translates observable network features into estimates of server load states. The Kalman filter acts as an intermediary that processes noisy observations to produce reliable estimates of instantaneous server capacity and queue length
4Productivity
If heterogeneous virtualized environments are deployed, then resource utilization can be optimized, but load balancing becomes more difficult without manual configuration
Solution Approach 1:
The system dynamically adjusts load balancing parameters (weights) based on observed network features and inferred server capacities. Instead of static manual configuration, the parameters change automatically in response to changing conditions in heterogeneous virtualized environments
Solution Approach 2:
The load balancer continuously updates its understanding of server processing capacities and queue lengths based on observed flow durations and network features. These dynamic parameter changes enable optimal workload distribution across heterogeneous resources without manual reconfiguration
Data Source
AI summary
Load aware load balancing may be provided. Flow duration data associated with a plurality of flows associated with a plurality of servers may be obtained. Then a plurality of queue lengths respectively associated with the plurality of servers may be obtained. Next, a Shortest Expected Delay (SED) score may be determined for each of the plurality of servers based on the flow duration data and the plurality of queue lengths. A flow may then be assigned to a one of the plurality of servers having the lowest SED score.