The Dynamic Consistency Evaluator

Introduction

The Dynamic Consistency Evaluator is a runtime engine designed to dynamically adjust the consistency model and execution strategy based on real-time system metrics and intent importance. This approach allows for adaptive transitions between different consistency levels, ensuring that the system maintains a balance between consistency, availability, and partition tolerance.

System Health and Intent Criticality

System Health is an input metric representing the current operational status of the system, including CPU load, memory pressure, and network partition status [1]. Intent Criticality, on the other hand, is a classification of a business operation’s importance, determining the minimum acceptable consistency floor. The evaluator consumes these primary decision inputs to select the appropriate execution strategy.

Execution Strategies

The evaluator supports three execution strategies: SYNC, ASYNC, and OPTIMISTIC. The SYNC strategy ensures strong consistency by blocking until the operation is consistent across all replicas. The ASYNC strategy, in contrast, allows for higher availability by executing operations without waiting for consistency. The OPTIMISTIC strategy provides a balance between consistency and availability by executing operations immediately and verifying consistency in the background.

Decision Matrix

The decision matrix for the Dynamic Consistency Evaluator is based on the System Health and Intent Criticality metrics. For example, if the System Health is degraded and the Intent Criticality is low, the evaluator may select the ASYNC strategy to prioritize availability. On the other hand, if the System Health is stable and the Intent Criticality is high, the evaluator may select the SYNC strategy to ensure strong consistency.

Implementation

The implementation of the Dynamic Consistency Evaluator can be achieved using Java Records, which provide shallow immutability and are recommended for representing intent payloads. The evaluator’s logic can be implemented using a simple if-else statement, as shown in the following code example:

public record IntentStatus(String intentId, SystemHealth health, long replicaLag, Criticality criticality) {}

public enum ExecutionStrategy { SYNC, ASYNC, OPTIMISTIC }

public class ConsistencyEvaluator {
    public ExecutionStrategy evaluate(IntentStatus status) {
        if (status.health() == SystemHealth.DEGRADED && status.criticality() != Criticality.HIGH) {
            return ExecutionStrategy.ASYNC;
        }
        if (status.replicaLag() > 500 && status.criticality() == Criticality.LOW) {
            return ExecutionStrategy.OPTIMISTIC;
        }
        return ExecutionStrategy.SYNC;
    }
}

Conclusion

The Dynamic Consistency Evaluator provides a flexible and adaptive approach to consistency modeling in distributed systems. By considering real-time system metrics and intent importance, the evaluator can select the most appropriate execution strategy to ensure a balance between consistency, availability, and partition tolerance.

Sources

[1] Shah, S. (2024). Understanding AI-Driven Adaptive Consistency in Distributed Systems. DZone.