simplify complex algorithms

When implementing algorithms, prioritize simplicity and maintainability over premature optimization. Complex algorithms should be broken down into smaller, reusable components or replaced with simpler approaches when possible.

Key principles:

• Choose the simplest algorithm that meets requirements first, then optimize if needed
• Break complex algorithms into smaller, testable functions with clear responsibilities
• Consider computational complexity tradeoffs - sometimes a slightly less optimal but much simpler approach is better
• Avoid nested complexity layers that make code hard to understand and debug
• Use well-established algorithms and data structures rather than custom complex solutions

For example, instead of implementing complex bucket-level histogram manipulation:

// Complex approach - manipulating individual buckets
for bucketIndex, bucketVal := range resultHistogram.PositiveBuckets {
    if bucketVal <= 0 {
        continue
    }
    bucketStartVal := firstH.PositiveBuckets[bucketIndex]
    bucketDelta := bucketVal * deltaScale
    predictedAtStart := bucketStartVal - (bucketDelta / sampledInterval * durationToStart)
    // ... more complex logic
}

Prefer a simpler count-based approach:

// Simpler approach - use overall count for decisions
durationToZero := sampledInterval * (samples.Histograms[0].H.Count / resultHistogram.Count)
if durationToZero < durationToStart {
    durationToStart = durationToZero
}

This approach reduces complexity, improves maintainability, and often performs better while being easier to reason about and test.