CPU consumption 2 tests aggregation

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Summary

The problem at hand involves aggregating CPU consumption data from two separate logs collected at different times on a Jetson device. The goal is to estimate the system’s overall load for capacity planning. Two approaches are considered: time-aligned aggregation and sum of per-service “5-minute peaks”. The correct approach must be determined to accurately estimate the system’s load.

Root Cause

The root cause of the issue is the lack of synchronization between the two logs, making it challenging to directly compare and aggregate the data. The different timestamps and non-overlapping services in each log add to the complexity of estimating the system’s overall load.

Why This Happens in Real Systems

This issue occurs in real systems due to:

  • Limited logging capabilities: Logs may not be collected simultaneously or at the same frequency.
  • Diverse system usage: Different services may be running at different times, making it difficult to capture a comprehensive view of system load.
  • Resource constraints: Collecting and storing large amounts of log data can be computationally expensive and storage-intensive.

Real-World Impact

The impact of inaccurate load estimation can be significant, leading to:

  • Inadequate capacity planning: Insufficient resources may be allocated, resulting in performance issues and downtime.
  • Overprovisioning: Excessive resources may be allocated, leading to wasted resources and increased costs.
  • Poor decision-making: Inaccurate load estimates can inform suboptimal decisions regarding system design, optimization, and maintenance.

Example or Code

import pandas as pd

# Sample log data
log1 = pd.DataFrame({'timestamp': [1, 2, 3], 'service1': [10, 20, 30], 'service2': [5, 10, 15]})
log2 = pd.DataFrame({'timestamp': [4, 5, 6], 'service3': [20, 30, 40], 'service4': [10, 20, 30]})

# Time-aligned aggregation
log1_resampled = log1.resample('1s', on='timestamp').mean()
log2_resampled = log2.resample('1s', on='timestamp').mean()
combined_log = log1_resampled.join(log2_resampled, how='outer')

# Sum of per-service "5-minute peaks"
log1_peaks = log1.rolling('5min').max()
log2_peaks = log2.rolling('5min').max()
combined_peaks = log1_peaks.sum() + log2_peaks.sum()

How Senior Engineers Fix It

Senior engineers address this issue by:

  • Carefully evaluating the logging setup to ensure that logs are collected at a sufficient frequency and with adequate detail.
  • Implementing data synchronization techniques, such as time-aligned aggregation, to combine logs from different sources.
  • Using statistical methods, like rolling averages and percentiles, to estimate system load and account for variability.
  • Considering the worst-case scenario when estimating system load, while also being mindful of the potential for overly conservative** estimates.

Why Juniors Miss It

Junior engineers may overlook this issue due to:

  • Lack of experience with logging and data analysis.
  • Insufficient understanding of statistical concepts and their application to system load estimation.
  • Failure to consider the implications of non-synchronized logs and different system usage patterns.
  • Overreliance on simplistic approaches, such as summing per-service peaks, without fully considering the potential consequences.

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