Skewed Metrics: Impact Of Workload Collection On Mean Values

Have you ever wondered if the way we collect metrics for a workload can actually distort the results? It's a fascinating question, and the answer is a resounding yes. In this article, we'll dive into the reasons why collecting metrics can lead to skewed mean metric values, particularly when using tools like PerfSpect and workloads such as stress-ng. We'll explore the problem, look at an example, and discuss potential solutions to ensure we're getting accurate performance data. So, let's get started!

The Challenge: Skewed Mean Metric Values

When we talk about collecting metrics for a workload, we're essentially gathering data points over a specific period to understand the performance characteristics of that workload. For example, we might use PerfSpect to monitor CPU utilization, memory usage, or disk I/O while a stress-ng workload is running. The goal is to get a comprehensive picture of how the system behaves under load.

However, there's a catch. The timing of our metric collection can significantly impact the results, especially the calculated mean (average) values. The issue arises because the last sample of metrics we collect might occur after or during the very end of the workload execution. Think about it: if the workload is winding down or has already finished, the final metric readings might be drastically different from the values recorded during peak activity. These final, potentially inaccurate values can then skew the mean value, giving us a misleading representation of the workload's overall performance.

Why does this happen? Imagine you're tracking the speed of a race car. If you take a measurement just as the car crosses the finish line and begins to slow down, that final speed reading will be much lower than the car's average speed during the race. Similarly, in workload metric collection, the final sample can drag down the mean, especially if the collection period is relatively short. This is a crucial point to grasp, as it highlights the importance of careful consideration when designing our metric collection strategies.

To make this concept crystal clear, let's consider a practical example. We will see how a seemingly small timing difference can lead to significant distortions in our performance metrics. We will explore potential solutions to mitigate this problem. This will ensure that the metrics we collect are truly representative of the workload's behavior. This will help us to gain a reliable understanding of system performance.

A Practical Example with PerfSpect and stress-ng

Let's illustrate this issue with a concrete example using PerfSpect and the stress-ng workload generator. Suppose we want to evaluate the CPU performance of our system under a moderate load. We decide to use stress-ng to simulate CPU stress and PerfSpect to collect CPU utilization metrics. We run the following command:

perfspect metrics -- stress-ng --cpu 0 --cpu-load 60 --timeout 30

In this command:

• perfspect metrics tells PerfSpect to collect metrics.
• -- stress-ng --cpu 0 --cpu-load 60 instructs stress-ng to load CPU 0 with a 60% load.
• --timeout 30 sets both PerfSpect and stress-ng to run for 30 seconds.

Now, let's consider what might happen during those 30 seconds. Stress-ng starts loading the CPU, and PerfSpect begins collecting metrics. Ideally, PerfSpect would collect data evenly throughout the 30-second interval. However, the final sample of metrics collected by PerfSpect might occur slightly after stress-ng has finished its 30-second run. Or, it might occur during the last moments when stress-ng is winding down its CPU load.

The Problem: If the collection period is short (e.g., 30 seconds), we'll have a relatively small number of metric values. This means that the final, potentially lower value can have a disproportionate impact on the calculated mean. Imagine PerfSpect collects ten samples, and the first nine show a CPU utilization of 60%, but the last one shows 20% because stress-ng is ending. The mean CPU utilization would be significantly lower than the actual sustained load during the workload's execution.

This is particularly problematic because it can lead to inaccurate conclusions about the system's performance. We might underestimate the true CPU utilization or other resource consumption metrics, which could affect our capacity planning, performance tuning, and troubleshooting efforts. The key takeaway here is that even a single skewed data point can significantly distort the overall picture when dealing with short collection windows and relatively small sample sizes.

The impact of this skewed mean can ripple through our analysis. We might make incorrect decisions based on the faulty data. For instance, we might think our system has more headroom than it actually does or that a particular optimization has had a greater effect than it truly has. Therefore, recognizing and addressing this issue is paramount for accurate performance monitoring.

In the next section, we will delve into potential solutions to mitigate this problem, ensuring that our metric collection process yields more reliable and representative data. We'll explore strategies for excluding potentially skewed data points and other techniques to improve the accuracy of our performance metrics.

Potential Solutions: Excluding the Final Metric Set

So, what can we do to address the problem of skewed mean metric values? Fortunately, there are several strategies we can employ to improve the accuracy of our workload performance data. The easiest and perhaps most effective solution is to exclude the final set of metrics from the summary statistics. This approach is based on the idea that the last few data points are the most likely to be affected by the workload's termination phase. By removing these potentially inaccurate values, we can get a more representative picture of the workload's sustained performance.

How does this work in practice? The specific implementation will depend on the metric collection tool you're using. However, the general principle is the same: identify and discard the final data points before calculating summary statistics like the mean, median, or standard deviation. Here’s a breakdown of how this might work:

1. Identify the End: Determine the point at which the workload is expected to terminate or begin winding down. This might be based on a predefined timeout, a specific event, or a manual signal.
2. Buffer Period: Establish a buffer period after the expected end of the workload. This buffer represents the time window during which metric samples are likely to be skewed.
3. Exclude Data: Discard any metric samples collected during the buffer period. These samples are considered potentially unreliable and should not be included in the final analysis.
4. Calculate Statistics: Calculate the summary statistics (mean, median, etc.) using only the remaining metric samples.

For example, in our PerfSpect and stress-ng scenario, we might exclude the last 5 seconds of data collection. If PerfSpect collects metrics every second, this would mean discarding the final five samples. This simple step can significantly reduce the impact of skewed values on the calculated mean.

Why is this effective? By excluding the final metric set, we're essentially focusing on the period during which the workload was running at its peak or sustained level. This gives us a more accurate representation of the system's performance under load. It's like trimming the edges of a photograph to remove distracting elements and focus on the main subject.

However, there are a few considerations to keep in mind when using this approach. First, we need to choose an appropriate buffer period. If the buffer is too short, we might not exclude all the skewed data. If it's too long, we might discard valuable information. Second, this method assumes that the skewed values occur primarily at the end of the collection period. This might not always be the case, especially in more complex workloads with varying phases of activity. Third, we must consider that removing data can impact the statistical power of the analysis, particularly with short collection windows and small sample sizes.

In the following sections, we will explore other potential solutions and discuss the trade-offs associated with each approach. We'll also touch on more advanced techniques for handling skewed data and ensuring the accuracy of our workload performance metrics.

Further Considerations and Advanced Techniques

While excluding the final metric set is a simple and effective solution for many scenarios, it's not a one-size-fits-all approach. In some cases, more sophisticated techniques may be necessary to handle skewed data and ensure accurate workload performance metrics. Let's explore some further considerations and advanced techniques.

1. Identifying and Handling Outliers:

Excluding the final metric set addresses the specific issue of skewed values caused by workload termination. However, outliers can occur at any point during the collection period due to various factors, such as transient system events or interference from other processes. Identifying and handling these outliers can further improve the accuracy of our metrics.

There are several statistical methods for outlier detection, such as:

• Z-score: Measures how many standard deviations a data point is from the mean. Data points with a Z-score above a certain threshold (e.g., 3 or -3) are considered outliers.
• Interquartile Range (IQR): Defines outliers as data points that fall below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR, where Q1 and Q3 are the first and third quartiles, respectively.
• Box Plots: A graphical representation of data that visually highlights outliers.

Once outliers are identified, we can choose to remove them from the dataset or apply transformations to reduce their impact. However, it's crucial to exercise caution when removing data, as we don't want to discard legitimate values that reflect the true behavior of the system.

2. Using Median Instead of Mean:

The mean is sensitive to extreme values, while the median is more robust. The median represents the middle value in a dataset, so it's less affected by outliers or skewed values. In situations where we suspect significant data skewness, using the median as a measure of central tendency can provide a more accurate representation of the workload's performance.

3. Longer Collection Periods:

The impact of a single skewed data point is more significant when the collection period is short. Increasing the collection period allows us to gather more samples, diluting the effect of any individual outlier. This approach can be particularly useful when dealing with workloads that have variable performance characteristics over time.

4. Workload Stabilization:

Before collecting metrics, it's often beneficial to allow the workload to stabilize. Many workloads exhibit a