The Throughput Run Chart is a performance monitoring tool that visualizes throughput over time. Unlike the histogram, which shows a static distribution of flow values over a period, Flow Monitoring allows you to see how flow changes over time, often in real time. This type of chart is particularly useful for monitoring the performance of a system or application over a period of time and spotting significant trends or fluctuations.
Throughput: Throughput represents the amount of work accomplished by a system over a given period of time. This may include the number of transactions, requests processed, or data transferred per second, minute, hour, etc.
Evolution over time: Throughput tracking shows how these values change over time, providing a dynamic, temporal view of performance.
Operation and Usefulness
The Throughput Run Chart is particularly useful for:
Track trends in flow: It allows you to visualize gradual or abrupt changes in flow over time, revealing increasing, decreasing trends, or periods of stability.
Detect anomalies and spikes: By tracking flow rates, it becomes easy to spot anomalies, such as sudden spikes (rapid increase or decrease in throughput) that could signal unexpected events, performance issues, or changes in performance. behavior of the system.
Optimize processes: The run chart can help identify periods when the system is operating optimally or suboptimal, allowing decisions to be made to adjust resources or improve efficiency.
Measure the impact of changes: If an update or modification is applied to a system, the run chart can be used to monitor the impact of this change on throughput over time.Comment lire un Throughput Run Chart ?
The Throughput Run Chart is in the form of a line drawn on a graph where each point represents the throughput observed at a given time.
Here are the key elements to understand when reading:
Horizontal axis (x): This axis represents time, generally divided into regular intervals, depending on the chosen observation period.
Vertical axis (y): This axis represents the throughput.
Trend line: This allows you to visualize the evolution of the flow rate over time, whether it is an increase, a decrease or a regular variation.
Key points to observe:
Spikes (high flow values): If the flow suddenly increases, this may indicate a period of high activity or peak demand.
Dips (low throughput values): Sudden drops may signal reduced activity or a performance issue (such as overload or slowdown).
Plateaus: If the flow line is relatively stable, this means that the system is maintaining a constant flow rate, which can be interpreted as a good sign of stability, unless the plateau is at low flow levels, which could indicate a problem.Utilité dans Wiveez
The Throughput Run Chart in Wiveez allows users to:
Monitor performance in real time: By continuously observing throughput, users can quickly respond to performance variations and adjust resources accordingly.
Diagnose problems: Quickly identifying spikes or dips in throughput helps locate bottlenecks or periods of overload in a system.
Predict future trends: By studying the evolution of flow rates over a long period of time, users can anticipate future resource needs or prepare adjustments to maintain optimal flow.
The chart
Filters
Predictability Analysis
The Predictability analysis makes it possible to measure the quality of the flow and the level of confidence in its use for projections.
The analysis is based on the Thin-Tailed - Fat-Tailed principle
Of course ! The concept of Thin-Tailed and Fat-Tailed distributions is often used in risk analysis, statistics and finance to understand and model the impact of rare and extreme events. Here is a description that you could integrate into the Wiveez user documentation, adapted to explain their principle, their operation and their usefulness in this context.
Thin-Tailed and Fat-Tailed: Principle and Usefulness
Principle:
In data modeling, particularly in finance and risk management, we often talk about probability distributions to describe how events or values are distributed. Two types of distributions are particularly important: Thin-Tailed and Fat-Tailed distributions.
Thin-Tailed: A thin-tailed distribution is characterized by a relatively low probability that extreme events (or large deviations from the mean) will occur. In other words, extreme values (very far from the average) are rare. A typical example would be the normal (or Gaussian) distribution, where most of the data concentrates around the mean and extremes are very unlikely.
Fat-Tailed: In contrast, a fat-tailed distribution has a higher probability of extreme events. This means that rare (but very impactful) events are more frequent than would be expected with a thin-tailed distribution. Fat-tailed distributions are used to model phenomena where extreme events have a disproportionate impact, such as stock market crashes or economic crises.
Detailed ticket analysis
Wiveez allows the user to analyze the performance of each flow in detail by displaying the list of tickets associated with a column and displaying the details of the Flow Metrics of a ticket.
Analyze with our AI Alice
Wiveez provides you with its AI, named Alice, to help you analyze graphs.
Click on the Alice icon to start analyzing your graph;
A page is displayed containing an analysis of the health of your graph and tips for improvement;
You can save this analysis in a PDF file;
You can copy/paste the analysis into another type of document.
As long as no modification has been made to the chart filters or no data refresh has been initiated, your analysis remains accessible.
