CPU Spike Data Issues With Victoria Metrics

Introduction

In the realm of modern IT operations, AIOps (Artificial Intelligence for IT Operations) plays a crucial role in automating and enhancing the management of complex IT environments. A core component of AIOps is the ability to ingest, process, and analyze vast amounts of data from various sources to detect anomalies and predict potential issues. Victoria Metrics is a popular time-series database often used for storing such operational data. However, recent testing of an E2E self-router healing flow has highlighted significant challenges with CPU spike data reaching Victoria Metrics, leading to a cascade of problems that undermine the effectiveness of AIOps. This article delves into a comprehensive Root Cause Analysis (RCA) of these issues, providing insights into the technical nuances, offering layman explanations, and exploring potential solutions. We'll dissect each observed problem, examining the underlying causes and how they disrupt the intended workflow, ultimately affecting incident detection, correlation, and resolution.

1. Insufficient CPU Spike Data Volume to Victoria Metrics

One of the primary concerns identified during the testing was the extremely low volume of CPU spike messages being sent to Victoria Metrics. The observed rate was approximately one message per minute. This is particularly problematic because the system is configured to scrape this data every five seconds. Ideally, for effective monitoring and anomaly detection, the simulator responsible for generating this data should be updating it much more frequently, ideally every one to two seconds. When the data ingestion rate is so drastically lower than the expected scrape rate, it creates significant data gaps. Imagine trying to understand a car's speed by looking at its speedometer only once every minute, while the car is capable of changing speed every second – you'd miss most of the critical fluctuations. This sparse data prevents the AIOps system from accurately identifying the true extent and pattern of CPU spikes, rendering anomaly detection less reliable. Without a sufficient data stream, Victoria Metrics becomes a less effective tool for real-time performance analysis. The AIOps platform, relying on this data, cannot perform its intended function of detecting subtle or rapid performance degradations that could precede major outages. The system is designed to identify deviations from normal behavior, and if the normal behavior itself isn't captured accurately due to infrequent updates, then deviations become difficult, if not impossible, to discern. The simulator's role here is critical; it's the source of truth for CPU activity in this test scenario. If the simulator isn't providing timely updates, the entire downstream data pipeline, including Victoria Metrics and the AIOps platform, is compromised from the start. This lack of granular data means that even if a significant CPU spike occurs, it might be smoothed over or entirely missed between the infrequent updates, leading to a false sense of system stability. For effective AIOps, especially in high-throughput environments, data needs to be fresh and frequent. The current setup fails this fundamental requirement.

Layman's Description and Workflow:

Think of your computer's CPU like the engine in a car. When the engine is working hard, it's like a CPU spike. We want to know exactly when the engine starts revving high, how long it stays that way, and when it calms down. This test setup is supposed to send updates about the engine's activity (CPU usage) every few seconds to a special logbook (Victoria Metrics). However, the updates are only coming in once a minute. This is like the car's dashboard only showing the engine RPMs once every minute. We miss all the important moments when the engine suddenly revs up or down. Because the logbook is so sparsely updated, the AIOps system, which is like a mechanic trying to diagnose problems, can't get a clear picture of what's happening. It's like trying to diagnose a car problem by only looking at the speedometer once a minute – you'd never catch a sudden acceleration or braking. This lack of detail means the AIOps system can't reliably tell if there's a real problem brewing or if the system is just having a brief, normal surge in activity. It's like the mechanic not being able to tell if the engine is just working hard normally or if it's about to overheat because they don't have enough readings.

2. Syslog Events Preceding CPU Spikes

Another critical issue observed is that syslog messages are being sent before the actual CPU spike occurs, even when the system is in a nominal and stable state. This premature signaling is highly problematic for incident creation workflows. In a typical scenario, a system detects an anomaly (like a CPU spike), generates an alert or log, and then this information is processed to create an incident. However, here, the syslog events are appearing out of sequence. This means that the AIOps system might receive a syslog message indicating an issue when, in reality, the system is performing normally. Consequently, the vector messages (which are likely processing these syslogs) are picked up and pushed for incident creation prematurely. This leads to the creation of false incidents, consuming valuable resources and alerting operators to non-existent problems. The core of this issue likely lies in the event generation or timing within the simulator or the preceding system components. There might be a delay in reporting the CPU spike itself, while the generic syslog generator fires off its messages without proper synchronization. This out-of-order data transmission fundamentally breaks the logic of anomaly detection and incident management. If the trigger (syslog) fires before the actual event (CPU spike), the system is essentially reacting to a phantom. This not only creates noise but also erodes confidence in the AIOps system's ability to accurately represent the system's state. Victoria Metrics might receive these syslogs, but without the corresponding, time-aligned CPU spike data, they become misleading data points. The goal is to correlate events, and if the events are arriving in the wrong order, correlation becomes impossible or, worse, leads to incorrect associations. This is a classic case of a race condition or a timing dependency issue in event generation. The system needs to ensure that critical performance metrics are reported concurrently with, or immediately after, the events that trigger them, not before.

Layman's Description and Workflow:

Imagine a fire alarm system. Normally, when there's a fire (CPU spike), the alarm (syslog) goes off. But in this case, the alarm is going off before any fire actually starts, even when the building is perfectly fine. So, the security guards (AIOps system) get a notification that there's a fire, and they start responding, but there's no actual fire. This means they are wasting time and resources dealing with a false alarm. The logs (vector messages) are being processed, and an incident is created based on this premature alarm, even before the real fire signal (CPU spike data) has a chance to be recorded. It's like receiving a "smoke detected" alert when there's no smoke, and then a separate alert for "heat rising" comes later, but the system has already assumed there's a fire based on the first false alarm. This causes confusion and makes it hard to trust the alarm system.

3. Cross-Correlation Function Failure

Despite having multiple anomalies reported concurrently, the cross-correlation function in the AIOps system fails to work as expected in the given scenario. Specifically, even when two anomalies from syslog and one from memory usage reach the correlation system at the same time, no meaningful cross-correlation is established. This suggests a fundamental issue with the correlation logic, its configuration, or its ability to handle the specific types of anomalies being generated. Cross-correlation is the backbone of advanced incident management; it's designed to intelligently group related alerts from different sources to form a single, coherent incident, rather than flooding operators with numerous individual alerts. If this function is not working, each individual alert is treated in isolation, leading to alert fatigue and obscuring the true underlying issue. Possible reasons for this failure include:

• Incorrect correlation rules: The rules defining what constitutes a