Modern businesses cannot afford to lose access to their IT environment. Downtime of mission-critical systems is expensive and can result in lost customers and business opportunities. Decision-makers may be challenged in finding an effective way to prevent downtime and maximize the value of their IT investment.
Cloud monitoring tools offer companies a viable method to minimize downtime. The key to using them effectively is to construct a closed-loop system that addresses current issues while providing feedback to improve processes and procedures, minimizing or eradicating their recurrence. The goal is to eliminate recurring problems and take proactive measures to head off evolving issues.
Teams should implement the following steps to leverage cloud monitoring tools to prevent downtime.
Define What Downtime Means to the Organization
Organizations typically have diverse IT environments with multiple parts that may be more or less important to the business. Companies are much less affected by issues with their test systems than an outage impacting customer-facing ecommerce applications. Teams must understand the specific issues most important to developing preventive measures to minimize downtime.
The first step is to define the service-level objectives (SLOs) essential to the health of the IT environment. Examples of viable SLOs include:
- Availability targets for business-critical systems and applications;
- Acceptable levels of process errors or misses;
- Latency thresholds that cannot be exceeded without impacting customer satisfaction.
Organizations that do not define SLOs risk monitoring unimportant aspects of the environment, missing critical incidents, and generating excessive alerts.
Identify the Right Signals to Monitor
Modern IT environments produce many signals that can be monitored. Teams must identify which signals they need to monitor to address their defined SLOs. A good starting point is the “Golden Signals” model, created by Google’s Site Reliability Engineering (SRE) team, which examines four essential metrics.
- Latency is the time to serve successful and failed requests.
- Traffic measures the amount of demand placed on the system, for example, website requests per second.
- Errors are the rate of failed requests. A request may fail explicitly, implicitly, or as a result of policy enforcement.
- Saturation refers to the load on limited resources such as memory or network bandwidth. This metric is essential for capacity planning.
Organizations must collect and monitor these metrics to ensure they meet SLOs. Specific SLOs may need additional application and infrastructure metrics or information from dependent third-party services.
Centralize and Correlate Signals
In many cases, teams cannot properly monitor SLOs from a single, isolated signal source. It often requires correlating multiple signals to provide a more detailed view of issues affecting SLO success. Companies should incorporate information from multiple sources, including:
- Directly monitored metrics to measure system health;
- Ad hoc traces to investigate anomalies;
- Logs to perform forensic root cause analysis.
For example, customers may be experiencing slow response times on an ecommerce portal. IT teams investigating the issue may discover a latency spike in direct monitoring tools. They can run a trace to verify slow-query activity, review logs to determine when the issue began, and begin looking for solutions.
Set Intelligent Alerts to Avoid Alert Fatigue
Teams receiving too many low-value or irrelevant alerts risk falling victim to alert fatigue. This condition, caused by an abundance of unnecessary alerts, increases the probability that they will miss real incidents affecting SLOs. Organizations should consider the following factors when defining intelligent alerts.
- Set severity levels to determine the required response time. Not all alerts should be treated equally.
- Alert on the symptoms of a problem, not its prospective cause. For example, if users are experiencing a high error rate due to excessive CPU usage, the alert should be generated based on the error rate, which directly impacts an SLO.
- Teams should implement multi-condition alerts to minimize the noise from single metrics. These composite alerts minimize noise and increase precision. Alerts may trigger only if two or more conditions are met, reducing false positives and alerts that don’t affect SLOs.
- Use time windows to avoid alerting in recurring traffic spikes or known environmental conditions.
- Deploy modern machine learning monitoring tools to perform anomaly detection based on observed baselines.
Automate Incident Response Procedures
Organizations can speed up and optimize response time by automating incident response procedures. This automation can take several forms, including:
- Auto-healing by restarting failed services or applications;
- Autoscaling to dynamically add resources to address load spikes;
- Executing approved runbooks to address known issues;
- Routing alerts of given severity to the appropriate personnel.
Efficient automation can be instrumental in meeting SLOs and avoiding downtime.
Improve With Regular Reviews
Companies should continuously refine and improve SLO monitoring by regularly reviewing how incidents are identified and handled. Teams can review incidents to determine if signals were missed and if additional signals are necessary. The alert may have been too noisy or generated too late to avoid a disruption. A specific type of incident may require signals to be correlated differently to identify and proactively avoid issues.
Feedback from these reviews may be used to fine-tune alert thresholds to minimize excess noise. Teams can discover incident patterns that can lead to the development of automated runbooks to address the issue. Continuous improvement will result in the successful meeting of SLOs.
Best Cloud Monitoring Practices
Organizations should follow the steps outlined above for effective cloud monitoring. Teams should implement an iterative process of defining SLOs, correlating metrics and information, intelligently alerting, automating responses, and continuously refining.
Companies must avoid noise overload from monitoring too many irrelevant metrics. An appropriate team or individual should own alerts to ensure proper action is taken. Teams should test alerts to ensure they work and not ignore infrequent failures that have a high impact on the organization.
VAST’s Cloud Management Solutions
VAST’s cloud experts understand the importance of effective monitoring to prevent downtime and keep your business running smoothly. VAST View is a cloud management solution available as a self-service tool or as part of our managed public cloud services. It provides complete visibility into your cloud infrastructure and helps you meet your SLOs and business objectives. The platform optimizes your cloud environment and helps you stay ahead of vulnerabilities before they cause issues.
Talk to our team to learn how you can put VAST View to work to meet your SLOs and prevent downtime.
