What are the best strategies to monitor large-scale systems effectively?

Monitoring large-scale systems requires a clear understanding of what truly matters. Imagine your system as a vast ship, carrying crucial data and experiences. Like a captain, set clear goals: Identify vital signs: Track critical metrics like engine performance (CPU), passenger safety (security), and course accuracy (network latency) to ensure smooth sailing. Align with business goals: Steer towards high uptime, robust security, efficient resource use, and exceptional user experience, just like aiming for a successful voyage. Prioritize like a pro: Categorize issues as critical (engine failure), high (navigation errors), medium (minor glitches), or low (informational alerts). Allocate resources wisely, focusing on critical issues first.

Actually the concept of monitoring is not new, and should be part of a more robust Portfolio Management operation that builds on standards like Technology Business Management (TBM). Picking the right metics and measurements with a dashboard is a common approach. Instead of having disparate steps, using automated tools that guides implementation is recommended.

In the US Air Force Security Forces, we learned the value of precise objectives and adaptation, akin to monitoring large-scale systems. Defining monitoring goals mirrored setting mission objectives, focusing on key outcomes. Selecting tools was like choosing the right equipment for a mission, crucial for effectiveness. Designing monitoring architecture and implementing practices reflected strategic deployment and disciplined execution. Evaluating results and adapting strategies were akin to post-mission analysis, crucial for continuous improvement. This disciplined approach ensures monitoring efforts are focused, adaptable, and aligned with overarching objectives, mirroring military precision in achieving mission success.

In my experience with monitoring large building systems globally main challenges were energy inefficiencies & maintenance challenges, few of Metrics & KPI’s are as below 1. Energy Efficiency: KPI Metrics: Energy consumption trends, peak demand periods, intensity per square foot. 2. Occupant Comfort: KPI Metrics: Indoor air quality, temperature/humidity variations, occupant surveys. 3.Equipment Health: KPI Metrics: Runtime, maintenance completion rate, fault detection alerts. 4.Safety and Security: KPI Metrics: Fire alarm frequency, access control logs, surveillance footage. 5.Sustainability: KPI Metrics: Recycling rate, water consumption, carbon footprint reduction. By monitoring these goals we can achieve them.

The best way of monitoring starts with the system deep knowledge to identify the behaviour of the same and what are the factors that have mahor influence. Than define as is status and what do you want to monitor, last point to reacheable targets and state the motor interval could be useful to be more effective

Choosing the right monitoring tools involves evaluating compatibility, scalability, features, and cost. Opt for tools that integrate well with your tech stack, scale with your system, and offer comprehensive monitoring capabilities. Consider open-source options like Prometheus for infrastructure and ELK Stack for logs, or commercial solutions like Datadog for application performance. Balance functionality with ease of use and total cost of ownership. Test tools in your environment to ensure they meet your specific needs before full adoption.

The selection of monitoring tools is a crucial step for large-scale systems. However, it's also important to consider the integration of these tools with existing systems and the scalability they offer as your system grows. Furthermore, the choice of tools should not only be based on current needs but also based on future requirements. Predictive analytics tools, for instance, are becoming increasingly relevant in proactive system monitoring and maintenance. Lastly, while technology is important, don't overlook the role of skilled personnel in interpreting and acting on the data these tools provide. Data is no good without action and solutions.

Gro?e und teure Tools machen Das System unflexibel. Teure Wartung hat das zur Folge. Es ist besser eine Suite von kleineren und speziellen L?sungen einzusetzen die auf ein Dashboard zusammengeführt werden. Ich bin echt gespannt, welches Tool die langsame Verschmutzung eines Datenlakes entdecken kann.

Collecting data is the easy bit. Analyzing them and getting a useful information about the system is the hardest part. Having created a value stream and knowing all inputs and expected output the analysis of data shall be focused on the deviation of outputs. Answering simple questions such as is the system doing what I intended it to do? Should lead to clear “yes” and “no” answers. It is therefore important to limit the amount of data for such Tools to the absolute minimum. The analysis can be done with any kind of tool, be it excel, a python script or whatever. The key outcome shall be a clear visual representation of the system behavior and status.

En mi experiencia quisiera compartirles que el uso de herramientas es crucial en la gestión de un monitoreo de sistemas he tenido la suerte de trabajar con alguna de ellas que nos facilitan la vida como zabbix, lansweeper,datadog,?y?grafana super recomendadas

Clear goals is critical (i.e. what are you hoping for the system to achieve or how do you want it to perform) as well as understanding the parameters within which you are comfortable for it to perform. Understanding the architecture and sub-system inter-dependencies will help predict/ model behaviour/ performance (& subsequent impact) from changes to the system which will inform safe/ acceptable operating parameters

This is back to the visualization of the value stream. You only will see if your data makes sense once you mapped out the value stream with all its inputs and outputs. Than you can focus on all the interfaces data pipelines etc.

Es ist viel besser die überwachung in die Systemarchitektur als Funktion zu integrieren. Im Automobilsektor hei?t das Funktionale Sicherheit. Und die muss beim Systemdesign implementiert werden. Nachtr?gliche Einrichtung der überwachung, separate Tools, die Anwendung von systemfremden Prozessen hat sehr viel Abstimmungsaufwand und notwendiges Prozessverst?ndnis zur Folge. Meisten sind Einschr?nkungen and Qualit?tsverluste damit verbunden. Nachtr?gliche Gesetzes?nderungen machen oft ?nderungen der überwachung erforderlich. Und manchmal ist der Prozess bzw. das Verfahren dann am Ende.

Define Monitoring Objectives: Clearly define the goals and objectives of your monitoring efforts. Determine what aspects of your infrastructure, applications, and services you need to monitor to ensure optimal performance, availability, and reliability. Implement Monitoring Agents: Deploy monitoring agents or agentsless monitoring solutions on servers, endpoints, and network devices to collect relevant performance data. Ensure that monitoring agents are properly configured and optimized for accurate data collection Implement Dashboards and Visualization: Create dashboards and visualization tools to present monitoring data in a clear and actionable format. Use graphs, charts, and reports to visualize performance trends

Design a scalable monitoring architecture with layers for data collection (agents/integrations), aggregation/storage (time-series databases for metrics, Elasticsearch for logs), and analysis (real-time for anomalies, historical for trends). Use dashboards (Grafana, Kibana) for visualization, configure alerts for threshold breaches and anomalies, and automate responses where possible. Ensure the architecture is modular for easy updates, scalable to handle growth, and resilient for continuous operation. Start with essential metrics and expand based on insights.

During my time at Caterpillar, I focused on improving large-scale systems in Engineering. We made things smoother by using better tools to keep an eye on how everything was working. With alerts in place, we could quickly find and fix issues, making sure everything ran well and downtime was minimal. This simple and proactive monitoring approach was key to keeping our engineering systems running smoothly.

Implementing best monitoring practices in building automation systems for large buildings involves: 1. Planning:Define monitoring objectives and select appropriate sensors and systems. 2.Installation:Deploy sensors strategically throughout the building and integrate them with monitoring platforms 3.Testing:Conduct thorough testing of monitoring systems to ensure accuracy & reliability 4.Training:Train staff on how to use monitoring systems effectively & interpret data to take appropriate actions. 5.Optimization:Continuously optimize monitoring systems based on feedback and performance metrics. 6.Maintenance Regularly maintain sensors and monitoring equipment 7.Evaluation:Periodically evaluate the effectiveness of monitoring practices

Most of your employees really want to do a good job, but when coming into a new ecosystem, they will often feel lost. It's important to recognize the need to train them up, and provide internal support with the same veracity as supporting your paying customers.

Implement monitoring with these practices: Cover all system layers (infrastructure, application, network) thoroughly. Collect data at high granularity and resolution for precise insights. Design alerts to focus on problem symptoms, setting thresholds that reduce false alarms. Prioritize incidents based on impact, and automate responses to common issues. Regularly review and adjust your monitoring strategy based on system evolution and past incidents. Keep documentation updated for team clarity.

The solution I built follows a slightly different paradigm. I built 'compartmentalization' into the architecture. That allowed me a single source code, live debugging, real-time metric addition, self-evaluation, decoupled processing, an AI path, resource balancing, and most of all, the ability to change anyone of the components with minimal effect. Billions of metrics annually, zero downtime, significant savings, preempted 100s of impacting scenarios, secure, portable and all open-source.

Sounds like a security operation center. Can be manned by humans, robots, software etc. best is a combined approach. Systems often can not detect context related anomalies and only that what they were trained and designed for.

Evaluating monitoring results involves analyzing data to improve system performance and reliability. Regularly review metrics and alerts to identify trends and recurring issues. Use dashboards for real-time analysis and generate reports for historical performance. Assess the effectiveness of alert thresholds and adjust to reduce noise. Conduct root cause analysis on incidents to prevent recurrence. Continuously refine your monitoring setup based on findings, ensuring it evolves with your system's needs and scales. Share insights across teams to foster proactive improvements.

The best strategies to monitor large-scale systems effectively include 1. Setting up systems for systematic and ongoing data collection and analysis 2. Regular reporting and communication of findings to stakeholders 3. Utilizing feedback from various sources to make improvements to program design and implementation 4. Implementing monitoring tools and technology to streamline the monitoring process 5. Establishing clear and relevant indicators to track progress and outcomes 6. Ensuring coordination and collaboration among different teams and stakeholders involved in the monitoring process.

Review Metrics and KPIs: Regularly review the metrics and key performance indicators (KPIs) tracked by your monitoring system. Look for trends, anomalies, and areas of concern. Assess Impact on Users and Business Operations: Assess the impact of monitoring results on end-users and business operations. Determine whether performance issues or downtime had any negative effects on productivity, customer satisfaction, or revenue generation. Implement Continuous Improvement: Use insights gained from monitoring evaluations to drive continuous improvement initiatives. Implement changes to processes, configurations, and infrastructure to optimize performance, reliability, and security.

Any monitoring solution should have a cyclical analytical cycle. Your distribution of monitoring data should include several disciplines (e.g. support, engineering, management), all of which should have a cohesive action response plan. The feedback mechanisms (e.g. tuning, fixes, alert suppression) should be integrated into the architecture thus enabling a constantly evolving solution.

A well decoupled architecture will best afford you the ability to adjust on the fly. All components should constantly be in a state of observation by, yes, the monitoring system itself. Having that crucial data available 24/7 means there doesn't need to be an exact 'state' of adjustment, but more a real-time adjustment. Paradoxically, AI.

The first step is critical to the remaining steps as are all process oriented, outcomes based systems. Revisit this step throughout the process by integrating steps 4, 5 and 6 to verify and validate performance objectives.

Adapting monitoring strategies is a critical aspect of systems engineering, especially in the context of large-scale systems. The complexity and dynamism of these systems necessitate a flexible approach to monitoring. I've found that the dynamic nature of these systems necessitates continuous learning and improvement. It's important to note that the effectiveness of monitoring tools and strategies is not static; it evolves with the system. It's not just about having robust tools and architecture, but also about the ability to learn from past experiences and incorporate those learnings into future strategies. This iterative process is key to maintaining system efficiency and effectiveness and also fosters a proactive approach to challenges.

Defining clear Service-Level Indicators (SLIs) and Objectives (SLOs) provides a quantifiable measure of system performance and reliability. For a cloud-based application, we established SLOs for uptime, response time, and error rates, which were continuously measured against the SLIs, providing clear targets for system performance and guiding our monitoring efforts. Security is a critical aspect of large-scale systems. Integrating security monitoring tools and practices, such as intrusion detection systems and regular vulnerability scanning, into the overall monitoring strategy is essential. In one project, incorporating security monitoring allowed us to detect and mitigate threats proactively, safeguarding the system's integrity.

Considere come?ar de forma simples. capture ops logs de erro de sua aplica??o classifique e veja quantas pessoas s?o afetadas por cada uma das falhas. esse e um monitoramento simples de implementar e que ajuda na estabilidade da sua solu??o.

Often we focus on the technical aspects of systems, perhaps because as a society we are immersed in technology. Monitoring a system requires being attuned to all aspects; human, organizational, and technical. So monitoring a large complex system requires understanding how these various aspects work and work together. That requires framing and modeling to determine the key components in each and their dependencies. This can be down in a reductionist fashion through structured analytic techniques or additive by trying to map the systems behavior. The latter may even take the form of a digital twin that can be used to test system changes. The important part of either approach is to not forget that systems do not exist in isolation.

If it is large scale, then there are probably a lot of humans both using and monitoring the system. Over time some complacency may set in. - Users may not verify their work has completed successfully. - Monitoring roles may get lost in workplace reorganizations (a problem is reported but no-one is looking at it). A user reporting they cannot find the customer order they took should not be the first that you know a database is dangerously close to capacity. The answer is to run regular significant incident exercises and audit the monitoring processes to ensure they are still fit for purpose.

In case of large systems a "sensor-fusion" approach could be useful. In my opinion mixing different technologies could lead to a better representation of what is ongoing at different levels of accuracy