Site Reliability Engineering (SRE) focuses on keeping services reliable and scalable. A crucial part of this discipline is monitoring, which is where the concept of Golden Signals comes into play.
Golden Signals help teams quickly identify and diagnose issues within a system. This article breaks down what these signals are, why they're important, and how they can be used to maintain healthy systems.
The Four Golden Signals
SRE principles streamline monitoring by focusing on four key metrics—latency, errors, traffic, and saturation—collectively known as Golden Signals. Instead of tracking numerous metrics across different technologies, focusing on these four metrics helps in quickly identifying and resolving issues.
Latency:Latency is the time it takes for a request to travel from the client to the server and back. High latency can cause a poor user experience, making it critical to keep this metric in check. For example, in web applications, latency might typically range from 200 to 400 milliseconds. Latency monitoring helps detect slowdowns early, allowing for quick corrective action.
Errors:Errors refer to the rate of failed requests. Monitoring errors is essential because not all errors have the same impact. For instance, a 500 error (server error) is more severe than a 400 error (client error) because the former often requires immediate intervention. Identifying error spikes can alert teams to underlying issues before they escalate into major problems.
Traffic:Traffic measures the volume of requests coming into the system. Understanding traffic patterns helps teams prepare for expected loads and identify anomalies that might indicate issues such as DDoS attacks or unplanned spikes in user activity. For example, if your system is built to handle 1,000 requests per second and suddenly receives 10,000, this surge might overwhelm your infrastructure if not properly managed.
Saturation:Saturation is about resource utilization; it shows how close your system is to reaching its full capacity. Monitoring saturation helps avoid performance bottlenecks caused by overuse of resources like CPU, memory, or network bandwidth. Think of it like a car's tachometer: once it redlines, you're pushing the engine too hard, risking a breakdown.
Challenges associated with monitoring saturation in microservices:
Complexity of Microservice Architectures:In microservice environments, various services are often built on different technologies (e.g., Node.js, databases, Swift). Each service may handle resource usage differently, making it challenging to monitor and understand overall system saturation accurately. Saturation occurs when resources such as CPU, memory, or network bandwidth are fully utilized, leading to degraded performance.
Resource Utilization Visibility:Since each microservice can have its unique metrics, gaining a clear view of overall saturation is difficult. Teams need to aggregate and standardize data from multiple services to accurately assess saturation levels. This can be time-consuming and requires expertise across different technology stacks.
Identification of Bottlenecks:Saturation often results in bottlenecks where some services are overloaded while others are underutilized. Pinpointing which service is causing the bottleneck in a complex system can be difficult without a cohesive monitoring approach like the one provided by SRE Golden Signals.
Dynamic and Variable Loads:In microservice architectures, traffic and resource demands can fluctuate rapidly, making it essential to monitor saturation in real-time. Services must adapt to changes in load, but without proper monitoring, it's easy to miss critical saturation points that can impact overall system performance.
Why Golden Signals Matter
Golden Signals provide a comprehensive overview of a system's health, enabling SREs and DevOps teams to be proactive rather than reactive. By continuously monitoring these metrics, teams can spot trends and anomalies, address potential issues before they affect end-users, and maintain a high level of service reliability.
SRE Golden Signals help in proactive system monitoring
SRE Golden Signals are crucial for proactive system monitoring because they simplify the identification of root causes in complex applications. Instead of getting overwhelmed by numerous metrics from various technologies, SRE Golden Signals focus on four key indicators: latency, errors, traffic, and saturation.
By continuously monitoring these signals, teams can detect anomalies early and address potential issues before they affect the end-user. For instance, if there is an increase in latency or a spike in error rates, it signals that something is wrong, prompting immediate investigation.
What are the key benefits of using "golden signals" in a microservices environment?
The "golden signals" approach is especially beneficial in a microservices environment because it provides a simplified yet powerful framework to monitor essential metrics across complex service architectures.
Here’s why this approach is effective:
▪️Focuses on Key Performance Indicators (KPIs)
By concentrating on latency, errors, traffic, and saturation, the golden signals let teams avoid the overwhelming and often unmanageable task of tracking every metric across diverse microservices. This strategic focus means that only the most crucial metrics impacting user experience are monitored.
▪️Enhances Cross-Technology Clarity
In a microservices ecosystem where services might be built on different technologies (e.g., Node.js, DB2, Swift), using universal metrics minimizes the need for specific expertise. Teams can identify issues without having to fully understand the intricacies of every service’s technology stack.
▪️Speeds Up Troubleshooting
Golden signals quickly highlight root causes by filtering out non-essential metrics, allowing the team to narrow down potential problem areas in a large web of interdependent services. This is crucial for maintaining service uptime and a seamless user experience.
By applying these golden signals, SRE teams can efficiently diagnose and address issues, keeping complex applications stable and responsive.
How can the "one-hop dependency view" assist in troubleshooting?
The "one-hop dependency view" in application performance monitoring (APM) simplifies troubleshooting by focusing only on the services that directly impact the affected service.
Here’s how it helps:
▪️Reduces Investigation Scope
Rather than analyzing the entire microservices topology, the one-hop view narrows the scope to immediate dependencies. This selective approach allows engineers to focus on the most likely sources of issues, saving time in identifying the root cause.
▪️Streamlines Root-Cause Analysis
By examining only the services one level away, the team can apply the golden signals (latency, errors, traffic, saturation) to detect any anomalies quickly. If a direct dependency is experiencing problems, it becomes immediately apparent without unnecessary complexity.
▪️Decreases Mean-Time-to-Recovery (MTTR)
With fewer services to investigate, the MTTR is significantly reduced. Engineers can identify and address the root issue faster, minimizing downtime and maintaining the application’s reliability.
Using the one-hop dependency view helps SRE teams keep the troubleshooting process efficient, especially in complex, interdependent service ecosystems
Practical Application: Using APM Dashboards
Application Performance Management (APM) dashboards integrate Golden Signals into a single view, allowing teams to monitor all critical metrics at once. For example, the operations team can use APM dashboards to get insights into latency, errors, traffic, and saturation. This holistic view simplifies troubleshooting and reduces the mean time to resolution (MTTR).
Here's how they work together:
▪️Centralized Monitoring with APM Dashboards:APM tools provide dashboards that centralize the key Golden Signals—latency, errors, traffic, and saturation. This centralized view allows operations and development teams to monitor the health of their applications in real-time. By displaying these critical metrics in one place, APM tools simplify the identification of performance issues, making it easier to spot trends and anomalies that need attention.
▪️"One Hop" Dependency Views:APM tools often support a "one hop" dependency view, which shows only the immediate downstream services connected to a problematic service. This feature is particularly useful in complex microservice environments where pinpointing the root cause of an issue can be daunting. By focusing on immediate dependencies, teams can quickly assess which services are functioning within normal parameters and which are experiencing issues, thereby speeding up the troubleshooting process.
▪️Proactive Issue Detection and Resolution:Integrating Golden Signals into APM tools allows for proactive monitoring, where issues can be identified before they escalate into more serious problems. For example, if a service’s saturation levels begin trending upwards, the APM tool can alert the team before users experience degraded performance. This proactive approach helps reduce the mean time to resolution (MTTR) and improves overall service reliability.
▪️ Customization for Different Teams:The video also mentions that APM tools can be customized for different stakeholders within the organization. While the operations team may focus on all four Golden Signals, development teams might create specialized dashboards that prioritize the signals most relevant to their services. This tailored approach ensures that both dev and ops teams are aligned and can address issues quickly, often even before they impact the end-users.
In essence, the integration of SRE Golden Signals with APM tools empowers teams to maintain high levels of service performance and reliability by providing clear, actionable insights into the most critical aspects of their systems.
What is the significance of distinguishing 500 vs. 400 errors in SRE monitoring?
The distinction between 500 and 400 errors in SRE monitoring is crucial because it impacts how issues are prioritized and addressed.
Here’s a breakdown:
500 Errors (Server Errors)
These indicate serious problems on the server side, such as downtime or crashes. They require immediate attention because they prevent users from accessing the service entirely, often resulting in significant disruptions. For instance, a 500 error signals that something is failing within the server's infrastructure, meaning end-users can’t receive a response at all. Therefore, these errors are more critical in incident response and may trigger alerts for the SRE team.
400 Errors (Client Errors)
These typically indicate client-side issues, where a request is invalid or needs adjustment, like when the requested resource doesn’t exist or is restricted. Such errors might be resolved simply by retrying or by the client correcting the request, so they’re usually less urgent. Monitoring 400 errors can still reveal trends or user behavior that may require attention, but they don't indicate systemic issues.
In summary, recognizing the difference allows SREs to prioritize resources on issues that directly affect the system’s reliability and availability (like 500 errors) versus issues that may just need minor adjustments or retries.
Conclusion
Ready to take your system's reliability and performance to the next level? Gart Solutions offers top-tier SRE Monitoring services to ensure your systems are always running smoothly and efficiently. Our experts can help you identify and address potential issues before they impact your business, ensuring minimal downtime and optimal performance.
Discover how Gart Solutions can enhance your system's reliability today! Learn from our IT Monitoring case studies (Monitoring Solution for a B2C SaaS Music Platform and Advanced Monitoring for Digital Landfill Management) to learn more about our SRE Monitoring expertise.
https://youtu.be/BqPXUxhshTM?si=EWFFu0JNYgJCj7g0
To maintain smooth operation, you need to scale your resources. This article delves into the two main scaling strategies - horizontal scaling (spreading out) and vertical scaling (gearing up) - Horizontal vs. Vertical Scaling.
Even if a company pauses its processes, does not grow or develop, the amount of data will still accumulate, and information systems will become more complex. Computing requests require storing large amounts of data in the server's memory and allocating significant resources.
When corporate servers can no longer handle the load, a company has two options: purchase additional capacity for existing equipment or buy another server to offload some of the load. In this article, we will discuss the advantages and disadvantages of both approaches to building IT infrastructure.
Cloud Scalability
What is scaling? It is the ability to increase project performance in minimal time by adding resources.
Therefore, one of the priority tasks of IT specialists is to ensure the scalability of the infrastructure, i.e., the ability to quickly and without unnecessary expenses expand the volume and performance of the IT solution.
Usually, scaling does not involve rewriting the code, but either adding servers or increasing the resources of the existing one. According to this type, vertical and horizontal scaling are distinguished.
Vertical Scaling or Scale Up Infrastructure
Vertical scaling involves adding more RAM, disks, etc., to an existing server. This approach is used when the performance limit of infrastructure elements is exhausted.
Advantages of vertical scaling:
If a company lacks the resources of its existing equipment, its components can be replaced with more powerful ones.
Increasing the performance of each component within a single node increases the performance of the IT infrastructure as a whole.
However, vertical scaling also has disadvantages. The most obvious one is the limitation in increasing performance. When a company reaches its limits, it will need to purchase a more powerful system and then migrate its IT infrastructure to it. Such a transfer requires time and money and increases the risks of downtime during the system transfer.
The second disadvantage of vertical scaling is that if a virtual machine fails, the software will stop working. The company will need time to restore its functionality. Therefore, with vertical scaling, expensive hardware is often chosen that will work without downtime.
When to Scale Up Infrastructure
While scaling out offers advantages in many scenarios, scaling up infrastructure remains relevant in specific situations. Here are some key factors to consider when deciding when to scale up:
Limited growth
If your application experiences predictable and limited growth, scaling up can be a simpler and more efficient solution. Upgrading existing hardware with increased processing power, memory, and storage can often handle the anticipated growth without the complexities of managing a distributed system.
Single server bottleneck
Scaling up can be effective if you experience a performance bottleneck confined to a single server or resource type. For example, if your application primarily suffers from CPU limitations, adding more cores to the existing server might be sufficient to address the bottleneck.
Simplicity and familiarity
If your team possesses expertise and experience in managing a single server environment, scaling up might be a more familiar and manageable approach compared to the complexities of setting up and managing a distributed system with multiple nodes.
Limited resources
In scenarios with limited financial or physical resources, scaling up may be the more feasible option compared to the initial investment required for additional hardware and the ongoing costs associated with managing a distributed system.
Latency-sensitive applications
Applications with real-time processing requirements and low latency needs, such as high-frequency trading platforms or online gaming servers, can benefit from the reduced communication overhead associated with a single server architecture. Scaling up with high-performance hardware can ensure minimal latency and responsiveness.
Stateless applications
For stateless applications that don't require storing data on individual servers, scaling up can be a viable option. These applications can typically be easily migrated to a more powerful server without significant configuration changes.
Scaling up ( or verticalscaling) provides a sufficient and manageable solution for your specific needs and infrastructure constraints.
Example Situations of When to Scale Up:
E-commerce platform experiencing increased traffic during holiday seasons
Consider an e-commerce platform that experiences a surge in traffic during holiday seasons or special sales events. As more users flock to the website to make purchases, the existing infrastructure may struggle to handle the sudden influx of requests, leading to slow response times and potential downtime.
To address this issue, the e-commerce platform can opt to scale up its resources by upgrading its servers or adding more powerful processing units. By bolstering its infrastructure, the platform can better accommodate the heightened traffic load, ensuring that users can seamlessly browse, add items to their carts, and complete transactions without experiencing delays or disruptions.
Database management system for a growing social media platform
Imagine a social media platform that is rapidly gaining users and generating vast amounts of user-generated content, such as posts, comments, and media uploads. As the platform's database accumulates more data, the performance of the database management system (DBMS) may start to degrade, leading to slower query execution times and reduced responsiveness.
In response to this growth, the social media platform can choose to scale up its database infrastructure by deploying more powerful servers with higher processing capabilities and additional storage capacity. By upgrading its DBMS hardware, the platform can efficiently handle the increasing volume of user data, ensuring that users can swiftly retrieve and interact with content on the platform without experiencing delays or downtime.
Financial institution processing a growing number of transactions
Consider a financial institution, such as a bank or credit card company, that processes a large volume of transactions daily. As the institution's customer base expands and the number of transactions continues to grow, the existing processing infrastructure may struggle to keep up with the increasing workload, leading to delays in transaction processing and potential system failures.
To maintain smooth and efficient operations, the financial institution can opt to scale up its transaction processing systems by investing in more robust hardware solutions. By upgrading its servers, networking equipment, and database systems, the institution can enhance its processing capabilities, ensuring that transactions are processed quickly and accurately, and that customers have uninterrupted access to banking services.
Horizontal Scaling or Scale-Out
Horizontal scaling involves adding new nodes to the IT infrastructure. Instead of increasing the capacity of individual components of a node, the company adds new servers. With each additional node, the load is redistributed between all nodes.
Advantages of horizontal scaling:
This type of scaling allows you to use inexpensive equipment that provides enough power for workloads.
There is no need to migrate the infrastructure.
If necessary, virtual machines can be migrated to another infrastructure without stopping operation.
The company can organize work without downtime due to the fact that software instances operate on several nodes of the IT infrastructure. If one of them fails, the load will be distributed between the remaining nodes, and the program will continue to work.
With horizontal scaling, you can refuse to purchase expensive equipment and reduce hardware costs by 20 times.
When to scale out infrastructure
There are several key factors to consider when deciding when to scale out infrastructure:
Horizontal growth
If your application or service anticipates sustained growth in data, users, or workload over time, scaling out offers a more scalable and cost-effective approach than repeated scaling up. Adding new nodes allows you to incrementally increase capacity as needed, rather than investing in significantly larger hardware upgrades each time.
Performance bottlenecks
If you experience performance bottlenecks due to resource limitations (CPU, memory, storage) spread across multiple servers, scaling out can help distribute the workload and alleviate the bottleneck. This is particularly beneficial for stateful applications where data needs to be stored on individual servers.
Distributed processing
When dealing with large datasets or complex tasks that require parallel processing, scaling out allows you to distribute the workload across multiple nodes, significantly reducing processing time and improving efficiency. This is often used in big data processing and scientific computing.
Fault tolerance and redundancy
Scaling out can enhance fault tolerance and redundancy. If one server fails, the remaining nodes can handle the workload, minimizing downtime and ensuring service continuity. This is crucial for mission-critical applications where downtime can have significant consequences.
Microservices architecture
If your application employs a microservices architecture, where each service is independent and modular, scaling out individual microservices allows you to scale specific functionalities based on their specific needs. This offers greater flexibility and efficiency compared to scaling the entire application as a single unit.
Cost-effectiveness
While scaling out may require an initial investment in additional servers, in the long run, it can be more cost-effective than repeatedly scaling up. Additionally, cloud-based solutions often offer pay-as-you-go models which allow you to scale resources dynamically and only pay for what you use.
In summary, scaling out infrastructure is a good choice when you anticipate sustained growth, encounter performance bottlenecks due to resource limitations, require distributed processing for large tasks, prioritize fault tolerance and redundancy, utilize a microservices architecture, or seek cost-effective long-term scalability. Remember to carefully assess your specific needs and application characteristics to determine the optimal approach for your infrastructure.
Example Situations of When to Scale Out
Cloud-based software-as-a-service (SaaS) application facing increased demand
Consider a cloud-based SaaS application that provides project management tools to businesses of all sizes. As the application gains popularity and attracts more users, the demand for its services may skyrocket, putting strain on the existing infrastructure and causing performance degradation.
To meet the growing demand and maintain optimal performance, the SaaS provider can scale out its infrastructure by leveraging cloud computing resources such as auto-scaling groups and load balancers. By dynamically adding more virtual servers or container instances based on demand, the provider can ensure that users have access to the application's features and functionalities without experiencing slowdowns or service disruptions.
Content delivery network (CDN) handling a surge in internet traffic
Imagine a content delivery network (CDN) that delivers multimedia content, such as videos, images, and web pages, to users around the world. During peak traffic periods, such as major events or viral content trends, the CDN may experience a significant increase in incoming requests, leading to congestion and delays in content delivery.
To cope with the surge in internet traffic, the CDN can scale out its infrastructure by deploying additional edge servers or caching nodes in strategic locations. By expanding its network footprint and distributing content closer to end users, the CDN can reduce latency and improve the speed and reliability of content delivery, ensuring a seamless browsing experience for users worldwide.
E-commerce shopping cart
An e-commerce platform utilizes microservices architecture, where each service is independent and responsible for specific tasks like managing shopping carts. Scaling out individual microservices allows for handling increased user traffic and order volume without impacting other functionalities of the platform. This approach provides better flexibility and scalability compared to scaling up the entire system as a single unit.
These examples demonstrate situations where scaling out by adding more nodes horizontally is better suited to handle situations with unpredictable workloads, distributed processing needs, and independent service scaling within a larger system.
Choosing the Right Approach
The decision between horizontal and vertical scaling should be based on specific system requirements, constraints, and objectives.
Some considerations include:
Workload characteristics: Consider the nature of your workload. Horizontal scaling is well-suited for distributed and stateless workloads, while vertical scaling may be preferable for single-threaded or stateful workloads.
Cost and budget: Evaluate your budget and resource availability. Horizontal scaling can be cost-effective, especially when using commodity hardware, while vertical scaling may require a more significant upfront investment in high-performance hardware.
Performance and maintenance: Assess the performance gains and management complexity associated with each approach. Consider how well each option aligns with your operational capabilities and objectives.
Future growth: Think about your system's long-term scalability needs. If you anticipate significant growth, horizontal scaling may provide greater flexibility.
Here are some additional tips for choosing the right scaling approach:
Start with a small-scale deployment and monitor performance: This will help you understand your workload's requirements and identify any potential bottlenecks.
Use a combination of horizontal and vertical scaling: This can provide the best balance of performance, cost, and flexibility.
Consider using a cloud-based platform: Cloud providers offer a variety of scalable and cost-effective solutions that can be tailored to your specific needs.
By carefully considering all of these factors, you can choose the best scaling approach for your company's needs.
How Gart Can Help You with Cloud Scalability
Ultimately, the determining factors are your cloud needs and cost structure. Without the ability to predict the true aspects of these components, each business can fall into the trap of choosing the wrong scaling strategy for them. Therefore, cost assessment should be a priority. Additionally, optimizing cloud costs remains a complex task regardless of which scaling system you choose.
Here are some ways Gart can help you with cloud scalability:
Assess your cloud needs and cost structure: We can help you understand your current cloud usage and identify areas where you can optimize your costs.
Develop a cloud scaling strategy: We can help you choose the right scaling approach for your specific needs and budget.
Implement your cloud scaling strategy: We can help you implement your chosen scaling strategy and provide ongoing support to ensure that it meets your needs.
Optimize your cloud costs: We can help you identify and implement cost-saving measures to reduce your cloud bill.
Gart has a team of experienced cloud experts who can help you with all aspects of cloud scalability. We have a proven track record of helping businesses optimize their cloud costs and improve their cloud performance.
Contact Gart today to learn more about how we can help you with cloud scalability.
We look forward to hearing from you!
In today's digital world, businesses rely heavily on their IT infrastructure to operate effectively. Any downtime or performance issues can result in lost productivity, revenue, and brand reputation. This is where infrastructure monitoring comes in.
What Is Infrastructure Monitoring?
Infrastructure monitoring plays a vital role in collecting and analyzing data from various components of a tech stack, including servers, virtual machines, containers, and databases. This data is then analyzed to provide insights into the health and performance of the infrastructure. The tools also provide alerts and notifications when issues are detected, enabling IT teams to take corrective action.
By utilizing infrastructure monitoring practices, organizations can proactively identify and address issues that may impact users and mitigate risks of potential losses in terms of time and money.
Modern software applications must be reliable and resilient to meet clients' needs worldwide. Companies like Amazon are making an average of $14,900 every second in sales, therefore, even 30 seconds of downtime would have cost them thousands of dollars.
For software to keep up with demand, infrastructure monitoring is crucial. It allows teams to collect operational and performance data from their systems to diagnose, fix, and improve them.
Monitoring often includes physical servers, virtual machines, databases, network infrastructure, IoT devices and more. Full-featured monitoring systems can also alert you when something is wrong in your infrastructure.
In this article, we'll explain how infrastructure monitoring works, its primary use cases, typical challenges, use cases and best practices of infrastructure monitoring.
Infrastructure Monitoring: What Should You Monitor?
Infrastructure monitoring is essential for tracking the availability, performance, and resource utilization of backend components, including hosts and containers. By installing monitoring agents on hosts, engineers collect infrastructure metrics and send them to a monitoring platform for analysis. This allows organizations to ensure the availability and proper functioning of critical services for users.
Identifying which parts of your infrastructure to monitor depends on factors such as SLA requirements, system location, and complexity. Google has its Four Golden Signals (latency, traffic, errors, and saturation), which can help your team narrow down important metrics (review the official Google Cloud Monitoring Documentation). AWS, Azure also provides its best practices for monitoring.
Common System Monitoring Metrics Include
Sеrvеrs: Monitor sеrvеr CPU usagе, mеmory usagе, disk I/O, and nеtwork traffic.
Nеtwork: Monitor nеtwork latеncy, packеt loss, bandwidth usagе, and throughput.
Applications: Monitor application rеsponsе timе, еrror ratеs, and transaction volumеs.
Databasеs: Monitor databasе pеrformancе, including quеry rеsponsе timе and transaction throughput.
Sеcurity: Monitor sеcurity еvеnts, including failеd logins, unauthorizеd accеss attеmpts, and malwarе infеctions.
This list of metrics for each system isn't exhaustive. Rather, you should determine your business requirements and expectations for different parts of the infrastructure. These baselines will help you better understand what metrics should be monitored and establish guidelines for setting alerting thresholds.
Use Cases of Infrastructure Monitoring
Operations teams, DevOps engineers and SREs (site reliability engineers) generally use infrastructure monitoring to:
1. Troublеshoot pеrformancе issues
Infrastructure monitoring is instrumental in preventing incidents from escalating into outages. By using an infrastructure monitoring tool, engineers can quickly identify failed or latency-affected hosts, containers, or other backend components during an incident. In the event of an outage, they can pinpoint the responsible hosts or containers, facilitating the resolution of support tickets and addressing customer-facing issues effectively.
2. Optimize infrastructure use
Proactive cost reduction is another significant benefit of infrastructure monitoring. By analyzing the monitoring data, organizations can identify overprovisioned or underutilized servers and take necessary actions such as decommissioning them or consolidating workloads onto fewer hosts. Furthermore, infrastructure monitoring enables the redistribution of requests from underprovisioned hosts to overprovisioned ones, ensuring balanced utilization across the infrastructure.
Learn from this case study how Gart helped with AWS Cost Optimization and CI/CD Automation for the Entertainment Software Platform.
3. Forecast backend requirements
Historical infrastructure metrics provide valuable insights for predicting future resource consumption. For example, if certain hosts were found to be underprovisioned during a recent product launch, organizations can leverage this information to allocate additional CPU and memory resources during similar events. By doing so, they reduce strain on critical systems, minimizing the risk of revenue-draining outages.
4. Configuration assurancе tеsting
One of the prominent use cases of infrastructure monitoring is enhancing the testing process. Small and mid-size businesses utilize infrastructure monitoring to ensure the stability of their applications during or after feature updates. By monitoring the infrastructure, they can proactively detect any issues that may arise and take corrective measures, ensuring that their applications remain robust and reliable.
Ready to level up your Infrastructure Management? Contact us today and let our experienced team empower your organization with streamlined processes, automation, and continuous integration.
Infrastructure Monitoring Best Practices
Infrastructure monitoring best practices involve a combination of key strategies and techniques to ensure efficient and effective monitoring of your infrastructure. Here are some recommended practices to consider:
1. Opt for automation
To enhance Mean Time to Resolution (MTTR), leverage from the best infrastructure monitoring tools that offer automation capabilities. By adopting AIOps for infrastructure monitoring, you can achieve comprehensive end-to-end observability across your entire stack, facilitating quicker issue detection and resolution.
3. Install the agent across your entire environment
Rather than installing the monitoring agent on specific applications and their supporting environments, it is advisable to deploy it across your entire production environment. This approach provides a more holistic view of your infrastructure's health and performance, enabling you to make informed decisions based on comprehensive data.
Google Ops Agent Overview | AWS Systems Manager OpsCenter
3. Set up and prioritize alerts
Given the potential for numerous alerts in an infrastructure monitoring system, it's crucial to prioritize them effectively. As an SRE, focus on identifying and addressing the most critical alerts promptly, ensuring that essential issues are promptly resolved while minimizing distractions caused by less urgent notifications.
Google Cloud Monitoring Alerting Policy | AWS Alerting Policy
4. Create custom dashboards
Take advantage of the customization options available in infrastructure monitoring tools. Tools like Middleware offer the ability to create custom dashboards tailored to specific roles and requirements. By leveraging these capabilities, you can streamline your monitoring experience, presenting relevant information to different stakeholders in a clear and accessible manner.
5. Test your tools
Before integrating new applications or tools for infrastructure monitoring, testing is vital. This practice ensures that the monitoring setup functions correctly and all components are working as expected. By performing test runs, you can identify and address any potential issues before they impact your live environment.
6. Configure native integrations
If your infrastructure includes AWS resources, it is beneficial to configure native integrations with your infrastructure monitoring solution. For example, setting up the AWS EC2 integration allows for the automatic import of tags and metadata associated with your instances. This integration facilitates data filtering, provides real-time views, and enables scalability in line with your cloud infrastructure.
7. Activate integrations for comprehensive monitoring
Extend your infrastructure monitoring beyond CPU, memory, and storage utilization. Activate pre-configured integrations with services such as AWS CloudWatch, AWS Billing, AWS ELB, MySQL, NGINX, and more. These integrations enable monitoring of the services supporting your hosts and provide access to dedicated dashboards for each integrated service.
8. Create filter set for efficient resource management
Utilize the filter set functionality offered by your monitoring solution to organize hosts, cluster roles, and other resources based on relevant criteria. By applying filters based on imported EC2 tags or custom tags, you can optimize resource monitoring, proactively detect and resolve issues, and gain a comprehensive overview of your infrastructure's performance.
9. Set up alert conditions based on filtered data
Instead of creating individual alert conditions for each host, leverage the filtering capabilities to create alert conditions based on filtered data. This approach automates the addition and removal of hosts from the alert conditions as they match the specified tags. By aligning alerts with your infrastructure's tags, you ensure scalability and efficient alert management.
Our Monitoring Case Study
Wrapping Up
In conclusion, infrastructure monitoring is critical for ensuring the performance and availability of IT infrastructure. By following best practices and partnering with a trusted provider like Gart, organizations can detect issues proactively, optimize performance and be sure the IT infrastructure is 99,9% available, robust, and meets your current and future business needs. Leverage external expertise and unlock the full potential of your IT infrastructure through IT infrastructure outsourcing!
Let’s work together!
See how we can help to overcome your challenges
Contact us
