I Heard That Your Monitoring Is Not Ideal? Do You Have These 5 Characteristics

Monitoring Characteristics Introduction:

Monitoring Characteristics.In today's user-centric IT environment, more and more organizations are implementing the Site Reliability Engineer (SRE) function to define and measure system availability and uptime, increase release efficiency, and reduce failure costs. User needs are also driving frequent changes to the system. Based on this, traditional monitoring methods simply cannot meet the monitoring expectations and requirements of SREs. Essentially, monitoring is observing the behavior of a system. Its purpose: Are my systems doing what they should?

Monitoring Characteristics. Now, more and more organizations are beginning to adopt the microservice system architecture pattern, and the individual services in microservices have high degrees of freedom, elasticity and security requirements, so we need a new approach to monitoring.

Characteristics of a Successful Monitoring Strategy

For SRE to be successful, organizations need a new, modern way to manage and monitor rapidly expanding and rapidly changing IT infrastructures, where monitoring is a critical component of service stability.
So what should surveillance look like in an SRE world? What are the characteristics of a successful monitoring strategy?

Monitoring Characteristics1. Measure performance to meet quality of service requirements


Now, just a ping command to see if the system is up or down is not enough. The Ping command is useful, but it doesn't know what the service is doing. Knowing that a machine is running and the service it provides to customers is the real business value.

So, how can we best measure the performance of these services. The answer is to measure the latency of each interaction between each component in the system. In this customer-centric world, high latency is the new "glitch" .

The quality of web user experience is affected by the performance of numerous microservices. To fully understand performance, you need to examine the latency of every component and microservice in the system. All these delays add up to make or break the customer experience, and thus the quality of your service.

If 1 in 100 database queries is slow, will your quality of service suffer? Will your business suffer if 5 out of 100 customers are unhappy with your service?

However, traditional surveillance methods keep SREs unaware of these situations. Every user matters, and so does every user's interaction. However, their experience is directly impacted by every component interaction, every disk interaction, every cloud service interaction, every microservice interaction, and every API query, so they should all be measured.

the SRE has no way of understanding what is causing the web application to fail without measuring the total latency of user requests and alerting the SRE when unacceptable latency occurs in subcomponents and microservices .

Therefore, SREs need to be able to measure all data reliably and cost-effectively, and need a comprehensive view of all infrastructure and all service metrics. Not only does this help solve problems quickly, but once you collect these metrics, your team has the potential to uncover additional business value (eg, user preferences) in this massive amount of data.

Monitoring Characteristics2. Centralized monitoring platform


Traditional monitoring often has different monitoring tools, each with a specific purpose and creating data silos. This is an environment that lacks consistent standards and processes. As a result, it is often impossible to share information in a clear and standard way among different teams within an organization .

Monitoring Characteristics. Having different monitoring tools usually requires more additional cost and resources, and often only a few people know how to use them. At the strategic level, there is no comprehensive and unified view of the health and performance of the systems that support the business.

Monitoring Characteristics. By centralizing all of your metrics (applications, infrastructure, cloud platforms, networks, containers) into one observability platform, your organization will have a consistent standard of monitoring metrics across teams and services.

A centralized monitoring platform that unifies and correlates all data in real-time, consolidates the monitoring efforts of all teams within an organization, and enables businesses to get the most value from their monitoring efforts.

Monitoring Characteristics3. Metrics 2.0 monitoring solution

Monitoring Characteristics.Using traditional monitoring processes, today's SREs can require hours of engineering time to determine the source of performance issues from millions of data streams. In order to solve performance problems quickly, SRE needs more contextual content of the system.
Metrics with context help SREs correlate events and reduce the time it takes to identify the root cause of service failures.
This is why SREs need to have a Metrics 2.0 compliant monitoring solution. Metrics 2.0 is a set of conventions, standards, and concepts around metadata for time series metrics, with the goal of producing metrics in a self-describing and standardized format.

Monitoring Characteristics.The basic premise of Metrics 2.0 is that metrics without context have little value . Metrics 2.0 requires that metrics be tagged with associated metadata or context about the metrics being collected. For example, collecting CPU utilization from a hundred servers without context is not particularly useful. But using the Metrics 2.0 tag, you'll know that this specific CPU metric is coming from this specific server.

When all metrics are marked this way, querying and analysis becomes very powerful. You can search based on these tags and be able to slice and dice the data in a variety of ways to gather analytical insights and intelligence about your operations.

Monitoring Characteristics4. SLOs need to be flexible

Service Level Objectives (SLOs) have recently become a popular tool for describing application reliability. As described in the Google SRE book, SLO is a way for application developers and SRE teams to explicitly capture an application's risk tolerance by defining an acceptable level of failure and then making a risk vs reward decision based on that decision.

The basic steps for creating an SLO include the following parts:
•What to measure - number of requests, storage checks, operations, is what to measure.
•Desired ratio - eg "Success 50% of the time", "Readable 99.9% of the time", "Returns within 10ms 90% of the time".
•Time Range - The time period to use for the target: last 10 minutes, last quarter period, rolling time of 30 days. SLOs are mostly specified using rolling time

periods or calendar units such as "one month", allowing us to compare data from different time periods.
Putting these sections together and including important "location" information in them, an example SLO looks like this:
"The load balancer reported that 90% of HTTP requests were successful in the last 30 days."

Again, a basic SLO to measure latency might look like this:
According to client reports, 90% of HTTP requests returned within 20ms in the last 30 days.
When introducing this practice into an organization, start with this simple basic SLO. More complex SLOs can be created later as needed.
Because SLO is an availability and performance guarantee, it shouldn't be set up around identifying when something goes wrong. Instead, SLOs should be set around customer perceived value, as this directly impacts your ability to be successful.

Many organizations spend a lot of effort trying to get their SLOs right. Unfortunately, it's a waste of effort. Because, it's hard to get your SLO perfect the first time, it's impossible. Instead, SLO should be an iterative process. You should have a feedback loop that is constantly updated based on the information you get every day. Therefore, your SLOs need to be flexible and they need to be reassessed regularly to make sure they are not too loose nor too tight.

5. Monitoring Characteristics.Retain your monitoring data to reduce future risk

Previously, monitoring data was often considered low-value and high-cost. Times have changed, and like all computing resources, the cost of data storage has dropped dramatically.

More importantly, SRE increases the value of retaining this data over the long term. When the system fails, the SRE needs to be able to go back in time and understand the cause of the problem in the past, to understand how the failure occurred.
Data retention, often resulting in valuable learning, reduces future risk.

Summarize
In today's customer-centric IT environment, SREs increasingly require more advanced monitoring than in the past.
When organizations embrace these monitoring features, there are many benefits such as faster problem identification and resolution, full visibility of all metrics, better performance, lower costs, and greater confidence in the accuracy of decisions.

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