As organizations undergo digital transformations, there is increasing need to modernize the way IT operations is managed and to deliver applications faster, while at the same time delivering business value by managing applications’ availability, performance and outages in a cost-effective way.
Because outages have a huge impact on revenue, there is a growing demand for site reliability people and monitoring systems, which can enable them to handle applications’ uptime, performance and reliability with a proactive and predictive monitoring approach.
This article series focuses on the need for full-stack monitoring in today’s digital operations, the considerations for selection of monitoring tools and design considerations for implementing a full-stack monitoring solution.
Why is Full-Stack Monitoring Necessary?
Full-stack monitoring refers to the capability of monitoring all of the following:
- Infrastructure monitoring—Monitor servers, network, containers, cluster orchestrators, cloud, middleware, app servers, web servers and databases.
- Application performance monitoring—Perform log monitoring and analytics, synthetic transactions, sql transactions, data correlation with metrics and logs, end user experience monitoring, profiling and transaction-level tracing with code-level visibility.
- Performing end-end monitoring across the workloads in on-premises/cloud/hybrid environments through a centralized dashboard/visibility across the stack.
- Provide alerts, analyze, act and remediate the incidents faster, using the instrumentation data leveraging IT service management (ITSM), ChatOps and collaboration tools along with monitoring tools.
- Perform Analytics on the data being used to derive insights and predict demands.
The following are some of the needs driving enterprises to implement full-stack monitoring:
- Business agility: Business needs to deploy very frequently to sustain the market demands
- Application reliability: Site/application platform is expected to be reliable, available and scalable, as outages impact revenue and may result in losing business and customers.
- Bandwidth bottlenecks can impact the application performance, leading to potential business loss and needs network-level deep monitoring.
- Failure can happen at any level and go unnoticed, leading to overhead in recovery and remediation; hence, monitoring from various sources and at different angles with varied levels are needed.
- Application complexity and dependencies: Applications use multiple-third party components, multiple hosts and several underlying services, components and infrastructure. If any of these do not function as expected, it can degrade an application’s performance and result in issues/defects going unnoticed—and often reported by end users.
- Cloud born applications, which are multi-tiered and multi-layered, use mix workloads (PaaS + IaaS) and monitoring of different sources/workloads has to be done for distributed hybrid environments (multi-cloud).
- Use of virtualization and container technologies, which have enabled environments with dynamic and ephemeral states and such workloads, need monitoring at the orchestration and container levels.
- Lack of end-to end monitoring tools and lack of visibility across all IT assets and workloads, which has increased the need of centralized monitoring data. The SRE teams need to get stakeholders’ buy-in based on the monitoring data to push back on the bad releases and take calculated risks. They also need to cater different types of monitoring data to different stakeholders and line of business (LOB) owners and overcome cultural challenges to get stakeholders’ buy-in.
- IT operations needs to forecast an application’s behavior, capacity and performance, based on how the system performs in the given conditions. Machine learning of monitoring data and patterns can help predict capacity and anomaly.
- Large volumes of data are collected from multiple sources giving out metrics, logs, etc., for various components of the stack. The IT operations team needs in-depth information about the components and dependencies along with metrics to provide contextual/correlation information. They need to correlate the data to determine symptoms and causes for faster troubleshooting. As such, they need monitoring tools that can provide correlation out-of-box to reduce the effort involved in triaging.
- Configuration changes: Deployment of application/infrastructure/services are done through infrastructure as code automation scripts, which may contain issues in configuration. Lack of testing infrastructure, services and application end to end can increase the number of issues to be resolved; hence, there is a need of monitoring right from provisioning of resources, usage, maintenance and de-provisioning of resources.
- Lack of end user perspective and business metrics from traditional tools, coupled with the need to eliminate toil and perform faster triaging/post-mortem and automated incident resolution, demands use of new-age full-stack monitoring tools.
Growing business demands and agility, application reliability, deriving business insights and end user experience analytics are the key drivers for implementing full-stack monitoring. Hence, the IT operations traditional approaches are rapidly being transformed to digital operations (SRE operations), along with DevOps practices being widely adopted across enterprises.
In the second and third articles in this three-part series, we’ll look at the considerations for selection of monitoring tools and design considerations for implementing a full-stack monitoring solution.