The key cloud advantages of self-service, automatic provisioning, and rapid elasticity come at the cost of increased complexity at the application level. Each newly provisioned instance can have a hidden impact on the performance of already-running applications, an impact that may be visible only when we look at the underlying shared infrastructure.
Cloud features such as automatic provisioning and workload management allow us to ignore the relationship between VMs and assigned underlying hardware, but our applications must still run on the actual hardware. And when things go wrong we need to be able to track down the problem quickly and accurately.
As technology evolves, new challenges will arise. One thing remains the same: We need to understand the impact of resource utilization and hardware latencies on the performance of our application and workload and maintain the correlation between application and hardware at any given point to resolve issues. Here are three ways you can achieve better insight into the performance of your applications in the cloud.
Make sure your APM/IPM solution offers fine-grained visibility
An effective application performance management (APM)/infrastructure performance management (IPM) solution for the cloud optimizes your time by automatically providing you with time-series performance data that spans your big data hardware, software, and applications, sampling every five seconds and allowing you to solve the hard problems. Real-time visibility into how an application is using resources beyond CPU and memory is required to find contention and resource hogs wherever they lurk. The ROI benefit is measured in hundreds of man-hours that can be spent on other tasks, as well as the money you don’t have to spend running inefficient apps that are unnecessarily fighting for resources. Pepperdata provides application tuning recommendations, auto-generated reports, and insight tables that give you actionable observations and recommendations, not just raw data.
Deploy an APM/IPM solution that provides AI-driven optimization
The primary driver for moving to the cloud is cost-savings. On-premises, over-provisioning for workloads is a given, and resource costs are a secondary concern. But in the cloud, you pay for every minute of compute and storage resources that you use, so over-provisioning is a major issue. A standard YARN deployment allows for CPU and memory resources to be reserved and not used by applications as a normal practice. This inefficiency is wasting resources and driving up your cloud costs. By auto-tuning the platform, these inefficiencies are reduced by up to 50%. Pepperdata leverages AI to automatically optimize your big data platform in response to inefficient CPU and memory allocation. This programmatic approach to tuning is done at scale affecting thousands of applications simultaneously and eliminates the time-consuming hassle of trying to manually tune every job.
Choose an APM/IPM solution that includes a world-class support team
Pepperdata leverages the richest set of performance data available to tackle unknown challenges that stem from managing a complex, ever-changing combin