Performance monitoring in Cloud computing // IT Management Solutions

Performance monitoring in Cloud computing

Are your applications moving to an elastic cloud infrastructure? The question is no longer if, but when – whether that is a public cloud, a private cloud, or a hybrid cloud.

Classic computing capacity models clearly indicate that over-provisioning is essential to keep up with peak loads of traffic while the over-provisioned capacity is largely left under-utilized during non-peak periods. Such over-provisioning and under-utilization can be avoided by moving to an elastic cloud-computing capacity model where just-in-time provisioning and deprovisioning can be achieved by automatically scaling up and down on-demand.

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Cloud auto-scaling decisions are often made based on infrastructure metrics such as CPU Utilization. However, in a cloud or virtualized environment, infrastructure metrics may not be reliable enough for making auto-scaling decisions. Auto-scaling decisions based on application metrics, such as request-queue depth or requests per minute, are much more useful since the application is intimately familiar with conditions such as:

  • When the existing number of compute instances cannot handle the incoming arrival rate of traffic and must elastically scale up additional instances based on a high-watermark threshold on a given application metric
  • When it’s time to scale back down based on a low-watermark threshold on the same application metric.

The basic flow for auto-scaling using AppDynamics is shown in the diagram below:Every application service can be expressed as a statistical model of traffic, queues and resources as shown in the diagram below.

  • For a given arrival rate λ, we need to maximize the service rate μ with an optimum value of n resources. Monitoring either the arrival rate λ itself for synchronous requests or q depth for asynchronous requests will help us tune the application system to see if we need additional service compute instances to meet the demands of the current arrival rate.
  • Having visibility into this data allows us not only to find bottlenecks in the code but also possibly flaws in design and architecture. AppDynamics provides visibility into these application metrics.

Let’s take an example to illustrate how this actually works in AppDynamics. ACME Corporation has a multi-tier distributed online bookstore application running on AWS EC2:

The front-end E-Commerce tier is experiencing a very heavy volume of requests resulting in the tier going into a Warning (Yellow) state.

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