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SignalFx 2018 Predictions: The Rise of Application-Aware Autoscaling

VMblog Predictions 2018

Industry executives and experts share their predictions for 2018.  Read them in this 10th annual VMblog.com series exclusive.

Contributed by Patrick Lin, VP Product & Partnerships at SignalFx

The Rise of Application-Aware Autoscaling

A decade ago we saw enterprise adoption of virtualization accelerate dramatically, and with it came a number of use cases that stemmed from this new form of infrastructure. The advent of containers and microservices is the next wave, and like the previous wave, it opens up new possibilities for how infrastructure can be used. One of the newest use cases that we have seen arise is application-aware autoscaling.

The idea of application-aware autoscaling itself is not new; why wouldn't you want the infrastructure that supports your applications to automatically grow or shrink in line with actual demand? In practice, the results have rarely matched the marketing rhetoric that we've seen around application awareness. Utility computing and its various incarnations at the turn of the century never amounted to much, and innovations driven by infrastructure vendors tend to be application-aware in name only, instead making use of the utilization levels of the basic infrastructure food groups (CPU, memory, disk, network) as proxies for determining when an application needs additional resources.

In 2018, we will finally see some real progress on this front. Just as virtualization provided enough of an abstraction layer for automated load balancing to happen with arbitrary workloads, the rise of containers and orchestrators provides the prerequisite for autoscaling to happen with arbitrary services. In addition, the monitoring and evaluation of application and service metrics can now happen in real-time in a distributed and sophisticated manner that companies can trust to drive the scaling up or down of the supporting infrastructure:

  • The definition of real-time will vary by service, but a monitoring platform that can ingest and process metrics with a predictably low latency (a few seconds, tops) at production scale is what you should be looking for.
  • Applications trending towards or built on top of a microservices architecture have dependencies that need to be understood for automated actions to be taken. The monitoring platform you use to track performance and utilization metrics needs to account for the full stack.
  • Most importantly, the system you use to make scaling decisions must be sophisticated enough to create aggregate, composite or derived metrics out of raw data, and evaluate those metrics against dynamic thresholds that take into account the characteristics of the data, e.g. if there is seasonality in it, or if a ‘settling down' period is needed. A monitoring platform that uses the raw metrics that you gather, evaluated against fixed thresholds, is too simplistic to be trusted to make changes in your infrastructure.

Much like virtualization enabled a myriad of use cases over the past couple of decades, you will continue to see containers and orchestrators build on this transformation and provide valuable use cases for enterprises. This will continue to employ automated work streams for developers to help them focus on greenfield development opportunities, providing a better end user customer experience and the ability to handle the vast amount of data being pumped into enterprises. The onus will be on the enterprise to make sense of this data, and continue to monitor these technologies in real time as they start to roll them into production, making sure they have a pulse on their business as they migrate toward more dynamic environments.

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About the Author

Patrick Lin 

Patrick leads Product and Partnerships at SignalFx. Previously, he held senior leadership positions in Product Management at Jive Software and VMware.

Published Tuesday, January 23, 2018 7:35 AM by David Marshall
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