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nCast Episode 13: Multidimensional Pod Autoscaling & Machine Learning for Cloud Optimization

Dr. Haoran Qiu, a recent PhD from the University of Illinois Urbana-Champaign, joins our host James Wilson, VP of Engineering at nOps. They’re diving into multidimensional autoscaling, an area in which Haoran’s pioneering research is making waves in the Kubernetes community.

Some workloads work better with Horizontal Pod Autoscaler (HPA), others with Vertical Pod Autoscaler (VPA). Running them together can create conflicts, but using only one limits efficiency gains. A Multidimensional Pod Autoscaler solves this dilemma by combining the benefits of both VPA and HPA to dynamically adjust both the number and size of pods.

But is MPA poised to redefine resource optimization? What problems does it solve, and what fresh complexities are involved in its implementation?

Haoran and James dig into these questions while debating traditional heuristic versus Machine Learning approaches, industry versus academia, and other hot topics on the leading edge of cloud optimization.

Listen now to discover if MPA is the holy grail Kubernetes has been waiting for as we discuss the evolution of autoscaling technologies and their impact on cost, sustainability, and developer experience.

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Meet our hosts

James Wilson

VP of Engineering | nOps

Dr. Haoran Qiu

Platform Engineer | University of Illinois Urbana-Champaign

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