Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters

TL;DR

This paper presents a comprehensive trace-based simulation study comparing various autoscaling policies for workflows in datacenters, revealing their performance differences across application domains and workload patterns.

Contribution

It is the first detailed performance analysis of autoscaling policies for workflow workloads, including their complexity and operational behavior in different scenarios.

Findings

Autoscaling policies vary significantly across application domains.

Workload burstiness impacts autoscaling effectiveness.

Autoscaling complexity incurs additional operational costs.

Abstract

To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more sophisticated autoscaling policies, that is, policies that dynamically provision resources for the customer. Although selecting and tuning autoscaling policies is a challenging task for datacenter operators, so far relatively few studies investigate the performance of autoscaling for workloads of workflows. Complementing previous knowledge, in this work we propose the first comprehensive performance study in the field. Using trace-based simulation, we compare state-of-the-art autoscaling policies across multiple application domains, workload arrival patterns (e.g., burstiness), and system utilization levels. We further investigate the interplay between autoscaling and regular allocation policies, and the complexity cost of autoscaling. Our quantitative study focuses not only on traditional performance metrics and on state-of-the-art elasticity metrics, but also on time- and memory-related autoscaling-complexity metrics. Our main results give strong and quantitative evidence about previously unreported operational behavior, for example, that autoscaling policies perform differently across application domains and by how much they differ.