PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies

The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

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

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.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters