Inactivity Benchmarking

A4 Conference proceedings


Internal Authors/Editors


Publication Details

List of Authors: Wictor Lund, Johan Lilius
Editors: Waleed W. Smari
Khalid Zine-Dine
Publication year: 2018
Publisher: IEEE
Book title: 2018 International Conference on High Performance Computing & Simulation (HPCS)
Title of series: 2018 International Conference on High Performance Computing & Simulation (HPCS)
Number in series: 16
Start page: 1028
End page: 1033
ISBN: 978-1-5386-7878-7
eISBN: 978-1-5386-7879-4


Abstract

Computational workloads that run out of work need some way to signal the intent to be inactive. With a proper inactivity signaling, the battery life can be extended, cloud costs can be reduced and the performance can be improved. In this paper, we study some properties of the inactivity signaling capabilities on virtualized platforms. The main contribution of the paper is a presentation of the inner workings of Spurg-Bench - a benchmarking tool developed by the author. The Spurg-Bench generates a load consisting of computation and inactivity. A secondary contribution is a small study on the inactivity behaviour of two different operating system (OS) kernels.

Spurg-Bench generates a specified load on a system according to the utilization of the operations per second (OPS). By using the utilization notion of the load, Spurg-Bench generates a load occupying a fraction of the available processor time without any throughput guarantees. With the OPS notion, the generated load has a given throughput but has no guarantees on how much processor time it uses.

Spurg-Bench generates load in cycles by executing an operation repeatedly and becoming inactive for a while. The parameters of a cycle in Spurg-Bench are the number of the operations and the duration of the inactivity. To achieve the desired load, the parameters of Spurg-Bench are dynamically adjusted using a control algorithm which minimizes the difference between the current load and the specified load. Spurg-Bench differs from similar tools by running the workload in the same loop as the control algorithm. The in-loop approach is crucial to the inactivity benchmarking because it enables a full control over the duration and the starting time of the inactivity periods.


Keywords

cloud computing, cloud computing, Cloud computing

Last updated on 2019-16-10 at 03:18