Analysis of Workloads for Cloud Services

Title

Analysis of Workloads for Cloud Services

Creator

Sankaran L.; Saleema J.S.; Suleiman B.

Description

Capturing best quality datasets for a study is the first evidence for better outcomes of research. If the analysis are based on such datasets, then the metrics, the characteristics and few factors determines proof point for well proven theories. Hence it is obvious that we rely on the best possible ways to arrive at such data acquiring sources. It can be either based on historical techniques or from the innovations in application of it to industry. This paper introduces a mapping framework for analyzing, and characterizing data previously used by research community and how they are made to fit for Cloud systems, i.e. using 'workloads' and 'datasets' as the 'refined definitions'. It was contributed in the past two decades within the scientific community setting their own workflow analysis mechanisms. The framework thus is validated by acquiring a sample workload per layer of cloud. The sources are form the literature that are available from existing scientific theories. These workloads are then experimented against the three tiers of the cloud computing ie., IaaS(Infrastructure as a Service), PaaS(Platform as a Service), & SaaS(Software as a Service). The selected data is analyzed by the authors for an offline model presented here based on the Machine Learning tool-kits. There are future studies planned for and to be experimented in a cloud auto scaled environment with online model as well. 2022 IEEE.

Source

Proceedings - 2022 IEEE/ACIS 7th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2022, pp. 117-123.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Cloud Computing; Cloud Tiers; Data sets; Worflows; Workloads

Coverage

Sankaran L., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Saleema J.S., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Suleiman B., University of Sydney, Faculty of Engineering, Sydney, Australia

Rights

Restricted Access

Relation

ISBN: 978-166546582-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/BCD54882.2022.9900564

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141439570&doi=10.1109%2fBCD54882.2022.9900564&partnerID=40&md5=22c940ecb7675593d62470dc2e07c85f