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Cloud workload prediction based on workflow execution time discrepancies

Lookup NU author(s): Professor Raj Ranjan



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2018, The Author(s). Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (∼ 20%) better workload predictions for the future of simulated clouds than random workload selection.

Publication metadata

Author(s): Kecskemeti G, Nemeth Z, Kertesz A, Ranjan R

Publication type: Article

Publication status: Published

Journal: Cluster Computing

Year: 2019

Volume: 22

Issue: 3

Pages: 737-755

Print publication date: 01/09/2019

Online publication date: 16/10/2018

Acceptance date: 23/03/2018

Date deposited: 14/11/2018

ISSN (print): 1386-7857

ISSN (electronic): 1573-7543

Publisher: Springer New York LLC


DOI: 10.1007/s10586-018-2849-9


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GINOP-2.3.2-15-2016- 00037