<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In parallel and distributed computing, cloud computing is progressively replacing the traditional computing paradigm. The cloud is made up of a set of virtualized resources in a data center that can be configured according to users’ needs. In other words, cloud computing faces the problem of a huge number of users requesting unlimited jobs for execution on a limited number of resources, which increases energy consumption and the network cost of the system. This study provides a complete analysis of classic scheduling techniques specifically for handling data-intensive workloads to see the effectiveness of the energy and network costs of the system. The workload is selected from a real-world data center. Moreover, this study offers the pros and cons of several classical heuristics-based job scheduling techniques that take into account the time and cost of transferring data from multiple sources. This study is useful for selecting appropriate scheduling techniques for appropriate environments.</p>
</abstract>

In parallel and distributed computing, cloud computing is progressively replacing the traditional computing paradigm. The cloud is made up of a set of virtualized resources in a data center that can be configured according to users’ needs. In other words, cloud computing faces the problem of a huge number of users requesting unlimited jobs for execution on a limited number of resources, which increases energy consumption and the network cost of the system. This study provides a complete analysis of classic scheduling techniques specifically for handling data-intensive workloads to see the effectiveness of the energy and network costs of the system. The workload is selected from a real-world data center. Moreover, this study offers the pros and cons of several classical heuristics-based job scheduling techniques that take into account the time and cost of transferring data from multiple sources. This study is useful for selecting appropriate scheduling techniques for appropriate environments.

Analysis of Energy and Network Cost Effectiveness of Scheduling Strategies in Datacentre

Sindh University Laar Campus at Badin. University of Sindh

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In parallel and distributed computing, cloud computing is progressively replacing the traditional computing paradigm. The cloud is made up of a set of...

Analysis of Energy and Network Cost Effectiveness of Scheduling Strategies in Datacentre

Online veröffentlicht: 28. Sept. 2023

Seitenbereich: 56 - 69

Eingereicht: 05. Juni 2023

Akzeptiert: 24. Juli 2023

DOI: https://doi.org/10.2478/cait-2023-0024

Schlüsselwörter
network-intensive, energy cost, data center, workload, cloud computing

© 2023 Afia Bhutto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Analysis of Energy and Network Cost Effectiveness of Scheduling Strategies in Datacentre

Online veröffentlicht: 28. Sept. 2023

Seitenbereich: 56 - 69

Eingereicht: 05. Juni 2023

Akzeptiert: 24. Juli 2023

DOI: https://doi.org/10.2478/cait-2023-0024

Schlüsselwörternetwork-intensive, energy cost, data center, workload, cloud computing

© 2023 Afia Bhutto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
network-intensive, energy cost, data center, workload, cloud computing