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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

An MCDM and partial computations based deadline and energy-aware real-time workflow scheduling technique for fog integrated cloud environment

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pp. 1091–1103Vol. 46Issue 4-AMay 2025DOI: 10.47974/JIOS-1894XML
Received:
09 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1894
Pages:
1091–1103

Abstract

The phenomenal rise of Internet of Things(IoT) has driven the ascend of fog computing as a distributed model to reduce delays in networks. Generally, fog devices are resource-restricted while IoT applications are becoming computationally demanding which require certain QoS to be accomplished within hard time limits. Therefore, it is preferable for IoT jobs to finish their processing within deadline limits by producing approximate results than producing accurate result late. The placement of IoT jobs on fog and cloud resources for execution is a widely-recognized NP-hard problem. We study the placement of real-time IoT workflows in a fog and cloud infrastructure by applying approximate computations and TOPSIS. Our methodology aims to place complete as well as partial tasks in the available idle schedule holes in the schedules of fog as well as cloud resources. The proposed technique is verified through simulation experiments and is contrasted with state-of-the-art techniques on different performance measures. The experimental findings establish that the proposed technique is able to render better performance compared to its alternatives in terms of performance metrics like SLA violation ratio, response time and energy consumption at an insignificant loss of 0.15% of result precision for all experimental scenarios that are taken into consideration.

Keywords

Subject Classifications

00A72

References

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