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Reliable scheduling and resource allocation for IoT applications in fog computing

posted on 2023-05-28, 11:19 authored by Ranesh Kumar Naha
The massive development of ubiquitous computing leads the modern world to latency sensitive Internet of Things (IoT) applications such as smart transportation systems, healthcare services and emergency response services, in order to enable a better quality of life. These services generate a huge amount of data which need to be processed near to the edge. These kinds of applications can be handled effectively and efficiently using Cloud infrastructure because of the on-demand services and scalability feature of the Cloud. However, managing IoT applications in the Cloud exclusively is not a good solution for some applications, especially for those which are latency-sensitive. Thus, Fog computing has emerged which resides between the Cloud and the end devices. Generally, IoT devices and sensors are connected to the Fog devices. These Fog devices are located in close proximity and are responsible for intermediate computation and storage. Allocating resources for user applications to the highly dynamic, heterogeneous and complex Fog environment is challenging. In addition, the user might change their requirements dynamically and also require better reliability from the providers. Hence, reliable resource allocation and task scheduling techniques are required which consider the dynamic behaviour of the users' requirements. In Fog devices, available resources are changing over time since they are not dedicated to running Fog applications. More-over, resource failure and link failure could occur frequently in the Fog environment because of a lack of central management, the autonomous characteristics of the devices and wireless connectivity. Furthermore, most of the Fog devices are battery-powered; therefore, resource allocation and scheduling techniques need to be energy-efficient. To address these problems, this thesis proposes several resource allocation and failure handling techniques to make the Fog environment reliable. The influence of strict execution time and data transfer time is also considered during resource allocation and scheduling. In the Fog, some users may request cost-effectiveness, rather than fast execution. Hence, cost-effectiveness is also investigated. An evaluation of the proposed methods was tested in a simulated environment. This thesis adds to the body of the knowledge by making the following contributions: 1. An extensive survey on architecture, resource allocation and scheduling and failure handling in the Fog computing environment. 2. A comprehensive study on Fog computing architecture to develop a simulation environment for Fog computing. 3. A deadline-based dynamic resource allocation and provisioning in an hierarchical and hybrid fashion with dynamic user behaviour. 4. A multi-criteria-based dynamic user behaviour aware resource allocation in which resources are dynamic. 5. A fuzzy logic-based failure handling mechanism to handle predicted and unpredicted failures in the Fog environment, in order to ensure robust scheduling. 6. A multiple linear regression-based energy-aware resource allocation mechanism to ensure reliable execution when most of the devices have limited available energy for operation.


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Copyright 2021 the author Chapter 2 includes portions of the following published article Copyright 2018 IEEE. Reprinted, with permission, from : Naha, R. K., Garg, S., Georgakopoulos, D., Jayaraman, P. P., Gao, L., Xiang, Y., Ranjan,R., 2018. Fog computing : survey of trends, architectures, requirements, and research directions, IEEE access, 6, 47980-48009. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of the University of Tasmania's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to to learn how to obtain a License from RightsLink. Chapter 4 appears to be the equivalent of a pre-print version of an article published as: Naha, R. K., Garg, S., Chan, A., Battula, S. K., 2020. Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment, Future generation computer systems, 104, 131-141. Chapter 5 includes portions of the following published article: Naha, R. K., Garg, S., 2021. Multi-criteria based dynamic user behaviour-aware resource allocation in fog computing, ACM transactions on internet of things, 2(1), 1-31. An article posted on arXiv included portions of chapter 6. The article has a Creative Commons Attribution 4.0 International (CC BY 4.0) license (, and is cited as: Naha, R. K., Garg, S., Amin, M. B ., Ranjan, R., 2021. Fuzzy logic-based robust failure handling mechanism for fog computing. Submitted on 10 Mar 2021. An article posted on arXiv included portions of chapter 7. The article has a Creative Commons Attribution 4.0 International (CC BY 4.0) license (, and is cited as: Naha, R. K., Garg, S., Battula, S. K., Amin, M. B., Georgakopoulos, D., 2021. Multiple linear regression-based energy-aware resource allocation in the fog computing environmentng. Submitted on 10 Mar 2021.

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