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Link to original content: https://doi.org/10.1007/s10586-022-03710-3
ROGI: Partial Computation Offloading and Resource Allocation in the Fog-Based IoT Network Towards Optimizing Latency and Power Consumption | Cluster Computing Skip to main content
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ROGI: Partial Computation Offloading and Resource Allocation in the Fog-Based IoT Network Towards Optimizing Latency and Power Consumption

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Abstract

Fog computing has risen into a popular topic in recent years with the idea of deploying computation and communication closer to end-users. The capability of the fog model to face serious challenges like high latency and power consumption is of paramount research significance, notably in the context of the Internet of Things (IoT), given the volume and requirements of IoT applications. Accordingly, issues such as using the capacity of the fog layer maximally, minimizing latency while maintaining reliability, and efficiently distributing the workload across the network can be well explored. This motivated us to propose a scheme for partial computation offloading and resource allocation in the fog-based IoT network with the goal of optimizing latency and power consumption (ROGI). In this research, the nodes in all network layers are involved in processing the workload. Also, the power consumption in the end-users layer is reduced. Additionally, the fog nodes collaborate to improve system reliability and provide more resources for handling users’ requests. Furthermore, the concept of partial offloading is adopted, which would potentially lead to higher flexibility in resource management and provide the opportunity to leverage more resources in each layer of IoT architecture. Moreover, the whole model is decomposed into three problems, each of which is solved via optimization techniques. Extensive simulations are carried out to show the performance of the proposed scheme.

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Availability of data

The data supporting the findings of this study are generated in our simulations, in which some variables are static and some follow probability distributions. We explained all the information needed to replicate the simulations in the section “Performance Evaluation.”

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Benyamin T. Tabarsi & Ali Rezaee

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

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  1. Benyamin T. Tabarsi
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  2. Ali Rezaee
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Correspondence to Ali Rezaee.

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Tabarsi, B.T., Rezaee, A. & Movaghar, A. ROGI: Partial Computation Offloading and Resource Allocation in the Fog-Based IoT Network Towards Optimizing Latency and Power Consumption. Cluster Comput 26, 1767–1784 (2023). https://doi.org/10.1007/s10586-022-03710-3

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