A Novel Bio-Inspired Hybrid Algorithm (NBIHA) for Efficient Resource Management in Fog Computing

Rafique, Hina; Shah, Munam Ali; Islam, Saif ul; Maqsood, Tahir; Khan, Suleman; Maple, Carsten

doi:10.1109/access.2019.2924958

Cited by 129 publications

(56 citation statements)

References 38 publications

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“…Resource allocation represents a technique that is used to optimize the utilization of resources and reduce the required costs for processing [ 32 ]. Fulfillment time of a task is an important aspect that should be considered since it can impact the completion of resource allocation [ 33 ].…”

Section: Evaluation Framework For Resource Management Algorithms Imentioning

confidence: 99%

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Mijuskovic

Chiumento

Bemthuis

et al. 2021

Sensors

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Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.

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Section: Evaluation Framework For Resource Management Algorithms Imentioning

confidence: 99%

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Mijuskovic

Chiumento

Bemthuis

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…In the year 2018, Rafique et al [87] proposed novel bio-inspired hybrid algorithm (NBIHA) for load balancing in fog environment. Average response time and energy consumption have reduced by applying efficient task scheduling.…”

Section: Year-wise Review Of Load Balancing Techniquesmentioning

confidence: 99%

A systematic study of load balancing approaches in the fog computing environment

Kaur

Aron

2021

J Supercomput

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Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.

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“…The devices can efficiently upload tasks to the fog node and gets the response within the time-bound. Rafique et al [34] proposed a resource allocation strategy in fog network. A hybrid modified cat swarm optimization algorithm (MCSO) and a modified particle swarm optimization algorithm (MPSO) is used for task allocation at the fog nodes.…”

Section: A Fog Computing Frameworkmentioning

confidence: 99%

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment for Smart Cities

et al. 2021

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Fog computing has proved its importance over legacy cloud architectures for computation, storage, and communication where edge devices are used to facilitate the delay-sensitive applications. The inception of fog nodes has brought computing intelligence close to the end-devices. Many fog computing frameworks have been proposed where edge devices are used for computation. In this paper, we proposed a simulation framework for fog devices that can use end devices to handle the peak computation load to provide better Quality of Services (QoS). The regional fog nodes are deployed at network edge locations which are used as an intelligent agent to handle the computation requests by either scheduling them on local servers, cloud data centers, or at the under-utilized end-user devices. The proposed device-to-device resource sharing model relies on Ant Colony Optimization (ACO) and Earliest Deadline First(EDF) Algorithm to provide a better quality of service using device available at multi-layer design. The concept of using IoT devices as fog nodes has improved the performance of legacy fog based systems. The proposed work is benchmarked in terms of system cost, efficiency, energy, and quality of service. Further, the proposed framework is with xFogSim in terms of task efficiency.

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A Novel Bio-Inspired Hybrid Algorithm (NBIHA) for Efficient Resource Management in Fog Computing

Cited by 129 publications

References 38 publications

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification

A systematic study of load balancing approaches in the fog computing environment

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment for Smart Cities

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