Metaheuristic Optimization Technique for Load Balancing in Cloud-Fog Environment Integrated with Smart Grid

Naqvi, Syed Aon Ali; Javaid, Nadeem; Butt, Hanan; Kamal, Muhammad Babar; Hamza, Ali; Kashif, Muhammad

doi:10.1007/978-3-319-98530-5_61

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…However, the focus of the research is mainly on the cloud-fog architecture of a smart grid scenario, and there is neither a clear description of the proposed algorithms nor a load balancing mechanism. Similar research is conducted in [20] where the ACO algorithm is used for scheduling IoT tasks on the fog nodes in a SG. The proposed algorithm focused on minimizing the response time of the IoT tasks.…”

Section: Background and Related Workmentioning

confidence: 99%

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

2020

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The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes. INDEX TERMS Fog computing, Internet of Things, quality of service, task offloading and scheduling.

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Section: Background and Related Workmentioning

confidence: 99%

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

2020

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“…These features can be used to build more controllable, and reliable smart grid infrastructures that offer energy efficiency and cost reductions [45]. In addition, fog computing can help in maintaining the balance between electricity consumption and distribution [47]. Furthermore, cloud services can be deployed for critical management and planning applications in smart grids such as power load prediction, power utilization assessment, and failure discovery [48].…”

Section: Smart Energy Applicationsmentioning

confidence: 99%

Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

et al. 2021

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Several cities have recently moved towards becoming smart cities for better services and quality of life for residents and visitors, with: optimized resource utilization; increased environmental protection; enhanced infrastructure operations and maintenance; and strong safety and security measures. Smart cities depend on deploying current and new technologies and different optimization methods to enhance services and performance in their different sectors. Some of the technologies assisting smart city applications are the Internet of Things (IoT), fog computing, and cloud computing. Integrating these three to serve one system (we will refer to it as integrated IoT-fog-cloud system (iIFC)) creates an advanced platform to develop and operate various types of smart city applications. This platform will allow applications to use the best features from the IoT devices, fog nodes, and cloud services to deliver best capabilities and performance. Utilizing this powerful platform will provide many opportunities for enhancing and optimizing applications in energy, transportation, healthcare, and other areas. In this paper we survey various applications of iIFCs for smart cities. We identify different common issues associated with utilizing iIFCs for smart city applications. These issues arise due to the characteristics of iIFCs on the one side and the requirements of different smart city applications on the other. In addition, we outline the main requirements to effectively utilize iIFCs for smart city applications. These requirements are related to optimization, networking, and security.

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“…Mainly, this algorithm is inspired by other existing load balancing algorithms, i.e. honey bee behaviour-based load balancing, particle swarm optimization (PSO), ant colony optimization (ACO) [72,76]. Song et al [80] presented a scheme owned to transform physical nodes into virtual nodes by cloud automation technology in the fog computing environment.…”

mentioning

confidence: 99%

“…Mohanty et al [79] and Naqvi et al [76] provided meta-heuristic approach-based load balancing. They used the PSO algorithm for equal distribution of tasks and to enhance resource utilization.…”

mentioning

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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Metaheuristic Optimization Technique for Load Balancing in Cloud-Fog Environment Integrated with Smart Grid

Cited by 22 publications

References 6 publications

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

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

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