Hill Climbing Load Balancing Algorithm on Fog Computing

Zahid, Maheen; Javaid, Nadeem; Ansar, Kainat; Hassan, Kanza; Khan, Muhammad KaleemUllah; Waqas, Muhammad

doi:10.1007/978-3-030-02607-3_22

Cited by 26 publications

(12 citation statements)

References 14 publications

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“…The computing capacity of the cloud data centre is 120,000 MIPS, and the uplink and downlink bandwidth are 2 and 1.9 Mbps, respectively. To better verify the feasibility and performance of the improved algorithm proposed in this study, under the same conditions, the improved chaotic firefly optimisation algorithm is compared with the classic load balancing algorithms such as hill clipping load balancing algorithm [16], load balancing algorithm [17], ISCM [10]. Contrast experiments were carried out.…”

Section: Methodsmentioning

confidence: 99%

Load balancing strategy for medical big data based on low delay cloud network

Yong¹

2020

J. eng.

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Section: Methodsmentioning

confidence: 99%

Load balancing strategy for medical big data based on low delay cloud network

Yong¹

2020

J. eng.

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“…HCLB—Hill Climbing Algorithm: HCLB algorithm is defined as a mathematical optimization technique that is used for searching and monitoring the loads among VMs [ 38 ]. This technique is established on a random solution to discover accessible VMs.…”

Section: Classification Of Resource Management Algorithms Applied mentioning

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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“…A load balancing technique which decreased the response time and processing time of fogs to consumers has been proposed in Ref. [17]. A multi-objective evolutionary algorithm using Tchebycheff decomposition is proposed in Ref.…”

Section: Related Workmentioning

confidence: 99%

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

et al. 2020

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Edge cloud is a cloud computing system built on edge infrastructure. Task scheduling optimization is the key technology to ensure the quality of service in edge cloud. However, the openness of the edge cloud environment challenges the load balancing and profit optimization of task scheduling. In this paper, we analyze the business process and optimization factors of task scheduling in edge cloud. First, we propose a resource constrained task scheduling profit optimization algorithm (RCTSPO), which consists of clustering preprocessing, classification, profit matrix construction and optimal scheduling strategy calculation. Clustering preprocessing gathers similar tasks into one class and perform a classification on the clustered tasks. Then construct the profit matrix for resource constrained task scheduling, and the optimal task scheduling strategy is obtained based on the constructed profit matrix. Second, Petri nets are used to construct the different components of edge cloud, such as resource, task, user request and virtual machine, thus forming the task scheduling model of edge cloud. Third, the properties of task scheduling model are verified by using the related theory and tools of Petri nets. Finally, several experiments are done to evaluate the proposed method, the simulation results show that the algorithm not only achieves the maximum profit, but also performs well in terms of time, reliability and load balancing of task scheduling.

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Hill Climbing Load Balancing Algorithm on Fog Computing

Cited by 26 publications

References 14 publications

Load balancing strategy for medical big data based on low delay cloud network

Load balancing strategy for medical big data based on low delay cloud network

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

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

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