Efficient Load Balancing Algorithm for Task Preprocessing in Fog Computing Environment

Manju, Anamala Balaji; Sumathy, S.

doi:10.1007/978-981-13-1927-3_31

Cited by 31 publications

(20 citation statements)

References 11 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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“…ELBA—Efficient Load Balancing Algorithm: The min-min algorithm is implemented in the fog where fog nodes are divided in clusters and the algorithm determines the task which has minimum enforcement time and appoints it a particular node. That node is able to process it in a faster manner [ 39 ]. When a cluster is busy, the controller node inspects neighborhood clusters that contain ’inactive’ fog nodes and sends activity to the node which presents lowest latency.…”

Section: Classification Of Resource Management Algorithms Applied mentioning

confidence: 99%

“…It could be effective to process the activity in the cloud or, instead, leave it to have a delay due to pre-processing at the fog nodes. In another situation, where two or more neighboring ’inactive’ nodes are accessible, the node with the smallest latency can transmit the job activity [ 39 ]. Two factors need to be deliberated to calculate latency: one refers to the number of stand-by requests that need to be supplied in the clusters and the other refers to the inactive node’s distance from the task originator.…”

Section: Classification Of Resource Management Algorithms Applied mentioning

confidence: 99%

“…Two factors need to be deliberated to calculate latency: one refers to the number of stand-by requests that need to be supplied in the clusters and the other refers to the inactive node’s distance from the task originator. Calculation of the lowest distance between the source node and a fog node or a cloud data center can be determined by using Equation ( 1 ) [ 39 ]. N represents minimum latency, S is the source from where a particular activity is re-transmitted, C is the nearest cloud data center, and n depicts the number of fog nodes.…”

Section: Classification Of Resource Management Algorithms Applied mentioning

confidence: 99%

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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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“…FC is not meant to compete with cloud computing but boost and strengthen cloud computing efficacy [58]. Low latency, mobility, position awareness, scalability, security, and interaction with heterogeneous devices are supported by this technology [59].…”

Section: E Fog Computingmentioning

confidence: 99%

IoT Provisioning QoS based on Cloud and Fog Computing

Samann

Zeebaree

Askar

2021

JASTT

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The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

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Efficient Load Balancing Algorithm for Task Preprocessing in Fog Computing Environment

Cited by 31 publications

References 11 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

IoT Provisioning QoS based on Cloud and Fog Computing

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