Sustainability of Load Balancing Techniques in Fog Computing Environment: Review

Sulimani, Hamza; Alghamdi, Wael Y.; Jan, Tony; Bharathy, Gnana; Prasad, Mukesh

doi:10.1016/j.procs.2021.07.015

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…In [39], the authors presented the P2PFaaS framework, a software suite which enables the testing and benchmarking of scheduling and loadbalancing algorithms among sets of real nodes. [40] this paper explores recent articles to determine the possible research gaps and opportunities to implement an efficient solution for load balancing in fog environments after analyzing and assessing the existing solutions. In this study [41], the authors proposed a secure and energy-aware fog computing architecture,and implemented a load-balancing technique to improve the complete utilization of resources with an SDN-enabled fog environment.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

A. A. Gad-Elrab,

S. Alsharkawy,

E. Embabi

et al. 2024

IJCNC

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Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision- making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.

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Section: Related Workmentioning

confidence: 99%

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

A. A. Gad-Elrab,

S. Alsharkawy,

E. Embabi

et al. 2024

IJCNC

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“…In essence, control systems in multi-processing environments come in two forms: central and distributed. Central control, a traditional algorithm, suffers from reliability issues, as system failure can occur if the primary controller malfunctions (Sulimani, Alghamdi et al 2021). Consequently, recent research favours distributed systems, where each computing unit functions independently.…”

Section: Hyboff Designmentioning

confidence: 99%

“…This Section assesses the proposed algorithm and demonstrates how the hybrid offloading structure outperforms other classical LB schemes. Generally, the essential requirement of an effective LB is to keep all the computing units equally loaded by avoiding overloaded or underloaded cases (Sulimani, Alghamdi et al 2021). The RU% of servers are used to evaluate the effectiveness of LB.…”

Section: Preliminary Explanationsmentioning

confidence: 99%

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Sulimani,

Ramezani

et al. 2023

Preprint

Self Cite

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Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SOS) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (DOS) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create a uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SOS (64%) and DOS (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SOS and DOS, respectively. In summary, HybOff substantially contributes to load balancing and offloading research in fog computing.

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“…[9][10][11][12] Latency in time-sensitive applications, such as smart grids or unmanned vehicle management can result in major problems. [13][14][15][16] A fog network can use the available local computing and storage resources near the IoT edge devices and can thus provide necessary computations to the IoT edge devices, providing an opportunity to reduce the latency and bandwidth requirements due to being closer to IoT edge devices than cloud servers. [17][18][19][20] Cloud servers and fog networks provide alternative options for IoT edge devices to access computing resources.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, cloud servers are often located far from IoT edge devices, thus introducing latency and bandwidth challenges between users and computing nodes 9‐12 . Latency in time‐sensitive applications, such as smart grids or unmanned vehicle management can result in major problems 13‐16 . A fog network can use the available local computing and storage resources near the IoT edge devices and can thus provide necessary computations to the IoT edge devices, providing an opportunity to reduce the latency and bandwidth requirements due to being closer to IoT edge devices than cloud servers 17‐20 …”

Section: Introductionmentioning

confidence: 99%

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani

Sajjad

Alghamdi

et al. 2022

Trans Emerging Tel Tech

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A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.

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Sustainability of Load Balancing Techniques in Fog Computing Environment: Review

Cited by 10 publications

References 14 publications

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

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