Survey on Service Migration, load optimization and Load Balancing in Fog Computing Environment

Baburao, D.; Pavankumar, T.; Prabhu, C.S.R.

doi:10.1109/i2ct45611.2019.9033579

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…They also introduced some of the evaluation parameters and simulation tools used for load-balancing methods in the fog system. In addition, Baburao, et al [15] surveyed some of the techniques of service migration, load balancing, and load optimization in fog computing. Furthermore, Kaur and Aron [16] surveyed load-balancing approaches systematically in a 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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“…This is the procedure of distribute workloads and calculating possessions in the Fog background. Load evaluation provides Fog broker services to administer applications or workloads demand by allocating possessions for Fog computing systems Baburao et al (2019). summarized different types of load-balancing methods used in Fog computing environment Fan and Ansari (2020).…”

mentioning

confidence: 99%

Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Baburao

Pavankumar

Prabhu³

2021

Appl Nanosci

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Fog computing is the new technology era, which is deployed as a middle layer computing system between Internet of Things (IoT) devices and cloud computing systems, where data are acquired and analyzed at the border of the system. Cloud computing offers many advantages, and drawbacks of network congestions due to the huge amount of information coming from various sources, which causes higher latency for immediate responsive devices. To conquer these problems fog computing provides solutions as they are deployed near the edge of end users. The load examination concern arises in fog computing when a great amount of new IoT user applications are connected to the fog nodes. To efficiently handle load balancing, a particle swarm optimization-based Enhanced Dynamic Resource Allocation Method (EDRAM) has been proposed which in turn reduces task waiting time, latency and network bandwidth consumption and improves the Quality of Experience (QoE). The Enhanced Dynamic Resource Allocation Method (EDRAM), which in turns helps for allocating the required resource by removing the long-time inactive, unreferenced and sleepy services from the Random-Access Memory.

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A Load Balancing-Based Real-Time Traffic Redirection Method for Edge Networks

Liu

Xing

et al. 2021

Simulation Tools and Techniques

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Survey on Service Migration, load optimization and Load Balancing in Fog Computing Environment

Cited by 8 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

Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

A Load Balancing-Based Real-Time Traffic Redirection Method for Edge Networks

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