Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Kashyap, Vijaita; Kumar, Ashok

doi:10.1002/cpe.7183

Cited by 8 publications

(5 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the FC network might address the distance between edge devices and the cloud by placing servers near the edge to significantly decrease energy consumption and latency. In this situation, task offloading decisions between the machines at the border and FNs are influenced by the available resources, energy usage on offloading, and network load balancing 10 . Fog computing encounters several difficulties with increased IoT devices and service requirements.…”

Section: Introductionmentioning

confidence: 99%

“…In this situation, task offloading decisions between the machines at the border and FNs are influenced by the available resources, energy usage on offloading, and network load balancing. 10 Fog computing encounters several difficulties with increased IoT devices and service requirements. The other issues arise when several IoT devices request a service, which poses numerous challenges, such as increased latency, energy usage, and so forth.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Retracted: Minimal channel cost‐based energy‐efficient resource allocation algorithm for task offloading under FoG computing environment

Baskar,

Periasamy

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryThe Internet of Things (IoT) and fog computing (FC) can be integrated to manage massive data processing and complex networks. It can improve communication performance and provide robust computation for resource‐constrained IoT devices by deploying more fog nodes (FNs) at the edge/IoT networks. In this article, the optimal energy‐efficient fog resource allocation (oEeFRA) algorithm is proposed with the help of the minimal channel cost resource allocation (MiCCRA) method to ensure the load balancing and task offloading of the IoT‐FoG network under various constraints. Resource blocks (RBs) should be maintained higher than FNs in this proposed model to enhance the task‐offloading processes. The MiCCRA algorithm is proposed to provide a minimum of one FN and RB for each IoT device used in the IoT‐FoG network and also to ensure that weather each FN should be allotted with one or more RBs and IoT devices for efficient task offloading and load balancing activities. The energy efficiency (EE) of the proposed oEeFRA algorithm is computed through the MiCCRA algorithm by varying RB, FN, and IoT devices. The performance analysis shows that the proposed algorithm achieved the maximum EE of 6.12 × 109 bit/J, 3.019 × 1010 bit/J, and 5.69 × 1010 bit/J for varying RBs, FNs, and IoTs.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Retracted: Minimal channel cost‐based energy‐efficient resource allocation algorithm for task offloading under FoG computing environment

Baskar,

Periasamy

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Several methods based on LB are discussed in this survey [20], which overcomes the problem of overloaded data on the network. Latency, bandwidth, deadlines, cost, security, execution time, and execution time are some of the aspects that authors have focused on in LB.…”

Section: Related Workmentioning

confidence: 99%

“…𝑓𝑑 𝑗 𝑤 = 𝑓𝑑 𝑗 𝑟 * 𝑤 𝑞 + 1 𝑃𝑇 𝑖𝑗 𝑡 * 𝑤 𝑒 (20) where𝑓𝑑 𝑗 𝑟 is the rank of fdj, wqand we are the weight of fog device rank and the weight of task processing time respectively, such that wq+ we = 1.…”

Section: Phase 3: Determining Fog Device Weightmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Liu et al [12] demonstrated a fog-based privacy-preservation scheme for SG data aggregation that enables the service provider for multiple function queries on encryption of meter data. The authors of [20][21][22][23][24] have worked on IoT applications and events where Markov model has been used. However, none of them is suitable for a smart grid environment.…”

Section: Related Workmentioning

confidence: 99%

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

et al. 2023

View full text Add to dashboard Cite

The tremendous growth of about 8 billion devices connected to each other in various domains of Internet of Things (IoT)-based applications have attracted researchers from both industry and academia. IoT is a network of several devices connected with each other to provide sensing capabilities, particularly in smart grid (SG) environment. Various challenges such as the efficient handling of massive IoT data can be addressed with advances in fog computing. The secure data aggregation challenge is one such issue in IoT-based smart grid systems, which include millions of smart meters. Typical SG-based data aggregation approaches have high computation and communication costs, however, many efforts have been made to overcome these limitations while leveraging fog computing but no satisfactory results have been obtained. Moreover, existing solutions also suffer from high storage requirements. The traditional data aggregation schemes such as GCEDA (Grouping of Clusters for Efficient Data Aggregation) and SPPDA (Secure Privacy-Preserving Data Aggregation) also suffer from a few shortcomings. SPPDA follows a mixed aggregation architecture that includes trees and clusters which can lead to some performance complexities and is not energy-efficient, whereas GCEDA does not support heterogeneity. To overcome these problems, this research provides a fog-assisted strategy for secure and efficient data aggregation in smart grid. The concept of smart grid is implemented in fog environment, which was not the case in previous schemes. We used communication between smart meters (SMs) and fog nodes (FNs) to transmit confidential data in compressed form towards FN. The FN further aggregates the received data which can then be updated in cloud repositories later. We presented two algorithms—data aggregation and data extraction at FN and cloud, respectively, to achieve secure communication. The performance of the proposed strategy has been evaluated against existing data aggregation techniques GCEDA and SPPDA for various performance parameters such as storage, communication cost and transmission cost. The proposed scheme overcomes the limitation of heterogeneity and mixed aggregation which was faced in GCEDA and SPPDA and the results revealed outstanding performance in comparison with both, so the proposed solution can be used in a smart grid environment for efficient and secure data transmission.

show abstract

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Cited by 8 publications

References 86 publications

Retracted: Minimal channel cost‐based energy‐efficient resource allocation algorithm for task offloading under FoG computing environment

Retracted: Minimal channel cost‐based energy‐efficient resource allocation algorithm for task offloading under FoG computing environment

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

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

Contact Info

Product

Resources

About