A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Li, Xingjun; Zhiwei, Shao; Cheng, Hsu-Tang; Mohammed, Bayan Omar

doi:10.1002/dac.4370

Cited by 27 publications

(10 citation statements)

References 52 publications

(76 reference statements)

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“…The decryption method plans to attain the data (original) that is sent by the user. Here, using the user's private key, the encrypted data are decrypted, which is deduced in (18).…”

Section: Decryptionmentioning

confidence: 99%

“…An efficient technique for balancing the workload and also extending the object's lifetime in the IoT's design is called load balancing (LB) [17]. A few QoS parameters are improved by the best load balancers [18]. Likewise, a smart city network is affected by various security and also privacy problems, namely threats to privacy, integrity, and also user data's availability, false data injection, susceptibility to Sybil attack, together with a single point of failure because of centralized control [19].…”

Section: Introductionmentioning

confidence: 99%

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A secure data transmission and efficient data balancing approach for 5G‐based IoT data using UUDIS‐ECC and LSRHS‐CNN algorithms

et al. 2022

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Due to the realization of 5G technology, the Internet of things (IoT) has made remarkable advancements in recent years. However, security along with data balancing issues is proffered owing to IoT data's growth. The universally unique identifier short input pseudo‐random (SiP) hash‐based elliptic curve cryptography (UUDIS‐ECC) centred secure data transfer (DT) and linear scaling Rock Hyraxes swarm‐based convolutional neural network (LSRHS‐CNN) centred 5G IoT data balancing are proposed here to address those issues. Authentication, destination selection, validation, secure DT, and also load balancing are the proposed method's five phases. Initially, during the registration phase, the Length Nano ID (LNanoID) is created in the authentication. The user is permitted to further communicate if the LNanoID is matched with the already saved LNanoID. The destination is selected if the user is authorized. Utilizing the sender and the receiver's public key, the hash code is produced in the validation centre by the SiP hash function after destination selection. After that, by employing the UUDIS‐ECC algorithm, the IoT data is safely transmitted towards the destination. The 5G IoT data is balanced by using the LSRHS‐CNN algorithm during DT. Superior results are attained by the proposed methods analogized to existing research methods.

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“…The decryption method plans to attain the data (original) that is sent by the user. Here, using the user's private key, the encrypted data are decrypted, which is deduced in (18).…”

Section: Decryptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A secure data transmission and efficient data balancing approach for 5G‐based IoT data using UUDIS‐ECC and LSRHS‐CNN algorithms

et al. 2022

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“…Fuzzy GWO algorithm: Fuzzy GWO algorithm [82] is adapted to implement a modified load balancing algorithm in cloud computing and IoT. The objective is to achieve better response times with a balanced load.…”

Section: Gwo and Its Variationsmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

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“…AVS et al [24] stipulated that the improved Kmeans clustering algorithm could be applied to cloud computing, based on the task length, task priority, deadline and cost as the influencing factors of task classification, in order to classify tasks and virtual machines, thereby improving the performance and efficiency of cloud computing. Li Xingjun et al [25] designed a task scheduling algorithm based on Gray Wolf Optimization Algorithm (GWO), and aimed at reducing the response time and load, in order to achieve multi-objective optimization based on fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

Wang

2020

IEEE Access

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With the wide application of cloud computing technology, the services provided by cloud systems have become increasingly diverse, thus these systems are required to solve tasks of high variety and complexity, with a tremendously extensive amount of task data involved. That is why reasonable scheduling system resources are particularly important in cloud computing research. In this paper, a cloud computing system needs to take into account a wider range of cloud service resource types and collaborative optimization scheduling issues in order to solve the tasks at hand. Firstly, a new adaptive genetic algorithm (NAGA) was proposed. By improving the crossover mutation genetic operator, the algorithm was able to save excellent individuals as much as possible, enhance the algorithm's optimization ability, and greatly reduce the probability of the algorithm falling into the local optimal solution. Secondly, focusing on the main factors affecting service quality, such as task completion time, system load, and network bandwidth, an upgraded fitness operator method for the cloud resource collaborative optimization scheduling problem is set forth. Finally, an algorithm of cloud service resources based on an improved genetic algorithm (OSIG) is proposed. Experiments on the CloudSim cloud computing simulation platform demonstrate that the OSIG algorithm proposed in this paper can effectively optimize the resource scheduling strategy, shorten the task completion time, facilitate the system load balancing, and boost the system's service quality. The theoretical analysis was consistent with the experimental results.

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A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Cited by 27 publications

References 52 publications

A secure data transmission and efficient data balancing approach for 5G‐based IoT data using UUDIS‐ECC and LSRHS‐CNN algorithms

A secure data transmission and efficient data balancing approach for 5G‐based IoT data using UUDIS‐ECC and LSRHS‐CNN algorithms

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

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