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

2020 ◽  
Vol 33 (8) ◽  
pp. e4370 ◽  
Author(s):  
Li Xingjun ◽  
Shao Zhiwei ◽  
Cheng Hongping ◽  
Bayan Omar Mohammed
Keyword(s):  
Internet Of Things ◽  
Load Balancing ◽  
Optimization Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization

scholarly journals Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network

2021 ◽  
Author(s):  
Uday Chourasia ◽  
Sanjay Silakari
Keyword(s):  
Neural Network ◽  
Load Balancing ◽  
Optimization Algorithm ◽  
Dynamic Load Balancing ◽  
Service Availability ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Polynomial Neural Network ◽  
Optimal Load ◽  
Significant Factors

Abstract In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. Here providing an appropriate service is considered a major challenge in the heterogeneous environment. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. In order to provide optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. In addition to this, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. In addition to this, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

scholarly journals Meta-heuristic-based offloading task optimization in mobile edge computing

2021 ◽  
Vol 17 (6) ◽  
pp. 155014772110230
Author(s):  
Aamir Abbas ◽  
Ali Raza ◽  
Farhan Aadil ◽  
Muazzam Maqsood
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Response Time ◽  
Mobile Devices ◽  
Optimization Algorithm ◽  
Edge Computing ◽  
Optimal Selection ◽  
Mobile Edge Computing ◽  
Grey Wolf ◽  
Grey Wolf Optimization

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

scholarly journals Closed Loop Control of PV System Using Grey Wolf Optimization Algorithm under Partial Shading Condition

2020 ◽  
Vol 994 ◽  
pp. 012011
Author(s):  
Dr. J. Jayaudhaya ◽  
D. Rajasekaran ◽  
J. Sumithra ◽  
J. C. Vinitha ◽  
S. Karkuzhali
Keyword(s):  
Optimization Algorithm ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pv System ◽  
Partial Shading ◽  
Loop Control

scholarly journals Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

2021 ◽  
Vol 1821 (1) ◽  
pp. 012038
Author(s):  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hopfield Neural Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization
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