scholarly journals An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1583 ◽  
Author(s):  
Shanky Goyal ◽  
Shashi Bhushan ◽  
Yogesh Kumar ◽  
Abu ul Hassan S. Rana ◽  
Muhammad Raheel Bhutta ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Energy Resource ◽  
Whale Optimization Algorithm ◽  
Cloud Environment ◽  
Swarm Optimization ◽  
Cloud Technology ◽  
Whale Optimization

Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.

scholarly journals Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4893 ◽  
Author(s):  
Ivana Strumberger ◽  
Nebojsa Bacanin  ◽  
Milan Tuba ◽  
Eva Tuba
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
System Performance ◽  
Resource Scheduling ◽  
Whale Optimization Algorithm ◽  
Cloud System ◽  
Data Sets ◽  
Computing Paradigm ◽  
Whale Optimization

The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation–exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

scholarly journals Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

2020 ◽  
Author(s):  
Surbhi Vijh ◽  
Prashant Gaurav ◽  
Hari Mohan Pandey
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Lung Tumor ◽  
Tumor Detection ◽  
Whale Optimization Algorithm ◽  
Swarm Optimization ◽  
Whale Optimization

Abstract In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

scholarly journals A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Nonlinear Parameter ◽  
Whale Optimization Algorithm ◽  
Grey Model ◽  
Living Standard ◽  
Grey Prediction ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Whale Optimization

Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.

scholarly journals Highly Precise Modified Blue Whale Method Framed by Blending Bat and Local Search Algorithm for the Optimality of Image Fusion Algorithm

2020 ◽  
Vol 2 (4) ◽  
pp. 195-208
Author(s):  
Sayantan Dutta ◽  
Ayan Banerjee
Keyword(s):  
Local Search ◽  
Image Fusion ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Local Search Algorithm ◽  
Fusion Algorithm ◽  
Whale Optimization ◽  
Nature Inspired Algorithm

Image fusion has gained huge popularity in the field of medical and satellite imaging for image analysis. The lack of usages of image fusion is due to a deficiency of suitable optimization techniques and dedicated hardware. In recent days WOA (whale optimization algorithm) is gaining popularity. Like another straightforward nature-inspired algorithm, WOA has some problems in its searching process. In this paper, we have tried to improve the WOA algorithm by modifying the WOA algorithm. This MWOA (modified whale optimization algorithm) algorithm is amalgamed with LSA (local search algorithm) and BA (bat algorithm). The LSA algorithm helps the system to be faster, and BA algorithm helps to increase the accuracy of the system. This optimization algorithm is checked using MATLAB R2018b. Simulated using ModelSim, and the synthesizing is done using Xilinx Vivado 18.2 synthesis tool. The outcome of the simulation result and the synthesis result outshine other metaheuristic optimization algorithms.

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