Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

The immediate and quick spread of the coronavirus has become a life-threatening disease around the globe. The widespread illness has dramatically changed almost all sectors, moving from offline to online, resulting in a new normal lifestyle for people. The impact of coronavirus is tremendous in the healthcare sector, which has experienced a decline in the first quarter of 2020. This pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical results. The current situation motivated me to choose correlationbased development called correlation-based grey wolf optimizer to perform accurate classification. A proposed multistage model helps to identify Covid from Computed Tomography (CT) scan image. The first process uses a convolutional neural network (CNN) for extracting the feature from the CT scans. The Pearson coefficient filter method is applied to remove redundant and irrelevant features. Finally, the Grey wolf optimizer is used to choose optimal features. Experimental analysis proves that this determines the optimal characteristics to detect the deadly disease. The proposed model's accuracy is 14% higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image (SARS-CoV-2 CT) dataset. The COVID CT image dataset is 22% higher than the existing krill herd and bacterial foraging optimization techniques. The proposed techniques help to increase the classification accuracy of the algorithm in most cases, which marks the stability of the stated result. Comparative analysis reveals that the proposed classification technique to predict COVID-19 with maximum accuracy of 98% outperforms other competitive approaches.

show abstract

“…Sample CT images from the above datasets are stated in Figs. 1 and 2 respectively [26][27][28][29][30][31][32].…”

Section: Proposed Multistage Feature Selection Algorithmmentioning

confidence: 99%

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Bhuvaneshwari¹,

Ahmed²,

Masud³

et al. 2023

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The LS-II technique combines both the Figure 1: Architecture of the proposed method tendencies to produce highly efficient domestic and global search capabilities. These properties have led to its application in solving difficult problems [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Locust Search IImentioning

confidence: 99%

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Rani¹,

Chae²,

Nam³

et al. 2023

Intelligent Automation &Amp; Soft Computing

Self Cite

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data. WSNs use sensor nodes (SNs) to collect and transmit data. However, the power supplied by the sensor network is restricted. Thus, SNs must store energy as often as to extend the lifespan of the network. In the proposed study, effective clustering and longer network lifetimes are achieved using multi-swarm optimization (MSO) and game theory based on locust search (LS-II). In this research, MSO is used to improve the optimum routing, while the LS-II approach is employed to specify the number of cluster heads (CHs) and select the best ones. After the CHs are identified, the other sensor components are allocated to the closest CHs to them. A game theory-based energy-efficient clustering approach is applied to WSNs. Here each SN is considered a player in the game. The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest. The proposed multi-swarm with energy-efficient game theory on locust search (MSGE-LS) efficiently selects CHs, minimizes energy consumption, and improves the lifetime of networks. The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters, average energy consumption, lifespan extension, reduction in average packet loss, and end-to-end delay.

show abstract

“…Network latency specifies the distance from the UB to the DC. i.e., the delay or amount of time taken to transport the load between the UB and the DC [25]. Network traffic represents the amount of workload moving across a DC at any given time [26].…”

Section: Introductionmentioning

confidence: 99%

Efficient criticality oriented service brokering policy in cloud datacenters

Subramanian,

Natarajan

2024

IJECE

View full text Add to dashboard Cite

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.

show abstract

Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

Cited by 10 publications

References 32 publications

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Efficient criticality oriented service brokering policy in cloud datacenters

Contact Info

Product

Resources

About