Load balancing in cloud environment using enhanced migration and adjustment operator based monarch butterfly optimization

Kaviarasan, R.; Harikrishna, Pillutla; Arulmurugan, A.

doi:10.1016/j.advengsoft.2022.103128

Cited by 10 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input taken is the convolution output's small regions, which are sub-sampled to engender a single output. The PL's output (pool lyr ) is provided by, pool lyr = down (max (Con lyr )) (22) Where, the downsampling that retains the maximal value in the pooled area is signified as down(max(Con lyr ))). By utilizing flattening, the output is transformed into a single long linear vector after all convolutions pooling layers.…”

Section: Classificationmentioning

confidence: 99%

“…B Raveendranadh and S Tamilselvan (34) proposed the intrusion detection system centered at rock hyraxes swarm optimization algorithm. Similarly, monarch butterfly optimization (22) is discussed for load balancing in cloud environment and cuckoo search algorithm-trained radial basis function ( 9) is proposed for application layer DDoS attack detection by H Beitollahi, DM Sharif and M Fazeli.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RETRACTED: An efficient framework for application layer DDoS attack detection and mitigation for Mobile Ad Hoc Network

Salunke

Kurumbanshi

2023

Preprint

View full text Add to dashboard Cite

In today’s era of heterogeneous networks, MANET is an evident answer to anytime anywhere network. It is an accumulation of self-organizing mobile devices that communicates with each other wirelessly. A major threat to the mobile node's security and its communication in MANET is the Distributed Denial of Service (DDOS) attack. for researchers its very challenging to separate flash events from application layer DDOS attacks even though various ideas were provided by the prevailing models for attack detection and mitigation. The proposed novel Xavier SoftPlus Convolutional Neural Network (X-SPCNN), Double Shift Right and Compliment (DSRC), and PCC-based Interpolation based – Crossover Mutation Invasive Weed Optimization (I-CMIWO) techniques form an efficient model for DDOS Attack Detection and Mitigation in MANET with Differentiation from Flash Events. The key envelopment of this paper is the identification of unique features for DDoS attack detection with Brownian motion-centered Harris Hawks Optimization (BM-HHO). The projected X-SPCNN classifier which classifies the user as a legitimate user and Non-Legitimate based on chosen features is provided it hives a classification accuracy of 96.56%. proposed I-CMIWO and DSRC provide better results in monitoring and mitigation as compared to prevailing algorithms. Dissimilar IDS datasets which are created in unalike years and dissimilar investigational DDoS traffic generation tools are considered datasets to provide a better response in a distributed environment. The outcomes exhibited proposed model give better performance in assessment parameters than prevailing algorithms.

show abstract

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: An efficient framework for application layer DDoS attack detection and mitigation for Mobile Ad Hoc Network

Salunke

Kurumbanshi

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This performance degradation is quantified using Eq. (10), where the term ( ) represents CPU utilization associated with VM i during the migration process. This equation provides a measure of the performance impact of live migrations, which is crucial for optimizing resource allocation and minimizing SLA violations.…”

Section: Resultsmentioning

confidence: 99%

“…Cloud providers offer three primary service categories, namely Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), leveraging web service technology [7,8]. Notable examples include Amazon for IaaS [9], Google for PaaS [10], and Salesforce for SaaS [11], all renowned as leading cloud providers worldwide. On one front, cloud providers offer their computational resources to fulfill users' Quality of Service (QoS) requirements, and on the other, they must effectively manage their Total Cost of Ownership (TCO) to thrive in the increasingly competitive cloud market [12].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Linear Regression Models for Resource Usage Prediction in Dynamic Cloud Environments

2024

IJACSA

View full text Add to dashboard Cite

In response to the diverse resource utilization patterns observed across enterprises, this study proposes the utilization of adaptable cloud services. A novel system framework is presented, capturing and logging resource consumption at discrete intervals. Subsequently, this recorded data serves as input for a linear regression model, functioning as a machine learning tool to predict resource utilization in forthcoming intervals, leveraging historical data stored within the regression module. To bolster the resilience of the linear regression model, various effective meta-heuristic techniques are integrated alongside the conventional linear regression methodology, facilitating more accurate anticipation of overloaded or under-loaded resource conditions before their occurrence in real-world scenarios. Simulations demonstrate that the hybrid algorithm, named Whale Optimization Algorithmbased Linear Regression (WOA-LR), outperforms Genetic Algorithm-Linear Regression (GA-LR), Particle Swarm Optimization-Linear Regression (PSO-LR), JAYA-LR, and traditional Linear Regression (LR) in achieving desired objective functions and significantly reducing Mean Squared Error (MSE). This approach holds promise for more accurate resource utilization prediction and optimization in dynamic cloud environments.

show abstract

“…This method also finds resource failure rates to improve the reliability of all submissions. The researchers developed a system to guarantee that each cloud node is balanced correctly [9]. A novel algorithm having multiple objectives based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm -a RL technique that makes the ABC algorithm run more quickly-is proposed as an independent task scheduling approach for cloud computing [10].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Lahande,

Kaveri,

Saini

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Cloud computing is a technology that enables the delivery of various computing services over the Internet. The Resource Scheduling (RS) and Load Balancing (LB) mechanisms are essential for the cloud to provide consistent results. The submitted tasks by the users are computed on the cloud platform using its Virtual Machines (VMs). The cloud ensures an ideal LB mechanism, where no VMs will be overloaded or idle. This research paper focuses on this LB mechanism by experimenting in the WorkflowSim environment and computing tasks using the Sipht task dataset. The RS algorithms First Come First Serve (FCFS), Maximum -Minimum (Max -Min), Minimum Completion Time (MCT), Minimum -Minimum (Min -Min), and Round-Robin (RR) are utilized to balance the computational load of VMs. The experiment was conducted in four phases, where the Sipht task dataset varied in task length in each phase. Each phase included sixteen scenarios, where each scenario differed from another by the number of VMs used. The final results of this experiment convey that the load balanced by the algorithms

show abstract

Load balancing in cloud environment using enhanced migration and adjustment operator based monarch butterfly optimization

Cited by 10 publications

References 15 publications

RETRACTED: An efficient framework for application layer DDoS attack detection and mitigation for Mobile Ad Hoc Network

RETRACTED: An efficient framework for application layer DDoS attack detection and mitigation for Mobile Ad Hoc Network

Enhanced Linear Regression Models for Resource Usage Prediction in Dynamic Cloud Environments

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Contact Info

Product

Resources

About