Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

A Secure Multi-factor Authentication Protocol for Healthcare Services Using Cloud-based SDN

Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the network.Enterprises and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application requirement.The integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care services.It has improved the real-time monitoring of patients by medical practitioners.Patients’data get stored at the central server on the cloud from where it is available to medical practitioners in no time.The centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’privacy.In recent days,several schemes have been proposed to ensure the safety of patients’data.But most of the techniques still lack the practical implementation and safety of data.In this paper,a secure multi-factor authentication protocol using a hash function has been proposed.BAN(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient information.Security Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for simulation.The results prove that the proposed scheme ensures secure access to the database in terms of spoofing and identification.Performance comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.

Energy Efficient Unequal Fault Tolerance Clustering Approach

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

Load Balancing Algorithm for Migrating Switches in Software-Dened Vehicular Networks

In Software-Dened Networks(SDN),the divergence of the control interface from the data plane provides a unique platform to develop a programmable and exible network.A single controller,due to heavy load trafc triggered by different intelligent devices can not handle due to it’s restricted capability.To manage this,it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness.The ow of data through the multiple controllers also varies,resulting in an unequal distribution of load between different controllers.One major drawback of the multiple controllers is their constant conguration of the mapping of the switch-controller,quickly allowing unequal distribution of load between controllers.To overcome this drawback,Software-Dened Vehicular Networking(SDVN)has evolved as a congurable and scalable network,that has quickly achieved attraction in wireless communications from research groups,businesses,and industries administration.In this paper,we have proposed a load balancing algorithm based on latency for multiple SDN controllers.It acknowledges the evolving characteristics of real-time latency vs.controller loads.By choosing the required latency and resolving multiple overloads simultaneously,our proposed algorithm solves the loadbalancing problems with multiple overloaded controllers in the SDN control plane.In addition to the migration,our algorithm has improved 25%latency as compared to the existing algorithms.

Plant Disease Diagnosis and Image Classification Using Deep Learning

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem.Modern farming employs technology to improve productivity.Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity.Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s opinion.Deep learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant diseases.In this paper,the CNN model is proposed for the classification of rice and potato plant leaf diseases.Rice leaves are diagnosed with bacterial blight,blast,brown spot and tungro diseases.Potato leaf images are classified into three classes:healthy leaves,early blight and late blight diseases.Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study.The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%accuracy.The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases.Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure.Although various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s edges.The curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge enhancement.The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges.Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges.Therefore,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image.Afterwards C mean thresholding is used for the extraction of vessel.The recommended fusion approach is assessed on DRIVE dataset.Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result.The results demonstrate that the recommended method outperforms the traditional approaches.

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.