NEEMA GEORGE

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Engineering, Computer Engineering, Computer Engineering

Scopus Publications

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation
Neema George, Anoop B K, and Vinodh P Vijayan
EDP Sciences
Cloud computing has arisen as a shrewd and well known worldview for people and associations to work with the entrance and use of registering assets through the web.With the rapid growth of cloud computing technology, efficiently running big data applications within minimal time has become a significant challenge. In this dynamic and scalable environment, effective resource allocation and task scheduling of big data applications play pivotal roles in optimizing performance, enhancing efficiency, and ensuring cost-effectiveness. In environments involving remote computing, task scheduling is a crucial consideration. In order to effectively accomplish resource-optimal task scheduling and minimize overall task execution time, a novel technique called Multicriteria Generalized Regressive Neural Federated Learning (MGRNFL) is developed to address the particular issues in cloud systems. Tasks from several users arrive at the cloud server at the start of the procedure. The cloud server's job scheduler then uses Multicriteria Federated Learning to carry out resource-optimal task scheduling. A decentralized machine learning technique called federated learning (FL) enables model training across several tasks that are gathered from cloud computing customers. This decentralized approach primarily focuses on learning from datasets to obtain a global model by aggregating the results of local models. The proposed techniques involve two different steps: local training models and global aggregation models. In the local training model, the task scheduler determines the resource-optimal virtual machine in the cloud server using a Generalized Regression Neural Network (GRNN) based on multicriteria functions of the virtual machine, such as energy, memory, CPU, and bandwidth. Based on these objective functions, resource-efficient virtual machines are determined to schedule multiple user tasks. The locally updated models are then combined and fed into the global aggregation model. Calculated within the global aggregation model is the weighted total of locally updated findings. The algorithm iterates through this process till the maximum number of times. In order to schedule incoming tasks, the resource-optimal virtual machine is found. Various quantitative criteria are used for the experimental evaluation, including makespan, throughput in relation to the number of tasks, and task scheduling efficiency.


Multi-objective load balancing in cloud infrastructure through fuzzy based decision making and genetic algorithm based optimization
Neema George, Anoop Balakrishnan Kadan, and Vinodh P. Vijayan
Institute of Advanced Engineering and Science
Cloud computing became a popular technology which influence not only product development but also made technology business easy. The services like infrastructure, platform and software can reduce the complexity of technology requirement for any ecosystem. As the users of cloud-based services increases the complexity of back-end technologies also increased. The heterogeneous requirement of users in terms for various configurations creates different unbalancing issues related to load. Hence effective load balancing in a cloud system with reference to time and space become crucial as it adversely affect system performance. Since the user requirement and expected performance is multi-objective use of decision-making tools like fuzzy logic will yield good results as it uses human procedure knowledge in decision making. The overall system performance can be further improved by dynamic resource scheduling using optimization technique like genetic algorithm.


Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud
Neema George and Anoop B. K.
Auricle Technologies, Pvt., Ltd.
Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address  large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably.


Wireless IoT Security Management Enhancement and Optimization using Various Elements
C. Sahaya Kingsly, Neema George, Neena Joseph, K. Johnpeter, Sruthy. K. Joseph, and K. A. Mohamed Riyazudeen
IEEE
The old security measures are failing because of the exponential rate at which the technology environment is expanding, effectively outlawing modern technologies. Similarly, to that, Industry 4.0 needs modern, smart solutions to improve security and productivity. This assertion should be taken into consideration since traditional security methods cannot always safeguard rapidly created information and numbers. This is true because both the technology being targeted and the active and passive attack methods are developing quickly. The Internet of Things (IoT) will have 75 billion connected devices by 2025, which will undoubtedly produce massive amounts of data that must be safeguarded and protected at all costs as it travels around the globe. Failure to do so may result in undesirable circumstances. IoT is a part of Industry 4.0, hence an Industry 4.0 solution would be the ideal option for addressing the numerous IoT difficulties. To determine the optimal criteria for securing IoT, this article compares many elements of the fourth technological revolution, including Cloud Technology, Data Science, Cognitive Domains, and Blockchain. In addition, this study also suggests a hybrid method for enhancing and maximizing the security angle of IoT. Using the hybrid work id POW with Watch dog Mechanism added this work.


Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization
Neema George, B. K. Anoop, and Vinodh P. Vijayan
Computers, Materials and Continua (Tech Science Press)



Survival study on resource utilization and task scheduling in cloud
Neema George, K.G. Nandhakumar, and Vinodh P Vijayan
IEEE
Cloud computing provides different services to the registered users because users do not need to invest in computing infrastructure. Cloud infrastructure achieved enhanced resource service using various techniques depending on user requirements. Users have the potentiality in accessing several services provided by Cloud Infrastructure via the Internet. Cloud infrastructural resource scheduling allocated the arrival and service time for every resource with the cloud service provider. Cloud infrastructural resource optimization is the method of planning one or more resources for reducing the overall cost while attaining higher performance under given constraints. Several methods were designed for resource allocation, task scheduling in cloud infrastructure. But, some challenges like load instability and low quality of service were experienced by existing techniques due to different user requests in a heterogeneous environment. To overcome such issues, various resource allocations and task scheduling are reviewed.

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