Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation Neema George, Anoop B K, Vinodh P Vijayan E3s Web of Conferences, 2024 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, Vinodh P. Vijayan Iaes International Journal of Artificial Intelligence, 2023 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, Anoop B. K. International Journal on Recent and Innovation Trends in Computing and Communication, 2023 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.
Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization Neema George, B. K. Anoop, Vinodh P. Vijayan Computer Systems Science and Engineering, 2023 Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application. When workload execution, accuracy, and cost are accurately stabilized in opposition to the best possible framework in real-time, efficiency is attained. In addition, every workload or application required for the framework is characteristic and these essentials change over time. But, the existing method was failed to ensure the high Quality of Service (QoS). In order to address this issue, a Tricube Weighted Linear Regression-based Inter Quartile (TWLR-IQ) for Cloud Infrastructural Resource Optimization is introduced. A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources (i.e., CPU, RAM, and network bandwidth utilization) based on the usage history in each cloud server. Then, Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration. Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics. The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine (VM) Migrations as compared to the state-of-the-art methods.
Wireless IoT Security Management Enhancement and Optimization using Various Elements C. Sahaya Kingsly, Neema George, Neena Joseph, K. Johnpeter, Sruthy. K. Joseph, K. A. Mohamed Riyazudeen Proceedings 5th International Conference on Smart Systems and Inventive Technology Icssit 2023, 2023 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.
Survival study on resource utilization and task scheduling in cloud Neema George, K.G. Nandhakumar, Vinodh P Vijayan Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems Icesc 2021, 2021 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.
