Resource Oriented Architecture for Cloud and big data with Resource Workflow Management to Support Secure by Design Operation

Abstract

Introduction: The advent of cloud computing and big data technologies has revolutionized how data is stored, processed, and analyzed. However, the rapid expansion of these domains has introduced significant challenges in resource management, particularly in achieving high-performance computation

Objectives: Resource-Oriented Architecture (ROA), a paradigm rooted in efficient allocation, monitoring, and utilization of distributed resources, has emerged as a promising solution for addressing these challenges. This study delves into the application of ROA for cloud and big data management and computation, focusing on optimizing resource allocation, ensuring scalability, and enhancing computational efficiency.

Methods: The research develops the methods of task/job identification by utilizing the metadata which can use the cloud and big data workflow components to determine resource allocation. The identification process carried out on a task/job. The cloud and big data workflow components consist of task management, resource capacity management, communication and transition, and interaction.

Results: This paper underscores the potential of Resource-Oriented Architecture as a transformative approach for cloud and big data management. By enabling precise, scalable, and efficient resource management, ROA addresses the key challenges associated with high-performance computation in these domains.

Conclusions: By focusing on dynamic resource allocation, task management, and efficient data processing, ROA enables organizations to optimize their use of cloud resources while handling the immense volume, variety, and velocity of big data.