Daily Archives: September 25, 2026

Authors: Mohana Ravi Teja Gutta

Abstract: Data migration of large volumes of data from legacy information systems to cloud-based architecture is becoming more and more common in enterprise digital transformation projects, traditional data migration methods usually suffer from issues related to the use of disparate data format, incompatibility of data schema, migration downtime, inefficient resource utilization, and insufficient data validation. These issues lead to lengthy migration process, higher migration risks, and lower migration performance. This research proposes a Cloud Native Enterprise Data Migration Framework (CN-EDMF) that combines data discovery, data dependencies, data harmonization, workload management, resource management, and data validation in one migration framework. The proposed framework employs metadata-based intelligence for identifying relationships within enterprise datasets, determining optimal migration sequence, and allocating cloud resources according to the characteristics of workloads. Moreover, automated mechanisms for ensuring data quality and data integrity verify that the process is executed successfully and reliably. The framework was evaluated experimentally using enterprise datasets varying between 5 TB to 25 TB and was compared with other existing frameworks including ServiceMiner (SM) and CloudNet (CN). As a result, CN-EDMF has managed to achieve the following results Migration Success Rate of 99.2%, Data Quality Preservation of 99.4%, Migration Reliability of 99.6%, and Resource Utilization Efficiency of 94.4%. The research proves the effectiveness of the proposed framework for improving the migration process. It provides an efficient solution for cloud-native enterprise migration.

DOI: https://zenodo.org/records/22957355

Published by: vikaspatanker

Authors: Avinash Yeluri

Abstract: The fast embrace of multi-cloud computing has made it possible for organizations to gain scalability, flexibility, availability, and cost efficiency. Nevertheless, the monitoring of workloads in multi-cloud environments has become increasingly difficult because of fragmented visibility, privacy issues, increased communication overhead, and increasing telemetry data. Centralized monitoring methods involve sending information from distributed locations to a centralized location, resulting in higher costs and increased risks. The FL-IMME framework, introduced in this study, utilizes federated learning and is aimed at intelligent monitoring that preserves data locality. The framework combines telemetry gathering, generation of local intelligence, federated aggregation, anomaly detection, predictive monitoring, and adaptive decision-making mechanisms to ensure privacy-preserving and scalable monitoring capabilities. The novel approach involves training of local monitoring models in each cloud environment individually and exchanging encrypted model parameters via federated learning. Experiments were performed on a large multi-cloud observability data set and the performance was compared to CCFRL and AHFLP methods. The results revealed better performance of FL-IMME with anomaly detection accuracy of 99.0%, failure prediction accuracy of 99.1%, privacy preservation score of 99.4%, and system reliability of 99.5%. In addition, the proposed framework lowered the communication overhead and monitoring latency while increasing the efficiency of resources utilization.

DOI: https://zenodo.org/records/22957300

Published by: vikaspatanker

Authors: Satya Prakash Prakki, Venkatarami reddy, Madhulika Gajjala

Abstract: Cloud HCM platforms are becoming a game-changer in payroll operations, offering an opportunity for automation, real-time data integration, streamlined processes, and scalable workforce management. While implementation-focused models of payroll transformation tend to focus on the process, they lack adequate attention to aspects of stakeholder accountability, governance, decision rights, compliance and change management. This poses problems such as ownership, slow decision making, integration issues, compliance risks and resistance to organizational change. This study suggests the Stakeholder-Centric Payroll Governance Model (SCPGM) to deal with the above limitations in the context of payroll transformation programs in Cloud HCM environments. The model proposed involves a framework of stakeholder identification, governance roles, decision-right matrix, risk and compliance controls, communication mechanisms and performance monitoring. The model aims to bring HR, payroll, IT, compliance, implementation partners and business leadership on board during the transformation process. Proposed quantitative evaluation criteria include implementation efficiency, decision time, accuracy of payroll, adherence to compliance, and satisfaction and transformation risk of stakeholders. Target decision-making time should be reduced by 20-30%, payroll processing efficiency should improve by 15-25%, and the stakeholder satisfaction with the transformation should be maintained at more than 90%. The proposed SCPGM offers a governance model to enhance accountability, collaboration, transparency and the results of Cloud HCM payroll transformation initiatives.

DOI: https://zenodo.org/records/22956245

Published by: vikaspatanker