• Raghu Ram Bojanapalli

As organizations increasingly distribute their data assets and processing workloads across multiple cloud environments, workflow orchestration in multi-cloud data fabrics has become a compelling challenge. Although previous research has focused mainly on addressing challenges like integration, interoperability, and security in multi-cloud data fabrics, the challenge of latency and its impact on workflow efficiency and user experience has not been satisfactorily resolved. Existing orchestration frameworks tend to apply static scheduling or ignore the dynamic network and resource conditions typical of multi-cloud configurations. This work fills this gap by introducing a latency-aware workflow orchestration framework specifically designed for multi-cloud data fabrics. The framework utilizes real-time latency monitoring, adaptive scheduling algorithms, and predictive analytics for optimizing workflow execution paths to ensure operational efficiency and compliance with service-level agreements. Through dynamic most appropriate cloud resource selection and data transfer paths as a function of latency profiles, the introduced methodology minimizes end-to-end processing latency and improves overall workflow responsiveness. Empirical evaluation using benchmark datasets and real multi-cloud scenarios results in considerable workflow execution time savings and improved reliability compared to conventional orchestration approaches. This work provides a novel perspective on workflow management by focusing on latency awareness as a guiding principle in orchestrating data-intensive processes across heterogeneous, geographically dispersed cloud platforms. The findings highlight the necessity for further research into adaptive, latency-sensitive orchestration approaches in support of the changing needs of multi-cloud data ecosystems, thus resolving a critical research gap pointed out in current literature.

Latency-aware orchestration, multi-cloud data fabrics, workflow optimization, adaptive scheduling, real-time latency monitoring, cloud interoperability, data-intensive applications, service-level objectives, predictive analytics, distributed cloud computing.

"Latency-Aware Workflow Orchestration in Multi-Cloud Data Fabrics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.g28-g46, December-2021, Available :http://www.jetir.org/papers/JETIR2112603.pdf

"Latency-Aware Workflow Orchestration in Multi-Cloud Data Fabrics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppg28-g46, December-2021, Available at : http://www.jetir.org/papers/JETIR2112603.pdf