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Distributed Computing Models for Enterprise Workflow Systems

Author(s) Ritesh Kumar
Country United States
Abstract Enterprise workflow systems increasingly leverage distributed computing models to enhance scalability, resilience, and efficiency. Traditional monolithic architectures struggle with dynamic workload management, fault tolerance, and high availability. This paper examines key distributed computing paradigms, including microservices-based orchestration, event-driven architectures, and consensus mechanisms such as Raft and Paxos, which enable reliable and scalable workflow execution. We analyze state-of-the-art workflow automation platforms, including Netflix Conductor, AWS Step Functions, and Temporal.io, assessing their strengths, limitations, and trade-offs in terms of latency, scalability, and fault tolerance. Furthermore, real-world case studies and performance benchmarks provide insights into practical deployment considerations for enterprise workflow orchestration. Finally, we discuss emerging trends such as AI- driven workflow optimization, decentralized execution models, and self-healing architectures, which are shaping the next generation of enterprise workflow systems.
Keywords Distributed Computing, Workflow Orchestration, Microservices, Serverless Computing, Event-Driven Architecture, Consensus Algorithms, Workflow Automation, Fault Tolerance
Published In Volume 5, Issue 6, June 2024
Published On 2024-06-05
Cite This Distributed Computing Models for Enterprise Workflow Systems - Ritesh Kumar - IJLRP Volume 5, Issue 6, June 2024. DOI 10.5281/zenodo.14905681
DOI https://doi.org/10.5281/zenodo.14905681
Short DOI https://doi.org/g85r7v
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