Semantic Orchestration of Heterogeneous Distributed Batch Workflows using Knowledge Graphs and Machine Learning

Abstract

Contemporary enterprise computing environments face unprecedented challenges in managing distributed batch processing operations across heterogeneous infrastructures that span multiple cloud providers and on-premises systems. Traditional batch processing orchestrators demonstrate significant deficiencies when confronted with complex computational workloads that require dynamic resource allocation, intelligent sequencing, and multi-objective optimization considering cost efficiency and environmental sustainability. The kg-ml-batch-orchestrator framework addresses these multifaceted challenges through strategic integration of Knowledge Graph technologies and Machine Learning methodologies, creating an intelligent decision-making layer that augments existing batch schedulers rather than replacing them. This framework-agnostic solution establishes semantic understanding of workflow interdependencies, enables proactive resource management through predictive analytics, and facilitates real-time optimization decisions that consider temporal pricing variations, carbon intensity fluctuations, and performance requirements simultaneously. The architectural design incorporates sophisticated dynamic resource allocation mechanisms, intelligent sequencing capabilities, proactive bottleneck mitigation strategies, and adaptive learning components that continuously improve system effectiveness through operational experience. Implementation across diverse enterprise environments demonstrates superior resource utilization efficiency, substantial reduction in job completion times, significant cost optimization achievements, and notable environmental impact improvements compared to conventional scheduling systems.