Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: Legacy enterprise systems continue to serve as the operational backbone of critical industries such as banking, insurance, telecommunications, healthcare, and government administration. Despite their reliability and transactional stability, many of these systems face significant challenges due to inefficient batch processing and static job-scheduling mechanisms, which lead to prolonged execution windows, resource contention, delayed service-level agreements (SLAs), and reduced overall system performance. This paper presents an optimization-oriented framework for enhancing batch processing efficiency and job scheduling performance in legacy enterprise environments without requiring complete system modernization or migration. The proposed approach integrates adaptive workload analysis, dynamic resource allocation, predictive execution monitoring, and intelligent scheduling strategies to improve throughput and reduce processing delays. The study evaluates multiple scheduling techniques, including heuristic-based and priority-aware models, within enterprise-scale workload scenarios. Performance evaluation metrics such as batch completion time, CPU utilization, queue waiting time, and SLA compliance are analyzed to measure optimization effectiveness. Experimental findings indicate that intelligent scheduling and workload balancing can substantially improve operational efficiency, reduce execution bottlenecks, and enhance scalability in high-performance enterprise applications. The research demonstrates that legacy infrastructures can achieve significant performance improvements through targeted optimization strategies while maintaining operational continuity and cost efficiency.