Optimizing Workflow Scheduling for Web Servers in Hybrid Cloud Computing: Balancing Delay Sensitivity and Performance Efficiency

This research tackles the tough problem of balancing delay sensitivity with performance efficiency when scheduling workflows on web servers in hybrid‑cloud settings. Conventional scheduling approaches often fail to meet strict application delay limits while also keeping resource use and energy consumption optimal. To address this, the paper presents the Delay‑Aware and Performance‑Efficient Power Optimization (DAPEEO) strategy, built for heterogeneous edge‑cloud infrastructures. DAPEEO improves workflow execution by cutting power use, boosting throughput, and lowering both computational cost and execution time. The study reports that DAPEEO achieved: – a 4.217 % drop in power consumption, – a 19.51 % rise in throughput, – a 62.38 % reduction in computational costs, when compared

directly with the baseline Deadline‑Constrained Cost Optimization for Hybrid Clouds (DCOH) model. Compared with other reference baselines, average power consumption fell by 40.993 % and 90.384 %. For M.Tech researchers, the paper offers a solid mathematical and algorithmic foundation for simulating energy‑aware task scheduling. It gives practical guidance on modeling heterogeneous node capacities, setting deadline constraints, and weighing the trade‑offs between edge‑level latency and cloud‑level computational power.

Message: Hi FE, I need help with “Optimizing Workflow Scheduling for Web Servers in Hybrid Cloud Computing: Balancing Delay Sensitivity and Performance Efficiency” in “Computer Science & Engineering”