Katleho Moloi, University of South Africa, South Africa

Cloud computing infrastructures are increasingly being deployed in regions where power supply is intermittent and unreliable, posing significant challenges to service continuity and efficient resource utilization. Conventional cloud scheduling strategies typically assume stable energy availability and therefore perform poorly under fluctuating power conditions. This paper proposes an Energy-Adaptive Predictive Scheduling (EAPS) framework designed to enable resilient cloud operation in intermittently powered environments. The proposed architecture integrates short-term energy forecasting with adaptive workload orchestration and energy-aware node management, allowing computing workloads to be dynamically aligned with predicted energy availability. The scheduling problem is formulated as a multiobjective optimization task that maximizes service availability and workload completion while minimizing energy consumption and migration overhead. To solve this problem efficiently, a lightweight predictive scheduling algorithm is developed to perform proactive workload consolidation and migration under energy constraints. Experimental evaluation demonstrates that the pro-posed approach significantly improves service availability, workload completion rate, and node utilization compared to conventional scheduling strategies. The results highlight the potential of predictive energy-aware orchestration for enabling resilient and sustainable cloud computing infrastructures in power-constrained environments.

Energy-Adaptive Cloud Computing, Intermittent Power Systems, Energy-Aware Scheduling, Predictive Resource Management, Sustainable Cloud Computing.