Authors - Balvinder Pal Singh, Thangaraju B Abstract - Modern computing systems face the dual challenge of meeting escalating performance demands, driven especially by AI and ML workloads, while operating under stringent thermal constraints. As energy consumption continues to rise, conventional thermal mitigation techniques like frequency throttling often compromise performance and increase cooling costs, threatening long-term sustainability. This paper proposes a multi-level, software-centric approach to address this challenge through intelligent, temperature-aware scheduling. Leveraging evolutionary techniques, specifically Genetic Algorithm (GA), the proposed model reorders jobs at the OS scheduler level based on thermal impact and energy profiles. A secondary optimization phase further fine-tunes job execution using dynamic slice adjustment for thermally intensive tasks. Simulation results obtained using custom-integrated simulation framework leveraging GEM5, McPAT, and Hotspot tools, demonstrate 28% overall performance improvement, 53% reduction in thermal violations and a 15% decrease in energy consumption with 80% of the tasks executed without performance degradation. This approach was validated using representative benchmark workloads, optimizing both energy and temperature profiles. This AI-augmented, multi-level scheduling strategy significantly enhances thermal efficiency and performance, offering a scalable solution for next-generation high-performance computing environments.
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