Thermal Elasticity-Aware Host Resource Provision for Carbon Efficiency on Virtualized Servers

Servers in modern data centers face increasing challenges from energy inefficiency and thermal-related outages, both of which significantly contribute to their overall carbon footprint. These challenges often arise from a lack of coordination between computational resource provisioning and thermal management capabilities. This paper introduces the concept of thermal elasticity, a system’s intrinsic ability to absorb thermal stress without requiring additional cooling, as a guiding metric for sustainable thermal management. Building on this, we propose a collaborative in-band and out-of-band resource provisioning framework that adjusts CPU allocation based on real-time thermal feedback. By leveraging a machine learning model and runtime monitoring, the framework dynamically provisions CPU clusters to virtual machines co-located on the same host. Evaluations on real servers with multiple workloads show that our method reduces peak power consumption from 5.2% to 9.6%, and lowers peak temperatures between 4°C and 6.5°C (up to 40°C in extreme cases). Carbon emissions are also reduced from 7% to 37% during SPEC benchmark runs. These results highlight the framework’s potential to alleviate stress on power and cooling infrastructure, thereby enhancing energy efficiency, reducing carbon footprint, and improving service continuity during thermal challenges.