A Framework for Carbon-Aware Real-Time Workload Management in Clouds Using Renewables-Driven Cores
Tharindu B. Hewage, Shashikant Ilager, Maria A. Rodriguez, Rajkumar Buyya · IEEE Transactions on Computers · 2025
Cloud platforms commonly exploit workload temporal flexibility to reduce their carbon emissions. They suspend/resume workload execution for when and where the energy is greenest. However, increasingly prevalent delay-intolerant real-time workloads challenge this approach. To this end, we present a framework to harvest green renewable energy for real-time workloads in cloud systems. We useRenewables-driven coresin servers to dynamically switch CPU cores between real-time and low-power profiles, matching renewable energy availability. We then develop a VM Execution Model to guarantee running VMs are allocated with cores in thereal-time power profile. If such cores are insufficient, we conduct criticality-aware VM evictions as needed. Furthermore, we develop a VM Packing Algorithm to utilize available cores across the servers. We introduce theGreen Coresconcept in our algorithm to convert renewable energy usage into a server inventory attribute. Based on this, we jointly optimize for renewable energy utilization and reduction of VM eviction incidents. We implement a prototype of our framework in OpenStack asopenstack-gc. Using an experimentalopenstack-gccloud and a large-scale simulation testbed, we expose our framework to VMs running RTEval, a real-time evaluation program, and a 14-day Azure VM arrival trace. Our results show: i) a 6.52× reduction in coefficient of variation of real-time latency over an existing workload temporal flexibility-based solution, and ii) a joint 79.64% reduction in eviction incidents with a 34.83% increase in energy harvest over the state-of-the-art packing algorithms. We open sourceopenstack-gcat https://github.com/tharindu-b-hewage/openstack-gc.