A Framework for Carbon-aware Real-Time Workload Management in Clouds using Renewables-driven Cores
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915015545585664 |
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| author | Hewage, Tharindu B. Ilager, Shashikant Rodriguez, Maria A. Buyya, Rajkumar |
| author_facet | Hewage, Tharindu B. Ilager, Shashikant Rodriguez, Maria A. Buyya, Rajkumar |
| contents | 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 use renewables-driven cores in 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 the real-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 data center. We introduce the Green Cores concept 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 as openstack-gc. Using an experimental openstack-gc cloud 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 source openstack-gc at https://github.com/tharindu-b-hewage/openstack-gc. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_07628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework for Carbon-aware Real-Time Workload Management in Clouds using Renewables-driven Cores Hewage, Tharindu B. Ilager, Shashikant Rodriguez, Maria A. Buyya, Rajkumar Distributed, Parallel, and Cluster Computing 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 use renewables-driven cores in 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 the real-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 data center. We introduce the Green Cores concept 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 as openstack-gc. Using an experimental openstack-gc cloud 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 source openstack-gc at https://github.com/tharindu-b-hewage/openstack-gc. |
| title | A Framework for Carbon-aware Real-Time Workload Management in Clouds using Renewables-driven Cores |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2411.07628 |