tmcclell
commented
1 year ago Funding request for research paper and discussions of possible expectations. Concerns raised over the relevance of the paper to the Carbon Aware SDK and cloud provider participation. Not accepting the proposal at this time over concerns on previous collaboration, transparency and communication.
Scope
Temporal and spatial workload shifting are crucial for reducing the carbon emissions of cloud computing. However, several challenges can hinder their implementation. Firstly, acquiring data on grid carbon intensity can be challenging, as this information may not be easily accessible or comprehensible for all regions or offered by every cloud provider. Secondly, implementing temporal and spatial workload shifting might entail additional expenses, such as data transfer fees, increased management overhead, or potential impacts on reserved instance pricing. Companies must carefully weigh the cost implications of their workload shifting strategies. Thirdly, data protection regulations and residency requirements may restrict a company's ability to move workloads across different data center locations or store data in specific regions. Fourthly, managing workloads across multiple data centers and implementing advanced scheduling and load balancing can be complex. Companies need to invest in appropriate tools, resources, and expertise to effectively manage these processes and automate workload shifting based on energy efficiency and carbon emissions criteria. These challenges could prevent companies from implementing temporal and spatial workload shifting to reduce their cloud computing footprint.
The primary objective of this research is to conduct an in-depth investigation of regions with significant potential for temporal and spatial workload shifting and provide practical recommendations, valuable tools, and best practices for overcoming the challenges associated with implementing these strategies in cloud computing. To address this complex problem, we will analyze carbon intensity data covering the globe from WattTime and the UNFCCC, along with the data center distribution data of major cloud providers (e.g., AWS, Microsoft Azure, Google Cloud, Oracle Cloud, IBM Cloud, Alibaba Cloud, etc.). The goal is to deliver a comprehensive data-driven analysis on when and where to shift workloads in the cloud at a global scale.
Green Software Foundation Research Proposal - Ziliang Zong and Henry Richardson.docx