System and method for energy aware scheduling of artificial intelligence (ai) workloads

A system to execute Artificial Intelligence (AI) workloads in an AI data center, the system comprising:

• an application server, wherein the application server comprises:
• a processor, and
• a memory communicatively coupled with the processor, wherein the memory is configured to store one or more executable instructions, which cause the processor to:
• create one or more energy profiles for execution of each of one or more AI workloads based on one or more attributes associated with each of the one or more AI workloads, wherein each of the one or more energy profiles is indicative of energy consumption characteristics of each of the one or more AI workloads;
• categorize each of the one or more AI workloads into one or more execution categories based on the one or more energy profiles and a real-time energy availability of an electrical grid;
• create a dynamic schedule for execution of each of the one or more AI workloads for a pre-defined time based on the one or more execution categories, wherein the dynamic schedule is based on the real-time energy availability of the electrical grid and one or more user preferences for the execution of the one or more AI workloads;
• and
• execute each of the one or more AI workloads for the pre-defined time in the AI data center based on the dynamic schedule, wherein the execution of the one or more AI workloads is optimized based on the real-time energy availability of the electrical grid.

A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:

• creating one or more energy profiles for execution of each of one or more Artificial Intelligence (AI) workloads based on one or more attributes associated with each of the one or more AI workloads, wherein each of the one or more energy profiles is indicative of energy consumption characteristics of each of the one or more AI workloads;
• categorizing each of the one or more AI workloads into one or more execution categories based on the one or more energy profiles and a real-time energy availability of an electrical grid;
• creating a dynamic schedule for execution of each of the one or more AI workloads for a pre-defined time based on the one or more execution categories, wherein the dynamic schedule is based on the real-time energy availability of the electrical grid and one or more user preferences for the execution of the one or more AI workloads;
• and
• executing each of the one or more AI workloads for the pre-defined time in an AI data center based on the dynamic schedule, wherein the execution of the one or more AI workloads is optimized based on the real-time energy availability of the electrical grid.