# US 2026/0093532 A1 · System and method for energy aware scheduling of artificial intelligence (ai) workloads > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0093532 A1 - Document ID: US20260093532A1 - Status: Published application - Kind: A1 - Application number: 19/005,842 - Filed: 2024-12-30 - Published: 2026-04-02 - Earliest claimed: 2024-10-01 - Assignees: Wipro Limited - Applicants: Wipro Limited - Inventors: Prasanna Chandran MELNATAMI KRISHNARAM - Web page: https://patentgrep.com/patent/US20260093532A1 ## CPC - G06F 9/4893: {taking into account power or heat criteria (power management in computers in general G06F1/3203; thermal management in computers in general G06F1/206)} - G06F 9/5094: {where the allocation takes into account power or heat criteria (power management in computers in general G06F1/3203; thermal management in computers in general G06F1/206)} - G06F 9/485: {Task life-cycle, e.g. stopping, restarting, resuming execution (G06F9/4881 takes precedence)} - G06F 9/5072: {Grid computing} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The present invention relates to a method and system for execution of one or more AI workloads in an AI data center. The system is configured to create energy profiles for each AI workload, based on attributes associated with the AI workloads, which reflect their energy consumption characteristics. The system is configured to categorize AI workloads into execution categories based on the created energy profiles and a real-time energy availability of an electrical grid. The system is configured to create a dynamic schedule for execution of the AI workloads for a pre-defined time based on the execution categories. The dynamic schedule is based on the real-time energy availability of the electrical grid and user preferences for execution of the AI workloads. The system is configured to execute each of the AI workloads for the pre-defined time in the AI data center based on the dynamic schedule. ## Claims ### Claim 1 A method for execution of Artificial Intelligence (AI) workloads in an AI data center, the method comprising: creating, by an energy profiling unit of an application server, 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; categorizing, by a categorization unit of the application server, 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, by a scheduling unit of the application server, 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, by an execution unit of the application server, 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. ### Claim 2 (depends on 1) The method as claimed in claim 1, wherein the execution of each of the one or more AI workloads comprises: initiating execution of a first AI workload for the pre-defined time; transitioning from the first AI workload to a second AI workload on the execution of the first AI workload for the pre-defined time; creating a checkpoint associated with the first AI workload, wherein the checkpoint stores a state of ongoing execution of the first AI workload and data associated with the ongoing execution of the first AI workloads; initiating execution of the second AI workload from the one or more AI workloads for the pre-defined time; and resuming execution of the first AI workload for the pre-defined time based on the checkpoint on the execution of the second AI workload for the pre-defined time. ### Claim 3 (depends on 1) The method as claimed in claim 1, wherein the one or more AI workloads comprises at least one of Data Processing workloads, Machine Learning workloads, Deep Learning workloads, Natural Language Processing (NLP) workloads, Generative AI workloads, Computer Vision workloads, wherein the one or more AI workloads corresponds to either a training task or an inference task, and wherein the AI data center is a cloud-based data center configured for execution of the one or more AI workloads. ### Claim 4 (depends on 1) The method as claimed in claim 1, wherein the one or more attributes comprises operational parameters, data precision values, a page size, Graphics Processing Unit (GPU) hardware resources, input data format, type of languages, data size, data structure, preferred type of a pre-trained model, a pre-trained model framework and version, a checkpoint interval, a backup frequency, memory requirements, and parallelism settings, wherein the one or more attributes are indicative of an estimated energy consumption by each of the one or more AI workloads. ### Claim 5 (depends on 1) The method as claimed in claim 1, wherein the one or more execution categories comprises a low priority category, a medium priority category, and a high priority category, and wherein the pre-defined time is one of a first pre-defined time, a second pre-defined time, and a third pre-defined time. ### Claim 6 (depends on 5) The method as claimed in claim 5, wherein the low priority category comprises of a first set of AI workloads from the one or more AI workloads which are scheduled during the first pre-defined time, the energy availability of the electrical grid being below a first pre-defined threshold during the first pre-defined time, wherein the medium priority category comprises of a second set of AI workloads from the one or more AI workloads which are scheduled during the second pre-defined time, the energy availability of the electrical grid being within a range of the first pre-defined threshold and a second pre-defined threshold during the second pre-defined time, and wherein the high priority category comprises of a third set of AI workloads from the one or more AI workloads which are scheduled during the third pre-defined time window, the energy availability of the electrical grid being greater than the second pre-defined threshold during the third pre-defined time. ### Claim 7 (depends on 1) The method as claimed in claim 1, wherein creating the one or more energy profiles for each of the one or more AI workloads comprises: receiving energy limitation information associated with the electrical grid, wherein the energy limitation information comprises at least one of day-of-use constraints and peak power limits; receiving infrastructure information associated with the AI data center, wherein the infrastructure information comprises a hardware inventory and a software inventory, wherein the hardware inventory comprises at least one of Graphics Processing Unit (GPU), Central Processing Unit (CPU), memory, and network; receiving the one or more user preferences for the execution of the one or more AI workloads, wherein the one or more user preferences comprises data precision, size, format, and framework; and performing one or more preprocessing operations on the energy limitation information, the infrastructure information, and the one or more user preferences, wherein the one or more preprocessing operations corresponds to a normalizing operation and a validation operation. ### Claim 8 (depends on 7) The method as claimed in claim 7, wherein creating the one or more energy profiles for each of the one or more AI workloads comprises: creating a baseline energy profile for each of the hardware inventory by assessing a performance and the energy consumption characteristics of each of the hardware inventory and the software inventory; estimating one or more computational requirements for execution of the one or more AI workloads based on the one or more user preferences and data associated with the one or more AI workloads; estimating an energy consumption requirement for execution of the one or more AI workloads based on the one or more computational requirements; optimizing the energy consumption characteristics by adjusting a computational load distribution across the hardware inventory, wherein the adjusting is based on a trade-off between performance and energy efficiency in light of the one or more user preferences; and creating the one or more energy profiles comprising the energy consumption requirement for the hardware inventory and the software inventory based on the adjusting and the energy limitation information. ### Claim 9 (depends on 1) The method as claimed in claim 1, further comprising: generating one or more recommendations for updating the one or more energy profiles based on the real-time energy availability of the electrical grid. ### Claim 10 (depends on 7) The method as claimed in claim 7, further comprising: continuously monitoring for energy fluctuations within the hardware inventory and the real-time energy availability of the electrical grid; identifying a faulty hardware inventory based on the monitoring; and initiating creation of a checkpoint based on the energy fluctuations and the faulty hardware inventory. ### Claim 11 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. ### Claim 12 (depends on 11) The system as claimed in claim 11, wherein the processor is configured to execute each of the one or more AI workloads by: initiating execution of a first AI workload for the pre-defined time; transitioning from the first AI workload to a second AI workload on the execution of the first AI workload for the pre-defined time; creating a checkpoint associated with the first AI workload, wherein the checkpoint stores a state of ongoing execution of the first AI workload and data associated with the ongoing execution of the first AI workloads; initiating execution of the second AI workload from the one or more AI workloads for the pre-defined time; and resuming execution of the first AI workload for the pre-defined time based on the checkpoint on the execution of the second AI workload for the pre-defined time. ### Claim 13 (depends on 11) The system as claimed in claim 11, wherein the one or more AI workloads comprises at least one of Data Processing workloads, Machine Learning workloads, Deep Learning workloads, Natural Language Processing (NLP) workloads, Generative AI workloads, Computer Vision workloads, wherein the one or more AI workloads corresponds to either a training task or an inference task, and wherein the AI data center is a cloud-based data center configured for execution of the one or more AI workloads. ### Claim 14 (depends on 11) The system as claimed in claim 11, wherein the one or more attributes comprises operational parameters, data precision values, a page size, Graphics Processing Unit (GPU) hardware resources, input data format, type of languages, data size, data structure, preferred type of a pre-trained model, a pre-trained model framework and version, a checkpoint interval, a backup frequency, memory requirements, and parallelism settings, wherein the one or more attributes are indicative of an estimated energy consumption by each of the one or more AI workloads. ### Claim 15 (depends on 11) The system as claimed in claim 11, wherein the one or more execution categories comprises a low priority category, a medium priority category, and a high priority category, and wherein the pre-defined time is one of a first pre-defined time, a second pre-defined time, and a third pre-defined time. ### Claim 16 (depends on 15) The system as claimed in claim 15, wherein the low priority category comprises of a first set of AI workloads from the one or more AI workloads which are scheduled during the first pre-defined time, the energy availability of the electrical grid being below a first pre-defined threshold during the first pre-defined time, wherein the medium priority category comprises of a second set of AI workloads from the one or more AI workloads which are scheduled during the second pre-defined time, the energy availability of the electrical grid being within a range of the first pre-defined threshold and a second pre-defined threshold during the second pre-defined time, and wherein the high priority category comprises of a third set of AI workloads from the one or more AI workloads which are scheduled during the third pre-defined time window, the energy availability of the electrical grid being greater than the second pre-defined threshold during the third pre-defined time. ### Claim 17 (depends on 11) The system as claimed in claim 11, wherein the processor is configured to create the one or more energy profiles for each of the one or more AI workloads by: receiving energy limitation information associated with the electrical grid, wherein the energy limitation information comprises at least one of day-of-use constraints and peak power limits; receiving infrastructure information associated with the AI data center, wherein the infrastructure information comprises a hardware inventory and a software inventory, wherein the hardware inventory comprises at least one of Graphics Processing Unit (GPU), Central Processing Unit (CPU), memory, and network; receiving the one or more user preferences for the execution of the one or more AI workloads, wherein the one or more user preferences comprises data precision, size, format, and framework; and performing one or more preprocessing operations on the energy limitation information, the infrastructure information, and the one or more user preferences, wherein the one or more preprocessing operations corresponds to a normalizing operation and a validation operation. ### Claim 18 (depends on 17) The system as claimed in claim 17, wherein the processor is configured to create the one or more energy profiles for each of the one or more AI workloads by: creating a baseline energy profile for each of the hardware inventory by assessing a performance and the energy consumption characteristics of each of the hardware inventory and the software inventory; estimating one or more computational requirements for execution of the one or more AI workloads based on the one or more user preferences and data associated with the one or more AI workloads; estimating an energy consumption requirement for execution of the one or more AI workloads based on the one or more computational requirements; optimizing the energy consumption characteristics by adjusting a computational load distribution across the hardware inventory, wherein the adjusting is based on a trade-off between performance and energy efficiency in light of the one or more user preferences; and creating the one or more energy profiles comprising the energy consumption requirement for the hardware inventory and the software inventory based on the adjusting and the energy limitation information. ### Claim 19 (depends on 11) The system as claimed in claim 11, wherein the processor is configured to generate one or more recommendations for updating the one or more energy profiles based on the real-time energy availability of the electrical grid. ### Claim 20 (depends on 17) The system as claimed in claim 17, wherein the processor is configured to: continuously monitor for energy fluctuations within the hardware inventory and the real-time energy availability of the electrical grid; identify a faulty hardware inventory based on the monitoring; and initiate creation of a checkpoint based on the energy fluctuations and the faulty hardware inventory. ### Claim 21 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.