Sales-Based Models for Resource Management and Scheduling in Artificial Intelligence Systems

Abstract

Recent trends have shown a greatly increasing number of users in the digital world, so there is a need for a large number of resources. To handle these resources, there is the need to manage and schedule in an optimized manner using artificial intelligence (AI) systems. These systems deal with the business-common method of managing offerings. Ordinary models consolidate inbound deals, outbound bargains, account-based offerings, or a mix of diverse models. An organization model may gather multiple choices that an organization makes over a long period of time, considering a system, cycle, or trade. In our approach, computational resources are treated as commodities that can be bought and sold in a decentralized marketplace. Agents representing AI tasks or workloads participate in resource auctions, competing for the resources they need. The allocation of resources is determined through competitive bidding, where the highest bidder secures the required resources. This approach encourages efficient resource utilization and fair distribution based on the tasks’ priorities and value. Our sales-based models for resource management and scheduling offer a promising solution for optimizing AI systems’ resource allocation. By applying principles from auction theory and market dynamics, AI systems can become more adaptive, responsive, and efficient in managing computational resources, ultimately leading to improved performance and resource utilization.