MAS-Cloud plus : A novel multi-agent architecture with reasoning models for resource management in multiple providers

Nowadays, scientific and commercial applications are often deployed to cloud environments requiring multiple resource types. This scenario increases the necessity for efficient resource management. However, efficient resource management remains challenging due to the complex nature of modern cloud -distributed systems since resources involve different characteristics, technologies, and financial costs. Thus, optimized cloud resource management to support the heterogeneous nature of applications balancing cost, time, and waste remains a challenge. Multi -agent technologies can offer noticeable improvements for resource management, with intelligent agents deciding on Virtual Machine (VM) resources. This article proposes MAS-Cloud+, a novel agent -based architecture for predicting, provisioning, and monitoring optimized cloud computing resources. MAS-Cloud+ implements agents with three reasoning models including heuristic, formal optimization, and metaheuristic. MAS-Cloud+ instantiates VMs considering Service Level Agreement (SLA) on cloud platforms, prioritizing user needs considering time, cost, and waste of resources providing appropriate selection for evaluated workloads. To validate MAS-Cloud+, we use a DNA sequence comparison application subjected to different workload sizes and a comparative study with state-of-the-art work with Apache Spark benchmark applications executed on the AWS EC2. Our results show that to execute the sequence comparison application, the best performance was obtained by the optimization model, whereas the heuristic model presented the best cost. By providing the choice among multiple reasoning models, our results show that MAS-Cloud+ could provide a more cost-effective selection of the instances reducing approximate to 58% of execution average cost of WorkdCount, Sort, and PageRank BigDataBench benchmarking workloads. As for the execution time, the WorkdCount and PageRank present reduction, the latter with approximate to 58%. The results indicate a promising solution for efficient cloud resource management.