Resource Sharing in Public Cloud System with Evolutionary Multi-agent Artificial Swarm Intelligence

Beiran Chen, Yi Zhang, George Iosifidis

Abstract: Artificial Intelligence for IT operations (AIOps) is an emerging research area for public cloud systems. The research topics of AIOps have been expanding from robust and reliable systems to cloud resource allocation in general. In this paper we propose a resource sharing scheme between cloud users, to minimize the resource utilization while guaranteeing Quality of Experience (QoE) of the users. We utilise the concept of recently emerged Artificial Swarm Intelligence (ASI) for resource sharing between users, by using Artificial-Intelligence-based agents to mimic human user behaviours. In addition, with the variation of real-time resource utilisation, the swarm of agents share their spare resource with each other according to their needs and their Personality Traits (PT). In this paper, we first propose and implement an Evolutionary Multi-robots Personality (EMP) model, which considers the constraints from the environment (resource usage states of the agents) and the evolution of two agents’ PT at each sharing step. We then implement a Single Evolution Multi-robots Personality (SEMP) model, which only considers to evolve agent’s PT and neglects the resource usage states. For benchmarking we also implement a Nash Bargaining Solution Sharing (NBSS) model which uses game theory but does not involve PT or risks of usage states. The objective of our proposed models is to make all the agents get sufficient resources while reducing the total amount of excessive resources. The results show that our EMP model performs the best, with least iteration steps leading to the convergence and best resource savings.

External IDs:dblp:conf/icsoc/ChenZI20