Abstract
The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results.
Highlights
Cloud computing (CC) has undergone rapid growth since users began to notice its great advantage over traditional IT systems
If there are no available resources, or if there are fewer resources than those demanded by the service, the global manager (GMA) determines that the physical node is not part of the global assignment process
This research is one of the first to propose the use of a multi-agent systems (MAS) in the framework of control and surveillance systems in CC environments
Summary
Cloud computing (CC) has undergone rapid growth since users began to notice its great advantage over traditional IT systems. Current state-of-the-art research is based on methods that use centralized algorithms focused on mathematical and heuristic models [15,16,17], neither of which ensures the efficiency of the system, or even its availability in the case of a system failure In general terms, these algorithms add or eliminate the resources assigned to a particular computational service, according to the SLA established with the end user, the consumption of energy in the CC environment, and the actual state (available resources) of the CC environment [18]. We have developed a computational resource distribution model to be used in distributed environments, capable of managing resources according to past experiences, and of dynamically adjusting the resources allocated to each service Using such a model makes it possible to achieve appropriate responses to demand and to increase the degree of effectiveness/efficiency of the solution and the state of the CC.
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