Authors: Mohammad Samadi Gharajeh
Addresses: Polytechnic Institute of Porto, Porto, Portugal
Abstract: Resource discovery selects appropriate computing resources in cloud systems to accomplish the users' jobs. This paper proposes a knowledge and intelligent-based strategy for resource discovery in IaaS cloud systems, called KINRED. It uses a fuzzy system, a Multi-Criteria Decision Making (MCDM) controller and an artificial neural node to discover suitable resources under various changes on network metrics. The suggested fuzzy system uses hardware specifications of the computing resources in which CPU speed, CPU core, memory, disk, the number of virtual machines and utilisation rate are considered as inputs, and hardware type is considered as output of the system. The suggested MCDM controller makes proper decisions based on users' requirements in which CPU speed, CPU core, memory, and disk are assumed as inputs, and job type is assumed as output of the controller. Furthermore, the artificial neural node selects the computing resource having the highest success rate based on both outputs of the fuzzy system and MCDM controller. Simulation results show that the proposed strategy surpasses some of the existing related works in terms of the number of successful jobs, system throughput and service price.
Keywords: cloud computing; resource discovery; knowledge-based system; intelligent strategy; artificial neural node; decision support system; MCDM; multi-criteria decision making; fuzzy logic; the number of successful jobs; system throughput; service price.
International Journal of Grid and Utility Computing, 2021 Vol.12 No.2, pp.205 - 221
Received: 24 Jun 2019
Accepted: 09 Mar 2020
Published online: 30 Apr 2021 *