A heuristic approach to the multicriteria design of IaaS cloud infrastructures for Big Data applications. Issue 5 (12th January 2018)

• Record Type: Journal Article
• Authors: Arostegi, María; Torre‐Bastida, Ana; Bilbao, Miren Nekane; Del Ser, Javier
• Other Names: Camacho David guestEditor.; Novais Paulo guestEditor.
• Is Part Of: Expert systems. Volume 35:Issue 5(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-01-12
• Subjects: Big Data -- Cloud Computing -- IaaS -- multiobjective heuristics -- NSGA‐II; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12259
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed great opportunities for companies to shift their business model towards a fully digital strategy. A major obstacle in this matter is the requirement of highly specialized ICT infrastructures that are expensive and difficult to manage. It is at this point that the IaaS (infrastructure as a service) model offers an efficient and cost‐affordable solution to supply companies with their required computing resources. In the Big Data context, it is often a hard task to design an optimal IaaS solution that meets user requirements. In this context, we propose a methodology to optimize the definition of IaaS cloud models for hosting Big Data platforms, following a threefold criterion: cost, reliability, and computing capacity. Specifically, the proposed methodology hinges on evolutionary heuristics in order to find IaaS configurations in the cloud that optimally balance such objectives. We also define measures to quantify the aforementioned metrics over a Big Data platform hosted within an IaaS cloud model. The proposed method is validated by using real information from three IaaS providers and three Big Data platforms. The obtained results provide an insightful input for system managers when initially designing cloud infrastructures for Big Data applications.