A QoC‐based model for performance and QoE trade‐off in distributed systems. (20th June 2017)

• Record Type: Journal Article
• Authors: D'Amato, André; Dantas, Mario
• Is Part Of: Concurrency and computation. Volume 29:Number 18(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-20
• Subjects: distributed systems -- job management -- QoC -- QoE -- Quality of Context -- resource allocation -- throughput -- users' satisfaction; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4202
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: This paper proposes a quality of context (QoC)–based model to enhance trade‐off between system performance and users quality of experience (QoE) in distributed systems. The QoE considers the users subjective perceptions of quality on technology services and depends on context attributes related to the users expectations. In addition, context information can be used to improve performance in context‐aware systems. Therefore, the QoC‐based model proposed in this paper aims to establish performance and QoE trade‐off for resource allocation in distributed systems. With the massive data growth over the past years, the demand for computing capacity has been notably growing in a proportional way. In view of that, the demand for distributed systems has also increased significantly due to the advantages of distributed computing such as cost‐benefit gains, large processing capacity, and an extensive capability for resources sharing. In this sense, the large‐scale utilization of distributed systems has generated problems concerning resource allocation because of the heterogeneous characteristics and continuous evolution of distributed computational devices, resulting in several challenges in terms of environment performance and users' satisfaction trade‐off. The model conceived in this paper provides for an experimental evaluation considering performance and QoE trade‐off by means of simulations. Simulations were performed in order to study complex distributed systems scenarios. Experimental results showed that the proposed QoC model policy has an enhanced performance in comparison with classical approaches.