MFHS: A modular scheduling framework for heterogeneous system. (7th April 2020)

• Record Type: Journal Article
• Authors: Khiat, Abdelhamid; Tari, Abdelkamel; Guérout, Tom
• Is Part Of: Software, practice & experience. Volume 50:Number 8(2020)
• Page Start: 1463
• Page End: 1497
• Publication Date: 2020-04-07
• Subjects: distributed computing -- heterogeneous environment -- performance evaluation -- real‐experimental evaluation -- task scheduling; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2827
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Current innovative distributed architectures, proposing on‐line services, involve more and more computing resources. From a provider point of view, the platform management leads to challenging problematic relating to resource allocation, which involve different kind of quality of service parameters, the provider has to focus on to keep his platform reliable and efficient. MFHS is a modular generic framework, which can be adapted to any distributed computing environment. Structured in modules, MFHS allows to discover the existing computing resources in terms of computing performance, network throughput and disk I/O speeds (Resources Discovery module) and to predict how the experiment should behave ( P i value). As the setting up of real experiments is often complex, MFHS allows: to make theoretical experimentation (based on models), to use any kind of distributed emulators, or to deploy experiments on real‐experimental platforms. In this article, these three environments are used to highlight the reliability of MFHS (measured P i =90 % against 94 % for the predicted P i ). Deployment and scheduling studies have also been achieved using an experimental Cloud based on OpenStack while Emulab test‐bed has been used as emulator. During experiments, four QoS parameters are taken into account (Resources Monitoring module): energy consumption, cost, resource utilization, and makespan. These studies also includes a new heuristic called MMin, based on Max‐Min and Min‐Min algorithms. Experimentation section, proposes a detailed comparative analysis of these algorithms in terms of QoS results, while the abilities of the proposed heuristic MMin regarding the makespan metric is shown.