MFHS: A modular scheduling framework for heterogeneous system

Abstract

SummaryCurrent 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 (Pi 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 Pi=90% against 94% for the predicted Pi). 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.

An efficient list‐based task scheduling algorithm for heterogeneous distributed computing environment

“…For scheduling task t 2 , The ST and EFT values are calculated using Equations ( 6) and (7). The ST value of the task t 2 on the processor (p 1 , p 2 , p 3 ) is (27,27,9) and the EFT value of the task t 2 on the processor (p 1 , p 2 , p 3 ) is (40,46, 27 ). As processor p 3 has the least EFT, task t 2 is assigned to processor p 3 .…”

“…Next, the task in the priority queue to be scheduled is task t 6 . Then the calculated values of ST and EFT on the processors [p 1 , p 2 , p 3 ] is (23,23,27) and (36,39,36). Now the decision has to be made as the processor p 1 and p 3 have the same EFT which is 36.…”

“…Khiat et al 27 suggested a modular scheduling framework for heterogeneous systems (MFHS). The authors attempt to provide an efficient and reliable platform that includes a heuristic algorithm namely MMin .…”

HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms

“…Cloud computing is gaining popularity due to its ability for delivering cost‐effective cloud services which can bring a win‐win situation for the end users as well as for the service providers 1 …”