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.

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms.

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In this paper, we propose a new cloud reactive fault management technique called Hybrid Redundant Array of Independent resources for cloud computing (H_RAIC). The latter uses a new concept called Redundant Array of Independent resources for cloud computing (CRAIR), which is inspired by a powerful conventional technique called Redundant Arrays of Inexpensive Disks (RAID). H_RAIC takes into consideration the cloud resources state and aims to satisfy both cloud users and cloud provider requirements. Our solution was compared with the replication technique which represents a specific case of CRAIR, and with other CRAIR levels defined in this paper. The results show that our technique is a promising solution, that can be used to meet both user and provider requirements.