J. E. Muñoz Expósito

A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing

Grid computing is increasingly emerging as a promising platform for large-scale problems solving in science, engineering and technology. Nevertheless, a major effort is still required to harness the high potential performance of such computational framework and in this sense, an important challenge is to develop new strategies that efficiently address scheduling on the distributed, heterogeneous and shared environment of grids. Fuzzy rule-based systems (FRBSs) models are dynamic and are currently attracting the interest of scheduling research community to obtain near-optimal solutions on grids. However, FRBSs performance is strongly related to the quality of their knowledge bases and thus, with the knowledge acquisition process. Due to the inherent dynamic nature and the typical complex search spaces of grids, automatically finding a high-quality knowledge base that accurately describes the fuzzy system is extremely relevant. In this work, we propose a scheduling system for grids considering a novel learning strategy inspired by Michigan and Pittsburgh approaches that applies genetic algorithms (GAs) to evolve the fuzzy rule bases and improves the classical learning strategies in terms of computational effort and convergence behaviour. In addition, experimental results show that the proposed schema significantly outperforms other extensively used scheduling strategies.

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Proposal to improve scheduling in grid computing based on soft computing.Fuzzy Classifier Systems are used as grid schedulers.Critical aspect in these grid schedulers: knowledge acquisition.New rules discovery strategy based on PSO is proposed, KARP.Higher quality of knowledge allows a more efficient scheduling in the grid. Graphical abstractDisplay Omitted Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.

Fuzzy scheduling with swarm intelligence-based knowledge acquisition for grid computing

In spite of the existence of a large diversity in literature related to scheduling algorithms in computational grids, there are only a few efficiently dealing with the inherent uncertainty and dynamism of resources and applications of these systems. Further, the need to meet both users and providers QoS requirements, such as tardiness or resource utilization, calls for new adaptive scheduling strategies that consider current and future status of the grid. Fuzzy Rule-Based Systems (FRBSs) are knowledge based systems that are recently emerging as an alternative for the development of grid scheduling middleware. Their main strength resides in their adaptability to changes in environment and their ability to model vagueness. However, since their performance strongly depends on the quality of their acquired knowledge, new automatic learning strategies are pursued. In this work, a FRBS meta-scheduler for scheduling jobs in computational grids is suggested which incorporates a novel knowledge acquisition method based on Swarm Intelligence. Simulations results show that the fuzzy meta-scheduler improves six classical queued-based and scheduled-based approaches present in todays production systems and it is able to easily adapt to changes in the grid conditions.

Genetic fuzzy rule-based scheduling system for grid computing in virtual organizations

One of the most challenging problems when facing the implementation of computational grids is the system resources effective management commonly referred as to grid scheduling. A rule-based scheduling system is presented here to schedule computationally intensive Bag-of-Tasks applications on grids for virtual organizations. There exist diverse techniques to develop rule-base scheduling systems. In this work, we suggest the joining of a gathering and sorting criteria for tasks and a fuzzy scheduling strategy. Moreover, in order to allow the system to learn and thus to improve its performance, two different off-line optimization procedures based on Michigan and Pittsburgh approaches are incorporated to apply Genetic Algorithms to the fuzzy scheduler rules. A complex objective function considering users differentiation is followed as a performance metric. It not only provides the conducted system evaluation process a comparison with other classical approaches in terms of accuracy and convergence behaviour characterization, but it also analyzes the variation of a wide set of evolution parameters in the learning process to achieve the best performance.

On Providing Quality of Service in Grid Computing through Multi-objective Swarm-Based Knowledge Acquisition in Fuzzy Schedulers

Nowadays, Grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in Grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in Grid computing.

Genetic Fuzzy Rule-Based meta-scheduler for Grid computing

The growing interest in grids technologies for the solving of large-scale computational problems leads related framework improvement. One of the challenging problems in Grid computing is the efficient resources utilization and allocation of tasks, i.e. scheduling problem. Fuzzy Rule-Based Systems (FRBSs) have recently proved to be a competitive alternative for the development of scheduling systems, outperforming extensively used scheduling strategies such as EASY Backfilling or Greedy. However, FRBSs-based schedulers performance strongly depends on their data bases quality and a major effort is still required for the knowledge acquisition process improvement. This paper presents a fuzzy rule-based meta-scheduler incorporating a new genetic approach for the learning process. Concretely, the suggested learning strategy is inspired by classical rule evolution strategies, Pittsburgh and Michigan approaches. Experimental results show that further accuracy in the learning process of fuzzy meta-schedulers can be achieved without significantly increasing the associated computational effort.

Learning of Fuzzy Rule-Based Meta-schedulers for Grid Computing with Differential Evolution

Grid computing has arisen as the next-generation infrastructure for high demand computational applications founded on the collaboration and coordination of a large set of distributed resources. The need to satisfy both users and network administrators QoS demands in such highly changing environments requires the consideration of adaptive scheduling strategies dealing with inherent dynamism and uncertainty. In this paper, a meta-scheduler based on Fuzzy Rule-Based Systems is proposed for scheduling in grid computing. Moreover, a new learning strategy inspired by stochastic optimization algorithm Differential Evolution (DE), is incorporated for the evolution of expert system knowledge or rules bases. Simulation results show that knowledge acquisition process is improved in terms of convergence behaviour and final result in comparison to other evolutionary strategy, genetic Pittsburgh approach. Also, the fuzzy meta-scheduler performance is compared to other extended scheduling strategy, EASY-Backfilling in diverse criteria such as flowtime, tardiness and machine usage.

KASIA approach vs. Differential Evolution in Fuzzy Rule-Based meta-schedulers for Grid computing

Many efforts have been made in the last few years to solve the high-level scheduling problem in Grid computing, i.e., the efficient resources utilization and allocation of workload within resources domains. Nowadays, some trends are based on the consideration of Fuzzy Rule-Based Systems, whose performance is critically conditioned to theirs knowledge bases quality. In this sense, Genetic Algorithms have been extensively used to obtain such knowledge bases, mainly founded on Pittsburgh approach. However, new strategies are recently emerging showing improvement over genetic-based learning methods. In this work, comparative results of two non-genetic learning strategies derived from bio-inspired algorithms, Differential Evolution and Particle Swarm Optimization, are presented for the evolution of fuzzy rule-based meta-schedulers in Grid computing.

Gaussian Mixture Models vs. Fuzzy Rule-Based Systems for Adaptive Meta-scheduling in Grid/Cloud Computing

Adaptive scheduling strategies are about considering the state of computational grids to obtain efficient and reliable schedules and to prevent the system performance deterioration. In this work, emerging adaptive strategies in grid computing, namely Fuzzy Rule-Based Systems (FRBS) -based strategies and a new adaptive scheduling approach, gaussian scheduling founded on Gaussian Mixture Models (GMMs) are compared. Both types of strategies focus on modeling the state of resources and select the most convenient site of the grid at every scheduling step given the current conditions. FRBSs provide a fuzzy characterization of the grid state and the inference of a suitability index based on their own knowledge given in the form of fuzzy IF-THEN rules. Besides, a GMM can be trained to model a complex probability density distribution indicating the suitability of every site in the grid to be the target of the schedule with the current conditions of its resources. This way the GMM scheduler assigns a probability to every state of the site where a higher probability is associated to a higher suitability of selection. Simulations based on real grid facilities are conducted to test the FRBS and GMM-based models and results are analyzed in terms of accuracy and convergence behaviour of their associated learning processes.

Improving expert meta-schedulers for grid computing through weighted rules evolution

Grid computing is an emerging framework which has proved its effectiveness to solve large-scale computational problems in science, engineering and technology. It is founded on the sharing of distributed and heterogeneous resources capabilities of diverse domains to achieve a common goal. Given the high dynamism and uncertainty of these systems, a major issue is the workload allocation or scheduling problem which is known to be NP-hard. In this sense, recent works suggest the consideration of expert schedulers based on Fuzzy Rule-Based Systems (FRBSs) able to cope with the imprecise and changing nature of the grid system. However, the dependence of these systems with the quality of their expert knowledge makes it relevant to incorporate efficient learning strategies offering the highest accuracy. In this work, fuzzy rulebased schedulers are proposed to consider two learning stages where good quality IF-THEN rule bases acquired with a successful and well-known strategy rule learning approach, i.e., Pittsburgh, are subject to a second learning stage where the evolution of rule weights is entailed through Particle Swarm Optimization. Simulations results show that evolution of rule weights through this swarm intelligence -based strategy allows the improvement of the expert system schedules in terms of workload completion and increase the accuracy of the classical genetic learning strategy in FRBSs.