R. P. Prado

Knowledge Acquisition in Fuzzy-Rule-Based Systems With Particle-Swarm Optimization

Knowledge acquisition is a long-standing problem in fuzzy-rule-based systems. In spite of the existence of several approaches, much effort is still required to increase the efficiency of the learning process. This study introduces a new method for the fuzzy-rule evolution that forms an expert system knowledge: the knowledge acquisition with a swarm-intelligence approach (KASIA). Specifically, this strategy is based on the use of particle-swarm optimization (PSO) to obtain the antecedents, consequences, and connectives of the rules. To test the feasibility of the suggested method, the inverted-pendulum problem is studied, and results are compared for two of the most extensively used methodologies in machine learning: the genetic-based Pittsburgh approach and the Q-learning-based strategy, i.e., state-action-reward-state-action (SARSA). Moreover, KASIA is analyzed as a learning strategy in fuzzy-rule-based metascheduler design for grid computing, and performance is compared with other scheduling strategies based on genetic learning and existing scheduling approaches, i.e., EASY-backfilling and ESG+local periodical search. To be more precise, simulation results prove the fact that the proposed strategy outperforms classical learning approaches in terms of final results and computational effort. Furthermore, the main advantage is the capability to control convergence and its simplicity.

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.

Dynamic Voltage Frequency Scaling Simulator for Real Workflows Energy-Aware Management in Green Cloud Computing

Nowadays, the growing computational capabilities of Cloud systems rely on the reduction of the consumed power of their data centers to make them sustainable and economically profitable. The efficient management of computing resources is at the heart of any energy-aware data center and of special relevance is the adaptation of its performance to workload. Intensive computing applications in diverse areas of science generate complex workload called workflows, whose successful management in terms of energy saving is still at its beginning. WorkflowSim is currently one of the most advanced simulators for research on workflows processing, offering advanced features such as task clustering and failure policies. In this work, an expected power-aware extension of WorkflowSim is presented. This new tool integrates a power model based on a computing-plus-communication design to allow the optimization of new management strategies in energy saving considering computing, reconfiguration and networks costs as well as quality of service, and it incorporates the preeminent strategy for on host energy saving: Dynamic Voltage Frequency Scaling (DVFS). The simulator is designed to be consistent in different real scenarios and to include a wide repertory of DVFS governors. Results showing the validity of the simulator in terms of resources utilization, frequency and voltage scaling, power, energy and time saving are presented. Also, results achieved by the intra-host DVFS strategy with different governors are compared to those of the data center using a recent and successful DVFS-based inter-host scheduling strategy as overlapped mechanism to the DVFS intra-host technique.

Swarm Fuzzy Systems: Knowledge Acquisition in Fuzzy Systems and Its Applications in Grid Computing

This work proposes the use of bio-inspired knowledge acquisition for Fuzzy Systems founded on Swarm Intelligence-Particle Swarm Optimization (SI-PSO). Swarm-based models consider knowledge entities as particles that move in the space to reach the higher quality. Fuzzy Systems following SI-PSO for knowledge acquisition are categorized in this work as Swarm Fuzzy Systems (SFSs). Specifically, two learning methodologies, KASIA (using rule bases as particles in PSO) and KARP (using rules as particles in PSO) are introduced. SFSs performance is studied in a problem of practical importance nowadays with data sets, the learning of fuzzy meta-schedulers in computational grids. Fuzzy meta-schedulers are Fuzzy Systems doing intelligent allocation of jobs to improve the performance of the grid, such as the reduction of the execution time of workload. The scheduling decisions are taken based on the knowledge of the Fuzzy System and in this way, the relevance of their learning process are critical. In this work, compared results of the performance of the different SFSs and a comparison between SFSs and Genetic Fuzzy Systems are presented. Simulations results show that SFSs can achieve a faster convergence and higher quality with a reduced number of control parameters what makes them a good alternative to Genetic Fuzzy Systems.

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.