A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.

Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Meta task-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category

A particle swarm optimization and min-max­-based workflow scheduling algorithm with QoS satisfaction for service-­oriented grids

In service-orientated grids (SOG) environments, grid workflow schedulers play a critical role in providing quality-of-service (QoS) satisfaction for various end users (EUs) with diverse QoS objectives and optimization requirements. The EU requirements are not only many and conflicting, but also involve constraints of various degrees—loose, moderate or tight. However, most of the existing scheduling approaches violate EU constraints in tight situations and suffer inferior QoS optimization results. In this paper, a constraints-aware multi-QoS workflow scheduling strategy is proposed based on particle swarm optimization (PSO) and a proposed look-ahead heuristic (LAPSO) to improve performance in such situations. The algorithm selects the best scheduling solutions based on the proposed constraint-handling strategy. It hybridises PSO with a novel look-ahead mechanism based on a min–max heuristic, which deterministically improves the quality of the best solutions. Extensive simulation experiments have been carried out to evaluate the performance of the proposed approach. The simulation results show that the LAPSO algorithm guarantees satisfaction (0% violation) of the EU constraints even in tight situations. It also outperforms the comparison algorithm, with about 30% increase, in terms of cumulative QoS satisfaction of optimization requirements. In addition, the new scheme significantly reduces the CPU time by about 75% compared to the benchmark algorithm

Probabilistic reliability prediction models for task scheduling in distributed systems: A review

In service-oriented distributed systems, beside time and cost, reliability is the most important concern to both service users and the service providers. Although, this has been many decades problem, the existence of large number of service systems on the internet today has rendered the problem more difficult. This is because the distributed environment of today is more complex with numerous uncertainties and chances of failure at all levels. Therefore, selection of reliable service poses a serious challenge. To combat this problem, over the years, huge number of reliability researches has been reported in literature. These researches have been categorized and analysed in many survey and review studies. However, most of these studies focus on the architecture-based reliability mechanisms and pay little attention to the advances in the popular probabilistic reliability prediction methods which are based on quantitative reliability measurements. These methods which are sometimes called ‘black box’ techniques are of great importance to both service designers and service clients such as brokers and other proprietary schedulers, for evaluating reliability of services or service components. Therefore, in this study the previous survey and review studies are extended by analyzing these methods and their recently proposed variants. In the end the study reveal some of the current issues that need further research