Hybrid Meta-heuristic Algorithms for Static and Dynamic Job Scheduling in Grid Computing

The term ’grid computing’ is used to describe an infrastructure that connects geographically distributed computers and heterogeneous platforms owned by multiple organizations allowing their computational power, storage capabilities and other resources to be selected and shared. Allocating jobs to computational grid resources in an efficient manner is one of the main challenges facing any grid computing system; this allocation is called job scheduling in grid computing. This thesis studies the application of hybrid meta-heuristics to the job scheduling problem in grid computing, which is recognized as being one of the most important and challenging issues in grid computing environments. Similar to job scheduling in traditional computing systems, this allocation is known to be an NPhard problem. Meta-heuristic approaches such as the Genetic Algorithm (GA), Variable Neighbourhood Search (VNS) and Ant Colony Optimisation (ACO) have all proven their effectiveness in solving different scheduling problems. However, hybridising two or more meta-heuristics shows better performance than applying a stand-alone approach. The new high level meta-heuristic will inherit the best features of the hybridised algorithms, increasing the chances of skipping away from local minima, and hence enhancing the overall performance. In this thesis, the application of VNS for the job scheduling problem in grid computing is introduced. Four new neighbourhood structures, together with a modified local search, are proposed. The proposed VNS is hybridised using two meta-heuristic methods, namely GA and ACO, in loosely and strongly coupled fashions, yielding four new sequential hybrid meta-heuristic algorithms for the problem of static and dynamic single-objective independent batch job scheduling in grid computing. For the static version of the problem, several experiments were carried out to analyse the performance of the proposed schedulers in terms of minimising the makespan using well known benchmarks. The experiments show that the proposed schedulers achieved impressive results compared to other traditional, heuristic and meta-heuristic approaches selected from the bibliography. To model the dynamic version of the problem, a simple simulator, which uses the rescheduling technique, is designed and new problem instances are generated, by using a well-known methodology, to evaluate the performance of the proposed hybrid schedulers. The experimental results show that the use of rescheduling provides significant improvements in terms of the makespan compared to other non-rescheduling approaches

Hybrid meta-heuristic algorithms for independent job scheduling in grid computing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The term ’grid computing’ is used to describe an infrastructure that connects geographically distributed computers and heterogeneous platforms owned by multiple organizations allowing their computational power, storage capabilities and other resources to be selected and shared. The job scheduling problem is recognized as being one of the most important and challenging issues in grid computing environments. This paper proposes two strongly coupled hybrid meta-heuristic schedulers. The first scheduler combines Ant Colony Optimisation and Variable Neighbourhood Search in which the former acts as the primary algorithm which, during its execution, calls the latter as a supporting algorithm, while the second merges the Genetic Algorithm with Variable Neighbourhood Search in the same fashion. Several experiments were carried out to analyse the performance of the proposed schedulers in terms of minimizing the makespan using well known benchmarks. The experiments show that the proposed schedulers achieved impressive results compared to other selected approaches from the bibliography

Genetic Algorithm for Independent Job Scheduling in Grid Computing

Grid computing refers to the infrastructure which connects geographically distributed computers ownedby various organizations allowing their resources, such as computational power and storage capabilities, to beshared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources.It is considered one of the main steps to e ciently utilise the maximum capabilities of grid computing systems.The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methodsrepresent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure tosolve the problem of independent job scheduling in grid computing is presented. A known static benchmark forthe problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out anumber of experiments. The obtained results show that the proposed algorithm performs better than some knownalgorithms taken from the literature

A loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computing

Task scheduling is one of the most difficult problems in grid computing systems. Therefore, various studies have been proposed to present methods which provide efficient schedules. Meta-heuristic approaches are among the methods which have proven their efficiency in this domain. However, the literature shows that hybridizing two or more meta-heuristics can improve performance to a greater extent than stand-alone algorithms as the new high-level algorithm will inherit the best features of the hybridized algorithms. In this paper, a loosely coupled hybrid meta-heuristic algorithm is proposed for solving the static independent task scheduling problem in grid computing. It combines ant colony optimization and variable neighborhood search, where the former operates first and whose output is subsequently improved by the latter. The experimental results show that the proposed algorithm achieves better task-machine mapping in terms of minimizing makespan than other selected approaches from the literature