8,018 research outputs found
Exploration based Genetic Algorithm for Job Scheduling on Grid Computing
Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects
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
Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid
The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling
Parallel memetic algorithms for independent job scheduling in computational grids
In this chapter we present parallel implementations of Memetic Algorithms (MAs) for the problem of scheduling independent jobs in computational grids. The problem of scheduling in computational grids is known for its high demanding computational time. In this work we exploit the intrinsic parallel nature of MAs as well as the fact that computational grids offer large amount of resources, a part of which could be used to compute the efficient allocation of jobs to grid resources.
The parallel models exploited in this work for MAs include both fine-grained and coarse-grained parallelization and their hybridization. The resulting schedulers have been tested through different grid scenarios generated by a grid simulator to match different possible configurations of computational grids in terms of size (number of jobs and resources) and computational characteristics of resources. All in all, the result of this work showed that Parallel MAs are very good alternatives in order to match different performance requirement on fast scheduling of jobs to grid resources.Peer ReviewedPostprint (author's final draft
The Impact of Data Replicatino on Job Scheduling Performance in Hierarchical data Grid
In data-intensive applications data transfer is a primary cause of job
execution delay. Data access time depends on bandwidth. The major bottleneck to
supporting fast data access in Grids is the high latencies of Wide Area
Networks and Internet. Effective scheduling can reduce the amount of data
transferred across the internet by dispatching a job to where the needed data
are present. Another solution is to use a data replication mechanism. Objective
of dynamic replica strategies is reducing file access time which leads to
reducing job runtime. In this paper we develop a job scheduling policy and a
dynamic data replication strategy, called HRS (Hierarchical Replication
Strategy), to improve the data access efficiencies. We study our approach and
evaluate it through simulation. The results show that our algorithm has
improved 12% over the current strategies.Comment: 11 pages, 7 figure
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