11,592 research outputs found
Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
Grid computing is a distributed system with heterogeneous infrastructures. Resource
management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration
mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms
will enhance ACS in terms of exploration mechanism and solution refinement by
implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing
environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment,
performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan
Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population
Independent Job Scheduling is one of the most useful versions of scheduling in grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the grid resources. Besides traditional restrictions, mapping of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of the system. Because of the complexity of the problem, the heuristic and meta-heuristic approaches are the most feasible methods of scheduling in grids due to their ability to deliver high quality solutions in reasonable computing time. One class of such meta-heuristics is Hierarchic Genetic Strategy (HGS). It is defined as a variant of Genetic Algorithms (GAs) which differs from the other genetic methods by its capability of concurrent search of the solution space.
In this work, we present an implementation of HGS for Independent Job Scheduling in dynamic grid environments. We consider the bi-objective version of the problem in which makespan and flowtime are simultaneously optimized. Based on our previous work, we improve the HGS scheduling strategy by enhancing its main branching operations. The resulting HGS-based scheduler is evaluated under the heterogeneity, the large scale and dynamics conditions using a grid simulator. The experimental study showed that the HGS implementation outperforms existing GA-based schedulers proposed in the literature.Peer ReviewedPostprint (author's final draft
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
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
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
A Simulated Annealing Method to Cover Dynamic Load Balancing in Grid Environment
High-performance scheduling is critical to the achievement of application performance on the computational grid. New scheduling algorithms are in demand for addressing new concerns arising in the grid environment. One of the main phases of scheduling on a grid is related to the load balancing problem therefore having a high-performance method to deal with the load balancing problem is essential to obtain a satisfactory high-performance scheduling. This paper presents SAGE, a new high-performance method to cover the dynamic load balancing problem by means of a simulated annealing algorithm. Even though this problem has been addressed with several different approaches only one of these methods is related with simulated annealing algorithm. Preliminary results show that SAGE not only makes it possible to find a good solution to the problem (effectiveness) but also in a reasonable amount of time (efficiency)
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