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Computational models and heuristic methods for Grid scheduling problems
 FUTURE GENERATION COMPUTER SYSTEMS
, 2010
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GENETIC ALGORITHM BASED SCHEDULERS FOR GRID COMPUTING SYSTEMS
, 2006
"... Abstract. In this paper we present Genetic Algorithms (GAs) based schedulers for efficiently allocating jobs to resources in a Grid system. Scheduling is a key problem in emergent computational systems, such as Grid and P2P, in order to benefit from the large computing capacity of such systems. We p ..."
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Cited by 37 (9 self)
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Abstract. In this paper we present Genetic Algorithms (GAs) based schedulers for efficiently allocating jobs to resources in a Grid system. Scheduling is a key problem in emergent computational systems, such as Grid and P2P, in order to benefit from the large computing capacity of such systems. We present an extensive study on the usefulness of GAs for designing efficient Grid schedulers when makespan and flowtime are minimized. Two encoding schemes have been considered and most of GA operators for each of them are implemented and empirically studied. The extensive experimental study showed that our GAbased schedulers outperform existing GA implementations in the literature for the problem and also revealed their efficiency when makespan and flowtime are minimized either in a hierarchical or a simultaneous optimization mode; previous approaches considered only the minimization of the makespan. Moreover, we were able to identify which GAs versions work best under certain Grid characteristics, which is very useful for real Grids. Our GAbased schedulers are very fast and hence they can be used to dynamically schedule jobs arriving in the Grid system by running in batch mode for a short time.
Dynamic Task Scheduling Using Genetic Algorithms for Heterogeneous Distributed Computing
, 2005
"... An algorithm has been developed to dynamically schedule heterogeneous tasks on heterogeneous processors in a distributed system. The scheduler operates in an environment with dynamically changing resources and adapts to variable system resources. It operates in a batch fashion and utilises a genetic ..."
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Cited by 20 (1 self)
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An algorithm has been developed to dynamically schedule heterogeneous tasks on heterogeneous processors in a distributed system. The scheduler operates in an environment with dynamically changing resources and adapts to variable system resources. It operates in a batch fashion and utilises a genetic algorithm to minimise the total execution time. We have compared our scheduler to six other schedulers, three batchmode and three immediatemode schedulers. We have performed simulations with randomly generated task sets, using uniform, normal, and Poisson distributions, whilst varying the communication overheads between the clients and scheduler. We have achieved more efficient results than all other schedulers across a range of different scenarios while scheduling 10,000 tasks on up to 50 heterogeneous processors.
Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimization
 Int. J. BioInspired Comput
, 2009
"... This paper presents a Hybrid Particle Swarm Optimization (HPSO) method for solving the Task Assignment Problem (TAP) which is an nphard problem. Particle Swarm Optimization (PSO) is a recently developed population based heuristic optimization technique. The algorithm has been developed to dynamical ..."
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Cited by 11 (0 self)
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This paper presents a Hybrid Particle Swarm Optimization (HPSO) method for solving the Task Assignment Problem (TAP) which is an nphard problem. Particle Swarm Optimization (PSO) is a recently developed population based heuristic optimization technique. The algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed setup. Load balancing which is a major issue in task scheduling is also considered. The nature of the tasks are independent and non preemptive. The HPSO yields a better result than the Normal PSO when applied to the task assignment problem. The results Of PSO and HPSO is also compared with another popular heuristic optimization technique namely Genetic Algorithm ( GA). The results infer that the PSO performs better than the GA.
Heuristics Based Genetic Algorithm for Scheduling Static Tasks in Homogeneous Parallel System
"... Multiprocessor task scheduling is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running realtime applications, especially that a uniprocessor system would not be sufficient enough to execute all the tasks. That computing environm ..."
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Cited by 9 (0 self)
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Multiprocessor task scheduling is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running realtime applications, especially that a uniprocessor system would not be sufficient enough to execute all the tasks. That computing environment requires an efficient algorithm to determine when and on which processor a given task should execute. A task can be partitioned into a group of subtasks and represented as a DAG (Directed Acyclic Graph), that problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system. The problem of mapping metatasks to a machine is shown to be NPcomplete. The NPcomplete problem can be solved only using heuristic approach. The execution time requirements of the applications ’ tasks are assumed to be stochastic. In multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program’s execution time should be minimized. The last job must be completed as early as possible.
A DISCRETE PARTICLE SWARM OPTIMIZATION APPROACH FOR GRID JOB SCHEDULING
, 2009
"... Abstract. Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, heterogeneous and unpredictable one sharing different services among many different users. Because of heterogeneous and dynamic nature ..."
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Cited by 8 (1 self)
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Abstract. Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, heterogeneous and unpredictable one sharing different services among many different users. Because of heterogeneous and dynamic nature of grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be looked for. This paper represents a discrete Particle Swarm Optimization (DPSO) approach for grid job scheduling. PSO is a populationbased search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or nearoptimal solutions. In this paper, the scheduler aims at minimizing makespan and flowtime simultaneously in grid environment. Experimental studies illustrate that the proposed method is more efficient and surpasses those of reported metaheuristic algorithms for this problem.
Optimizing ondemand data broadcast scheduling in pervasive environments
 In EDBT
, 2008
"... Data dissemination in pervasive environments is often accomplished by ondemand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in ondemand broadcast scheduling has focused on the timely servicing of requests so as to ..."
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Cited by 7 (0 self)
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Data dissemination in pervasive environments is often accomplished by ondemand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in ondemand broadcast scheduling has focused on the timely servicing of requests so as to minimize the number of missed deadlines. However, there exists many pervasive environments where the utility of the data is an equally important criterion as its timeliness. Missing the deadline reduces the utility of the data but does not make it zero. In this work, we address the problem of scheduling ondemand data broadcasts with soft deadlines. We investigate search based optimization techniques to develop broadcast schedulers that make explicit attempts to maximize the utility of data requests as well as service as many requests as possible within the acceptable time limit. Our analysis shows that heuristic driven methods for such problems can be improved by hybridizing them with local search algorithms. We further investigate the option of employing a dynamic optimization technique to facilitate utility gain, thereby surpassing the requirement of a heuristic in the process. An evolution strategy based stochastic hill climber is investigated in this context. 1.
Metaheuristics for Grid Scheduling Problems
, 2008
"... In this chapter, we review a few important concepts from Grid computing related to scheduling problems and their resolution using heuristic and metaheuristic approaches. Scheduling problems are at the heart of any Gridlike computational system. Different types of scheduling based on different cri ..."
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Cited by 7 (1 self)
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In this chapter, we review a few important concepts from Grid computing related to scheduling problems and their resolution using heuristic and metaheuristic approaches. Scheduling problems are at the heart of any Gridlike computational system. Different types of scheduling based on different criteria, such as static vs. dynamic environment, multiobjectivity, adaptivity, etc., are identified. Then, heuristics and metaheuristics methods for scheduling in Grids are presented. The chapter reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristics and metaheuristics approaches for the design of efficient Grid schedulers.
Metaheuristic Based Scheduling MetaTasks in Distributed Heterogeneous Computing Systems
 SENSORS
, 2009
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PERFORMANCE COMPARISON OF SIX EFFICIENT PURE HEURISTICS FOR SCHEDULING METATASKS ON HETEROGENEOUS DISTRIBUTED ENVIRONMENTS
"... Abstract: Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and represents an NPcomplete problem. Therefore, using metaheuristic algorithms is a suitable approach in order to cope with its difficulty. In many metaheuristic a ..."
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Cited by 4 (2 self)
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Abstract: Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and represents an NPcomplete problem. Therefore, using metaheuristic algorithms is a suitable approach in order to cope with its difficulty. In many metaheuristic algorithms, generating individuals in the initial step has an important effect on the convergence behavior of the algorithm and final solutions. Using some pure heuristics for generating one or more nearoptimal individuals in the initial step can improve the final solutions obtained by metaheuristic algorithms. Pure heuristics may be used solitary for generating schedules in many realworld situations in which using the metaheuristic methods are too difficult or inappropriate. Different criteria can be used for evaluating the efficiency of scheduling algorithms, the most important of which are makespan and flowtime. In this paper, we propose an efficient pure heuristic method and then we compare the performance with five popular heuristics for minimizing makespan and flowtime in heterogeneous distributed computing systems. We investigate the effect of these pure heuristics for initializing simulated annealing metaheuristic approach for scheduling tasks on heterogeneous environments.