Probabilistic load scheduling with priorities in distributed computing systems
Computers and Operations Research
Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Journal of Parallel and Distributed Computing
A taxonomy and survey of grid resource management systems for distributed computing
Software—Practice & Experience
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The Grid 2: Blueprint for a New Computing Infrastructure
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
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International Journal of High Performance Computing Applications
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MJSA: Markov job scheduler based on availability in desktop grid computing environment
Future Generation Computer Systems
Immediate Mode Scheduling of Independent Jobs in Computational Grids
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
Capacity planning and scheduling in Grid computing environments
Future Generation Computer Systems
Future Generation Computer Systems
The Journal of Supercomputing
Model-based simulation and performance evaluation of grid scheduling strategies
Future Generation Computer Systems
A data locality aware online scheduling approach for I/O-intensive jobs with file sharing
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Minimizing mean response time in batch processing system
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
Task scheduling modelling and reliability evaluation of grid services using coloured Petri nets
Future Generation Computer Systems
Evaluation of hierarchical desktop grid scheduling algorithms
Future Generation Computer Systems
A multi-class probabilistic priority scheduling discipline for differentiated services networks
Computer Communications
A probabilistic priority scheduling discipline for multi-service networks
Computer Communications
A queuing network model for minimizing the total makespan of computational grids
Computers and Electrical Engineering
Performance evaluation of sensor networks by statistical modeling and euclidean model checking
ACM Transactions on Sensor Networks (TOSN)
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This paper presents a probabilistic task scheduling method to minimize the overall mean response time of the tasks submitted to the grid computing environments. Minimum mean response time of a given task can be obtained by finding a subset of appropriate computational resources to service the task. To achieve this, a discrete time Markov chain (DTMC) representing the task scheduling process within the grid environment is constructed. The connection probabilities between the nodes representing the grid managers and resources can be considered as transition probabilities of the obtained DTMC. Knowing the mean response times of the managers and resources, and finding fundamental matrix of the DTMC, the mean response time related to each of the absorbing DTMCs existing inside the overall DTMC can be computed. Minimizing the obtained mean response times and taking into account the probability constraints in each of the absorbing DTMCs, a nonlinear programming (NLP) problem is defined. Solving the NLP problem, the connection probabilities between the managers and resources are obtained. Finally, using the connection probabilities, the best scheduling path within the environment and the minimum mean response time of a particular task can be achieved. In a case in which there is only one optimal scheduling choice within the environment, the proposed method can deterministically find such scheduling by assigning zero or one to the connection probabilities. Results obtained from evaluating the proposed method on the hypothesis and real grid environments show the preference of the proposed method compared to the other methods in minimizing both the overall mean response time of the tasks and total makespan of the environment.