Allocating Independent Subtasks on Parallel Processors
IEEE Transactions on Software Engineering
Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
Factoring: a method for scheduling parallel loops
Communications of the ACM
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
SETI@home: an experiment in public-resource computing
Communications of the ACM
Data parallel programming in an adaptive environment
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
A Master-Slave Approach to Parallel Term Rewriting on a Hierarchical Multiprocessor
DISCO '96 Proceedings of the International Symposium on Design and Implementation of Symbolic Computation Systems
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Adaptive Execution of Data Parallel Computations on Networks of Heterogeneous Workstations
Adaptive Execution of Data Parallel Computations on Networks of Heterogeneous Workstations
Adaptive scheduling of master/worker applications on distributed computational resources
Adaptive scheduling of master/worker applications on distributed computational resources
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Adaptive data parallel computing on workstation clusters
Journal of Parallel and Distributed Computing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
A Middleware Framework for Maximum Likelihood Evaluation over Dynamic Grids
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
The Internet Operating System: Middleware for Adaptive Distributed Computing
International Journal of High Performance Computing Applications
Distributed and Generic Maximum Likelihood Evaluation
E-SCIENCE '07 Proceedings of the Third IEEE International Conference on e-Science and Grid Computing
Bioinformatics
A taxonomy of grid monitoring systems
Future Generation Computer Systems
Automatic tuning of iterative computation on heterogeneous multiprocessors with ADITHE
The Journal of Supercomputing
A probabilistic approach to finding geometric objects in spatial datasets of the milky way
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Concurrency and Computation: Practice & Experience
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Many scientific disciplines use maximum likelihood evaluation (MLE) as an analytical tool. As the data to be analyzed grows increasingly, MLE demands more parallelism to improve analysis efficiency. Unfortunately, it is difficult for scientists and engineers to develop their own distributed/parallelized MLE applications. In addition, self-adaptability is an important characteristic for computing-intensive application for improving efficiency. This paper presents a self-adaptive and parallelized MLE framework that consists of a master process and a set of worker processes on a distributed environment. The workers are responsible to compute tasks, while the master needs to merge the computing results, to initiate or to terminate another computing iteration, and to decide how to re-distribute the computing tasks to workers. The proposed approach uses neither any monitoring mechanism to collect system state nor load-balancing-decision mechanism to balancing the workload. Instead, it measures the performance of each worker for computing an iteration, and uses the information to adjust the workload of workers accordingly. The experimental results show that not only the proposed framework can adapt to environmental changes, but also the proposed framework is effective; even in a stable environment that is dedicated for one application, the proposed framework still demonstrates its significant improvement in self-adaptability. The self-adaptability will be significantly improved while the workload of computing machines unbalanced.