Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
An analytical approach to performance/cost modeling of parallel computers
Journal of Parallel and Distributed Computing
IEEE Transactions on Computers
IEEE Transactions on Parallel and Distributed Systems
"Agency Scheduling" A Model for Dynamic Task Scheduling
Euro-Par '95 Proceedings of the First International Euro-Par Conference on Parallel Processing
A Proposal for a Heterogeneous Cluster ScaLAPACK (Dense Linear Solvers)
IEEE Transactions on Computers
IEEE Transactions on Parallel and Distributed Systems
Adaptive parallel computing on heterogeneous networks with mpC
Parallel Computing
Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Systems
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Run-Time Adaptation with Resource Co-Allocation for Grid Environments
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Executing multiple pipelined data analysis operations in the grid
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
A Unified Resource Scheduling Framework for Heterogeneous Computing Environments
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Match Virtual Machine: An Adaptive Runtime System to Execute MATLAB in Parallel
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Journal of Parallel and Distributed Computing
Data Partitioning with a Functional Performance Model of Heterogeneous Processors
International Journal of High Performance Computing Applications
Journal of Parallel and Distributed Computing
Distributed data mining in grid computing environments
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Parallel exact inference on the cell broadband engine processor
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Parallel exact inference on the Cell Broadband Engine processor
Journal of Parallel and Distributed Computing
Anahy: a programming environment for cluster computing
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
Federate job mapping strategy in grid-based virtual wargame collaborative environment
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
Energy aware DAG scheduling on heterogeneous systems
Cluster Computing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Scheduling of tasks with batch-shared I/O on heterogeneous systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Scheduling multiple DAGs onto heterogeneous systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Cloud-DLS: Dynamic trusted scheduling for Cloud computing
Expert Systems with Applications: An International Journal
Bayesian Cognitive Model in Scheduling Algorithm for Data Intensive Computing
Journal of Grid Computing
Parallel resource co-allocation for the computational grid
Computer Languages, Systems and Structures
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With the advent of large scale heterogeneous environments, there is a need for matching and scheduling algorithms which can allow multiple DAG-structured applications to share the computational resources of the network. This paper presents a matching and scheduling framework where multiple applications compete for the computational resources on the network. In this environment, each application makes its own scheduling decisions. Thus, no centralized scheduling resource is required. Applications do not need direct knowledge of the other applications. The only knowledge of other applications arrives indirectly through load estimates (like queue lengths). This paper also presents algorithms for each portion of this scheduling framework. One of these algorithms is modification of a static scheduling algorithm, the DLS algorithm, first presented by Sih and Lee. Other algorithms attempt to predict the future task arrivals by modeling the task arrivals as Poisson random processes. A series of simulations are presented to examine the performance of these algorithms in this environment. These simulations also compare the performance of this environment to a more conventional, single user environment.