The available capacity of a privately owned workstation environment
Performance Evaluation
The utility of exploiting idle workstations for parallel computation
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Mechanisms and policies for supporting fine-grained cycle stealing
ICS '99 Proceedings of the 13th international conference on Supercomputing
Future Generation Computer Systems - Special issue on metacomputing
An Opportunity Cost Approach for Job Assignment in a Scalable Computing Cluster
IEEE Transactions on Parallel and Distributed Systems
Dynamic Cluster Resource Allocations for Jobs with Known and Unknown Memory Demands
IEEE Transactions on Parallel and Distributed Systems
Dynamic load balancing of SAMR applications on distributed systems
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Performance Modeling and Prediction of Nondedicated Network Computing
IEEE Transactions on Computers
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
Nimrod: a tool for performing parametrised simulations using distributed workstations
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
Grid Harvest Service: A System for Long-Term, Application-Level Task Scheduling
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Adaptive Scheduling for Task Farming with Grid Middleware
International Journal of High Performance Computing Applications
GHS: A Performance System of Grid Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 10 - Volume 11
Data partitioning for multiprocessors with memory heterogeneity and memory constraints
Scientific Programming - International Symposium of Parallel and Distributed Computing & International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogenous Networks
Resource-Aware Distributed Scheduling Strategies for Large-Scale Computational Cluster/Grid Systems
IEEE Transactions on Parallel and Distributed Systems
Grid scheduling divisible loads from two sources
Computers & Mathematics with Applications
The GHS grid scheduling system: implementation and performance comparison
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A distributed paging RAM grid system for wide-area memory sharing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Enhancing security of real-time applications on grids through dynamic scheduling
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
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While resource management and task scheduling are identified challenges of Grid computing, current Grid scheduling systems mainly focus on CPU and network availability. Recent performance improvement of CPU and computer network has made memory usage a significant factor of overall performance. In this study, we consider memory availability as a performance factor and introduce memory conscious task partition and scheduling. Three task partition policies are discussed. They are CPU-based, memory-based, and CPU-memory combined partition. We first investigate the three task partition policies on dedicated resources and verify the effectiveness of the CPU-memory combined partition algorithm in finding an optimal solution. We then extend the task partition policies in non-dedicated environments with the consideration of resource sharing. Analytical and experimental results show that the CPU-memory combined scheduling approach outperforms either the CPU-based or memory-based scheduling approach considerably for memory-intensive applications in Grid environments.