A bridging model for parallel computation
Communications of the ACM
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Coscheduling based on runtime identification of activity working sets
International Journal of Parallel Programming
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
LogP: a practical model of parallel computation
Communications of the ACM
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
The impact of I/O on program behavior and parallel scheduling
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Scheduling with implicit information in distributed systems
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Lessons from characterizating the input/output behavior of parallel scientific applications
Performance Evaluation - Special issue on tools for performance evaluation
Performance characteristics of gang scheduling in multiprogrammed environments
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Packing Schemes for Gang Scheduling
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Performance Evaluation of Gang Scheduling for Parallel and Distributed Multiprogramming
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Implications of I/O for Gang Scheduled Workloads
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Improved Utilization and Responsiveness with Gang Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Overhead Analysis of Preemptive Gang Scheduling
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Dynamic Coscheduling on Workstation Clusters
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A Lower Bound for Dynamic Scheduling of Data Parallel Programs
Euro-Par '98 Proceedings of the 4th International Euro-Par Conference on Parallel Processing
Implementation of Gang-Scheduling on Workstation Cluster
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
I/O Requirements of Scientific Applications: An Evolutionary View
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
Improving Throughput and Utilization in Parallel Machines through Concurrent Gang
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
| Hi-index | 0.00 |
We investigate the use of runtime measurements to improve job scheduling on a parallel machine. Emphasis is on gang scheduling based strategies. With the information gathered at runtime, we define a task classification scheme based on fuzzy logic and Bayesian estimators. The resulting local task classification is used to provide better service to I/O bound and interactive jobs under gang scheduling. This is achieved through the use of idle times and also by controlling the spinning time of a task in the spin block mechanism depending on the node's workload. Simulation results show considerable improvements, in particular for I/O bound workloads, in both throughput and machine utilization for a gang scheduler using runtime information compared with gang schedulers for which this type of information is not available.