Probabilistic bounds on the performance of list scheduling
SIAM Journal on Computing
Nonpreemptive run-time scheduling issues on a multitasked, multiprogrammed multiprocessor with dependencies, bidimensional tasks, folding and dynamic graphs
Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
Scheduling in multiprogrammed parallel systems
SIGMETRICS '88 Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems
The performance of multiprogrammed multiprocessor scheduling algorithms
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Factoring: a method for scheduling parallel loops
Communications of the ACM
A dynamic processor allocation policy for multiprogrammed shared-memory multiprocessors
ACM Transactions on Computer Systems (TOCS)
Processor scheduling on multiprogrammed, distributed memory parallel computers
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Application scheduling and processor allocation in multiprogrammed parallel processing systems
Performance Evaluation - Special issue: performance modeling of parallel processing systems
A probabilistic event scheduling policy for optimistic parallel discrete event simulation
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
The elusive goal of workload characterization
ACM SIGMETRICS Performance Evaluation Review
The impact of distributions and disciplines on multiple processor systems
Communications of the ACM
Probabilistic Scheduling Guarantees for Fault-Tolerant Real-Time Systems
DCCA '99 Proceedings of the conference on Dependable Computing for Critical Applications
Scheduling with Confidence for Probabilistic Data-flow Graphs
GLS '97 Proceedings of the 7th Great Lakes Symposium on VLSI
Scheduling partially ordered tasks with probabilistic execution times
SOSP '75 Proceedings of the fifth ACM symposium on Operating systems principles
Multiprocessor Scheduling Problem with Probabilistic Execution Costs
ISPAN '00 Proceedings of the 2000 International Symposium on Parallel Architectures, Algorithms and Networks
Analysis of Fork-Join Jobs Using Processor-Sharing
Analysis of Fork-Join Jobs Using Processor-Sharing
Probabilistic and Dynamic Optimization of Job Partitioning on a Grid Infrastructure
PDP '06 Proceedings of the 14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
The Effect on Throughput of Multiprocessing in a Multiprogramming Environment
IEEE Transactions on Computers
An efficient approach for self-scheduling parallel loops on multiprogrammed parallel computers
LCPC'05 Proceedings of the 18th international conference on Languages and Compilers for Parallel Computing
| Hi-index | 0.00 |
Scheduling for large parallel systems such as clusters and grids presents new challenges due to multiprogramming/polyprocessing [1]. In such systems, several jobs (each consisting of a number of parallel tasks) of multiple users may run at the same time. Processors are allocated to the different jobs either statically or dynamically; further, a processor may be taken away from a task of one job and be reassigned to a task of another job. Thus, the number of processors available to a job varies with time. Although several approaches have been proposed in the past for scheduling tasks on multiprocessors, they assume a dedicated availability of processors. Consequently, the existing scheduling approaches are not suitable for multiprogrammed systems. In this paper, we present a novel probabilistic approach for scheduling parallel tasks on multiprogrammed parallel systems. The key characteristic of the proposed scheme is its self-adaptive nature, i.e., it is responsive to systemic parameters such as number of processors available. Self-adaptation helps achieve better load balance between the different processors and helps reduce the synchronization overhead (number of allocation points). Experimental results show the effectiveness of our technique.