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
Coordinating parallel processes on networks of workstations
Journal of Parallel and Distributed Computing
Availability and utility of idle memory in workstation clusters
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Alternatives to coscheduling a network of workstations
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Implicit coscheduling: coordinated scheduling with implicit information in distributed systems
ACM Transactions on Computer Systems (TOCS)
Designing Web Usability: The Practice of Simplicity
Designing Web Usability: The Practice of Simplicity
Adaptive Parallelism and Piranha
Computer
Demand-Based Coscheduling of Parallel Jobs on Multiprogrammed Multiprocessors
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Packing Schemes for Gang Scheduling
IPPS '96 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
Adaptive scheduling under memory constraints on non-dedicated computational farms
Future Generation Computer Systems - Selected papers from CCGRID 2002
A Comparative Evaluation of Implicit Coscheduling Strategies for Networks of Workstations
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Modeling and characterizing parallel computing performance on heterogeneous networks of workstations
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Gang Scheduling with Memory Considerations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Buffered Coscheduling: A New Methodology for Multitasking Parallel Jobs on Distributed Systems
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Impact of Exploiting Load Imbalance on Coscheduling in Workstation Clusters
ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
Adaptive Parallel Job Scheduling with Flexible Coscheduling
IEEE Transactions on Parallel and Distributed Systems
Concurrency and Computation: Practice & Experience
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
State-based predictions with self-correction on Enterprise Desktop Grid environments
Journal of Parallel and Distributed Computing
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Implicit coscheduling techniques applied to non-dedicated homogeneous Networks Of Workstations (NOWs) have shown they can perform well when many local users compete with a single parallel job. Implicit coscheduling deals with minimizing the communication waiting time of parallel processes by identifying the processes in need of coscheduling through gathering and analyzing implicit runtime information, basically communication events. Unfortunately, implicit coscheduling techniques do not guarantee the performance of local and parallel jobs, when the number of parallel jobs competing against each other is increased. Thus, a low efficiency use of the idle computational resources is achieved. In order to solve these problems, a new technique, named Cooperating CoScheduling (CCS), is presented in this work. Unlike traditional implicit coscheduling techniques, under CCS, each node takes its scheduling decisions from the occurrence of local events, basically communication, memory, Input/Output and CPU, together with foreign events received from cooperating nodes. This allows CCS to provide a social contract based on reserving a percentage of CPU and memory resources to ensure the progress of parallel jobs without disturbing the local users, while coscheduling of communicating tasks is ensured. Besides, the CCS algorithm uses status information from the cooperating nodes to balance the resources across the cluster when necessary. Experimental results in a non-dedicated heterogeneous NOW reveal that CCS allows the idle resources to be exploited efficiently, thus obtaining a satisfactory speedup and provoking an overhead that is imperceptible to the local user.