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This paper proposes a dynamic adaptive coscheduling model DASIC to take advantage of excess available resources in a network of workstations (NOW).Besides coscheduling related subtasks dynamically, DASIC can scale up or down the process space depending upon the number of available processors on an NOW.Based on the dynamic idle processor group (IPG), DASIC employs three modules:the coscheduling module, the scalable scheduling module and the load balancing module, and uses six algorithms to achieve scalability. A simplified DASIC was also implemented, and experimental results are presented in this paper, which show that it can maximize system utilization, and achieve task parallelism as much as possible.
This paper proposes a dynamic adaptive coscheduling model DASIC to take advantage of excess available resources in a network of workstations (NOW) .Besides coscheduling related subtasks dynamically, DASIC can scale up or down the process space depending upon the number of available processors on an NOW . Based on the dynamic idle processor group (IPG), DASIC employs three modules: the coscheduling module, the scalable scheduling module and the load balancing module, and uses six algorithms to achieve scalability. A simplified DASIC was also implemented, and experimental results are presented in this paper, which show that it can maximize system utilization, and achieve task parallelism as much as possible.