History-driven dynamic load balancing for recurring applications on networks of workstations

The existing dynamic approaches to load balancing on parallel or distributed computer systems are primarily based on the current system load. Often, variations in the future exertable load are neglected or assumed as fixed. In this study, a new dynamic load balancing scheme (DYLOBA) is presented, where both the current system load and the load to be exerted by the application are equally important. The target system chosen is a general purpose network of workstations (NOW). The approach utilizes the past execution statistics of the applications. In this sense, information on the run time system load and resource requirement of the applications, averaged over past executions, is integrated. DYLOBA subsumes that most demanding applications are recurring. Thus, in the long run, it will be in possession of the execution histories of most applications. The system load balances both independent and parallel applications. Once an application joins DYLOBA it is permanently given a unique identification, to monitor it during future executions. DYLOBA was subjected to selective artificial parallel applications. The performance results under DYLOBA showed significant improvements over three other, rather conventional, task assignment schemes: random, fixed, and worst.