A hybrid approach to adaptive load sharing and its performance

The average response time of tasks in a distributed system depends on the strategy by which workload is shared among the nodes of the system. A common approach to load sharing is to resort to some distributed algorithm that arranges for task transfer between nodes based on information on the system's state. In this paper, we depict a hybrid approach to adaptive load sharing which outperforms existing algorithms, and is especially effective in response to peaks of workload, under both heavy and light system load conditions. The strategy we propose is novel in that it relies on a fully distributed algorithm when the system is heavily loaded, but resorts to a centrally coordinated one when parts of the system become idle, The transition from one algorithm to the other is performed automatically, and the simplicity of the algorithms proposed makes it possible to use a centralized component without incurring in scalability problems and presenting instabilities. Both algorithms are very lightweight and do not need any tuning of parameters. Simulations show that the hybrid approach performs well under all load conditions and task generation patterns, it is weakly sensitive to processing overhead and communication delays, and scales well (to hundred of nodes) despite the use of a centralized component.