Load sharing with consideration of future task arrivals in heterogeneous distributed real-time systems

The authors propose a new loading sharing (LS) algorithm for real-time applications that accounts for the effect of future task arrivals on locating the best receiver for each unguaranteed task. The LS algorithm minimizes not only the probability of transferring an unguaranteed task T to an incapable node with Bayesian analysis, but also the probability that a remote node fails to guarantee T because of future arrivals of higher-laxity tasks with queuing analysis. All parameters needed for a node´s LS decision are collected/estimated online using time-stamped region-change broadcasts and Bayesian estimation. Simulation results show that the proposed LS algorithm outperforms other existing algorithms in minimizing the probability of dynamic failure, task collisions, and excessive task transfers. The performance improvement becomes more pronounced as the degree of system heterogeneity increases