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In this paper, to analyze end-to-end timing behavior in heterogeneousprocessor and network environments accurately, we adopt a heterogeneousselection value on communication contention (HSV_CC) algorithm, which cansynchronize tasks and messages simultaneously, for stream processingdistribution. In order to adapt HSV_CC to automotive data stream managementsystem (DSMSs), one must address three issues: (i) previous task and messageschedules might lead to less efficient resource usages; (ii) the conventionalmethod to determine the task scheduling order may not be best suited to dealwith stream processing graphs, and; (iii) there is a need to be able toschedule tasks with time-varying computational requirements efficiently. Toaddress (i), we propose the heterogeneous value with load balancing andcommunication contention (HVLB_CC) (A) algorithm, which considers loadbalancing in addition to the parameters considered by the HSV_CC algorithm. Wepropose HVLB_CC (B) to address issue (ii). HVLB_CC (B) can deal with streamprocessing task graphs and more various directed acyclic graphs to preventassigning a higher priority to successor tasks. In addition, to address issue(iii), we propose HVLB_CC_IC. To schedule tasks more efficiently with variouscomputation times, HVLB_CC_IC utilizes schedule holes left in processors. Theseidle time slots can be used for the execution of an optional part to generatemore precise data results by applying imprecise computation models.Experimental results demonstrate that the proposed algorithms improve minimumschedule length, accuracy, and load balancing significantly compared to theHSV_CC algorithm. In addition, the proposed HVLB_CC (B) algorithm can schedulemore varied task graphs without reducing performance, and, using imprecisecomputation models, HVLB_CC_IC yields higher precision data than HVLB_CCwithout imprecise computation models.

Scheduling Parallel and Distributed Processing for Automotive Data Stream Management System