Real-Time-Aware Embedded DSMS Applicable to Advanced Driver Assistance Systems

Katsunuma, Satoshi; Honda, Shinya; Sato, Kenya; Watanabe, Yousuke; Nakamoto, Yukikazu; Takada, Hiroaki

doi:10.1109/srdsw.2014.20

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…The evaluation results of both a database management system (DBMS) and a data stream management system (DSMS) showed advantage of the latter system because the DSMS can already efficiently handle continuous incoming data such as vehicle sensor data [12]. The above observation becomes a basis for developing a data-stream management system for an automotive embedded system (eDSMS) [13], [14]. In addition, a data-stream-based local dynamic map (LDM) was previously proposed [15].…”

Section: Autosarmentioning

confidence: 97%

Vehicle Embedded Data Stream Processing Platform for Android Devices

Akiyama¹,

Nakamoto²,

Yamaguchi³

et al. 2015

ijacsa

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Abstract-Automotive information services utilizing vehicle data are rapidly expanding. However, there is currently no data centric software architecture that takes into account the scale and complexity of data involving numerous sensors. To address this issue, the authors have developed an in-vehicle datastream management system for automotive embedded systems (eDSMS) as data centric software architecture. Providing the data stream functionalities to drivers and passengers are highly beneficial. This paper describes a vehicle embedded data stream processing platform for Android devices. The platform enables flexible query processing with a dataflow query language and extensible operator functions in the query language on the platform. The platform employs architecture independent of data stream schema in in-vehicle eDSMS to facilitate smoother Android application program development. This paper presents specifications and design of the query language and APIs of the platform, evaluate it, and discuss the results.

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Section: Autosarmentioning

confidence: 97%

Vehicle Embedded Data Stream Processing Platform for Android Devices

Akiyama¹,

Nakamoto²,

Yamaguchi³

et al. 2015

ijacsa

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“…In this section, we describe a DSMS for automotive embedded systems [26,27], assumptions about the target system, and a stream processing application model.…”

Section: System Modelmentioning

confidence: 99%

Scheduling parallel and distributed processing for automotive data stream management system

Rho

Azumi

Nakagawa

et al. 2017

Journal of Parallel and Distributed Computing

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In this paper, to analyze end-to-end timing behavior in heterogeneous processor and network environments accurately, we adopt and modify a heterogeneous selection value on communication contention (HSV CC) algorithm, which can synchronize tasks and messages simultaneously, for stream processing distribution. In order to adapt the concepts of a static algorithm like HSV CC to automotive data stream management system (DSMSs), one must first address three issues: (i) previous task and message schedules might lead to less efficient resource usages in this scenario; (ii) the conventional method to determine the task scheduling order may not be best suited to deal with stream processing graphs, and; (iii) there is a need to be able to schedule tasks with time-varying computational requirements efficiently. To address (i), we propose the heterogeneous value with load balancing and communication contention (HVLB CC) (A) algorithm, which considers load balancing in addition to the parameters considered by the HSV CC algorithm. We propose HVLB CC (B) to address issue (ii). HVLB CC (B) can deal with stream processing task graphs and more various directed acyclic graphs to prevent assigning a higher priority to successor tasks. In addition, to address issue (iii), we propose HVLB CC IC. To schedule tasks more efficiently with various computation times, HVLB CC IC utilizes schedule holes left in processors. These idle time slots can be used for the execution of an optional part to generate more precise data results by applying imprecise computation models. Experimental results demonstrate that the proposed algorithms improve minimum schedule length, accuracy, and load balancing significantly compared to the HSV CC algorithm. In addition, the proposed HVLB CC (B) algorithm can schedule more varied task graphs without reducing performance, and, using imprecise computation models, HVLB CC IC yields higher precision data than HVLB CC without imprecise computation models.

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“…Here, we describe stream processing, an automotive DSMS using an eDSMS [1], [3], and our task model.…”

Section: Preliminariesmentioning

confidence: 99%

“…To tackle these issues, an automotive DSMS based on a data integration architecture has been developed [1], [3] (Fig. 1).…”

Section: Data Integration Architecture Based On Dsmsmentioning

confidence: 99%

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Reservation-Based Scheduling for Automotive DSMS under High Overload Condition

Rho

Azumi

Yamaguchi

et al. 2016

Journal of Information Processing

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Recent automotive systems require various data, including data from on-board sensors and external sources to recognize environmental conditions. As the amount of sensor data used in automotive systems increases, processes that use such data become increasingly complicated. In addition, similar data processing can be duplicated over multiple applications. To address these issues, a data stream management system (DSMS) for automotive systems based on a data integration architecture has been developed. However, hard real-time deadlines cannot be guaranteed due to unpredictable load changes caused by data streams. For example, the arrival time and CPU utilization requested by data streams from vehicle-to-vehicle communications change rapidly depending on environmental conditions. We propose the reservation-based operator path earliest deadline first (ROP-EDF) scheduling algorithm for an automotive DSMS under overload conditions. The proposed algorithm reserves processor time preferentially for hard real-time tasks so that tasks can meet deadlines under overload conditions. ROP-EDF can be used for load testing on a single processor system. Experimental results show the effectiveness of the proposed algorithm compared with existing algorithms relative to the deadline miss ratio under overload conditions.

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Real-Time-Aware Embedded DSMS Applicable to Advanced Driver Assistance Systems

Cited by 6 publications

References 4 publications

Vehicle Embedded Data Stream Processing Platform for Android Devices

Vehicle Embedded Data Stream Processing Platform for Android Devices

Scheduling parallel and distributed processing for automotive data stream management system

Reservation-Based Scheduling for Automotive DSMS under High Overload Condition

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