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Abstract

Abstract

The amount of data handled by real-time and embedded applications is increasing. Also, applications normally have constraints with respect to freshness and timeliness of the data they use, i.e., results must be produced within a deadline using accurate data. This calls for data-centric approaches when designing embedded systems, where data and its meta-information (temporal correctness requirements etc) are stored centrally. The focus of this paper is on maintaining data freshness in soft real-time embedded systems and the target application is vehicular systems. The contributions of this paper are three-fold. We (i) define a specific notion of data freshness by adopting data similarity in the value-domain of data items using data validity bounds that express required accuracy of data, (ii) present a scheme for managing updates in response to changes in the data items; and (iii) present a new on-demand scheduling algorithm, On-Demand Depth-First Traversal denoted ODDFT, for enforcing data freshness by scheduling and executing update transactions. Performance experiments show that, by using our updating scheme and introduced notion of data freshness in the value-domain, computational work imposed by updates is reduced for both the new ODDFT and well-established on-demand algorithms. Moreover, ODDFT improves the consistency of produced results compared to well-established algorithms.

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