Dynamic and coordinated software reconfiguration in distributed data stream systems

Vasconcelos, Rafael Oliveira; Vasconcelos, Igor; Endler, Markus

doi:10.1186/s13174-016-0050-z

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…Trade-offs Reconfigurations of an SoS require to preserve the current execution state (e.g., data values, active operations, interdependencies) of all entities (e.g., consumers, constituents, mediators, controllers) involved in the reconfiguration plan. Strategies that ensure safety and non-disruptive reconfiguration approaches [66] must be defined and considered in MediArch.…”

Section: Rq4-how Can Sos Architectures Based On Mediarch Evolve?mentioning

confidence: 99%

Software mediators as first-class entities of systems-of-systems software architectures

2019

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Context: In contrast to traditional software systems that are mostly created from scratch, current software systems are engineered as a junction of systems already in operation. Examples can be found in domains, such as smart cities, crisis and emergency, IoT, big data, industry 4.0, and connected health systems. Most of them can be considered systems-of-systems (SoS), since they refer to alliances of operational and managerial independent software-intensive systems, which are sometimes distributed over different environments. Therefore, SoS software architectures must be dynamic, evolve over time, and support the execution of emergent behaviors to accomplish SoS missions. They must be also designed to enable the connection of heterogeneous systems, making possible their interoperation, communication, coordination, cooperation, and collaboration, most of the times, in a seamless way. Similar challenges have been addressed by using software mediators as architectural entities. However, the application of mediators in SoS has not been properly explored. Goal: This article introduces MediArch, a layered architecture that considers mediators as first-class software entities to be used in the construction of SoS architectures. Our objective is to present evidence about how MediArch's can support the construction of SoS architectures. Methods: The following four steps were conducted to define MediArch: (1) identification of mediation requirements to allow SoS properties; (2) establishment and categorization of twelve types of mediators, for enabling capabilities of communication and control of constituent systems interactions and conversion of heterogeneous messages exchanged through a mediation infrastructure; (3) specification of duties, behaviors, assumptions, and guarantees of mediators; and (4) organization of MediArch in three layers, namely, the constituents & consumer systems layer; the communication, conversion, & coordination layer; and the control layer. This architecture was used as the backbone for the software architectures of two SoS in different domains, namely, flood monitoring system-of-systems (FMSoS), and health-care supportive home system-of-systems (HSH-SoS), for providing evidence on how MediArch supports the architecting process of SoS. Results: MediArch (1) supports the integration of independent constituent systems, (2) provides strategies to manage emergent behaviors, (3) defines different schemes of control authorities, (4) offers elements to support SoS evolution, and (5) promotes the resilience and adaptability of SoS architectures. Conclusions: Although MediArch contributes to the establishment of SoS architectures, some challenges related to performance, resource consumption, security, safety, and non-disruptive reconfigurations must still be overcome.

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Section: Rq4-how Can Sos Architectures Based On Mediarch Evolve?mentioning

confidence: 99%

Software mediators as first-class entities of systems-of-systems software architectures

2019

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“…However, due to the large volume of data generated by some mobile smart device (i.e., sensors in vehicles), it has become impractical and costly to transmit all data to a cloud [17]. Among the many challenges of the IoT, such as heterogeneity and interoperability, the authors of [18] also highlighted the following: (i) middleware for communication with the cloud [19]; (ii) technologies supporting dynamic configuration [20]; and (iii) robust, real-time mechanisms for data filtering and data mining to cope with the large amount of raw data provided by smart devices and reduce the amount of data transmitted to the cloud.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Vasconcelos

Olivieri

et al. 2017

J Internet Serv Appl

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The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones can be used to online detect and classify the driver's behavior in near real-time. To do so, we first modeled and performed an intrinsic evaluation to assess the performance of three outlier detection algorithms formulated as a data stream processing network which receives as input and processes data streams of smartphone and vehicle sensors. Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers' maneuvers as either cautious or reckless. Thus, we adapted a data mining mechanism which takes into account a sensor's data rates and power to determine driver behavior in the scoring process. Finally, as the intrinsic evaluation does not necessarily reveal how well an algorithm will perform in a real-world scenario, we evaluated the algorithm that achieved the best result in a real-world case study to assess drivers' driving behavior. Our results indicate that the algorithm performs quickly and accurately; the algorithm classifies driver behavior with 95.45% accuracy. Moreover, such results are obtained within 100 milliseconds of processing time on average.

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Dynamic and coordinated software reconfiguration in distributed data stream systems

Cited by 2 publications

References 49 publications

Software mediators as first-class entities of systems-of-systems software architectures

Software mediators as first-class entities of systems-of-systems software architectures

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

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