Proceedings of the First International Workshop on Data Dissemination for Large Scale Complex Critical Infrastructures 2010
DOI: 10.1145/1862821.1862825
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Adapting and evaluating distributed real-time and embedded systems in dynamic environments

Abstract: Quality of Service (QoS)-enabled publish/subscribe (pub/-sub) middleware provides much needed infrastructure for data dissemination in distributed real-time and embedded (DRE) systems. It is hard, however, to quantify the performance of mechanisms that support multiple interrelated QoS concerns, e.g., reliability, latency, and jitter. Moreover, once an appropriate mechanism is selected, it is hard to maintain QoS properties as the operating environment fluctuates since the chosen mechanism might no longer prov… Show more

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Cited by 9 publications
(12 citation statements)
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“…ADAMANT extends our prior work [10,11] by empirically evaluating (1) the QoS delivered by DDS pub/sub middleware with respect to differences in computing and networking resources provided by cloud environments and (2) the accuracy and timeliness of ANN-based machine learning tools in determining appropriate middleware configurations. Figure 3 shows how ADAMANT works in a cloud environment (e.g., the ad-hoc SAR datacenter) to deploy cloud resources.…”
Section: Overview Of Adamantmentioning
confidence: 94%
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“…ADAMANT extends our prior work [10,11] by empirically evaluating (1) the QoS delivered by DDS pub/sub middleware with respect to differences in computing and networking resources provided by cloud environments and (2) the accuracy and timeliness of ANN-based machine learning tools in determining appropriate middleware configurations. Figure 3 shows how ADAMANT works in a cloud environment (e.g., the ad-hoc SAR datacenter) to deploy cloud resources.…”
Section: Overview Of Adamantmentioning
confidence: 94%
“…We selected ANN technology [11] due to its (1) fast and predictable performance, (2) accuracy for environments known a priori (i.e., used for ANN training) and unknown until runtime (i.e., not used for ANN training), and (3) low accidental development complexity. In particular, we chose the Fast Artificial Neural Network (FANN)(leenissen.dk/fann) implementation due to its configurability, documentation, ease of use, and open-source code.…”
Section: Artificial Neural Network Tools To Determine Middleware Confmentioning
confidence: 99%
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“…These techniques are not well-suited for enterprise DRE systems, however, since they do not provide bounded times when determining adjustments [4]. Some techniques, such as reinforcement learning [6], explore the solution space until an appropriate solution is found, regardless of the elapsed time.…”
Section: Introductionmentioning
confidence: 99%