In the last decade, there has been a significant upsurge in the demand for artificial intelligence. This remarkable growth can be attributed to the advancements in machine and deep learning techniques, coupled with th...
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In a correct control system, events are never late, nor are they early. They are delivered precisely when the engineer means to. IEC 61499 defines a modeling language for control software of distributedsystems. Howev...
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Serverless computing can favor the emergence of complex and error-prone applications. In order to gain observability in such applications, distributed tracing can be used. However, as serverless computing relies on th...
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ISBN:
(纸本)9798350322392
Serverless computing can favor the emergence of complex and error-prone applications. In order to gain observability in such applications, distributed tracing can be used. However, as serverless computing relies on the pay-per-use billing model, utilizing distributed tracing tools can have a noticeable impact on the resulting costs. Therefore, this paper investigates the impact of distributed tracing in serverless applications by exploring and comparing the efficiency characteristics of three selected distributed tracing tools - Zipkin, OpenTelemetry, and SkyWalking. In particular, the runtime, the memory usage, and the initialization duration were examined by benchmarking AWS Lambda function invocations. In the experiments, Zipkin imposed the lowest runtime overhead with an average of 10.73 %, while SkyWalking introduced the highest overhead with an average runtime overhead of 50.67 %. OpenTelemetry added 24.19% additional runtime. Besides runtime overheads, significantly higher memory usage and initialization durations were detected for all tools. Therefore, the results suggest that distributed tracing can significantly impact the efficiency of serverless applications. Nevertheless, differences could be observed concerning tracing mechanisms and use cases. This helps developers to carefully select the most suitable tracing tool considering factors such as runtime overhead, memory usage, and initialization durations.
In recent years, distributed data-processing frameworks have become popular for processing big data. However, in an HPC, where the computation and storage nodes are separated, the bandwidth between the computation and...
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ISBN:
(纸本)9798350370621
In recent years, distributed data-processing frameworks have become popular for processing big data. However, in an HPC, where the computation and storage nodes are separated, the bandwidth between the computation and storage components is small, causing a reduction in data processing throughput. Therefore, in this paper, data were stored on the computation node to solve the data processing throughput degradation. We propose an I/O acceleration method that integrates Apache Arrow and CHFS. It leverages non-volatile memory, a state-of-the-art storage device, via CHFS and leverages CHFS from a distributed data processing framework via Apache Arrow's abstract file system API. The evaluation results showed that the system achieved up to 11.60 times higher bandwidth than when reading data from the parallel file system Lustre. This study also compared with Apache Arrow with BeeOND and UnifyFS, other ad hoc filesystems. The proposed Apache Arrow CHFS showed up to 1.67x/1.23x better write performance. The implementation is published at https://***/tsukuba-hpcs/arrow-chfs
Self-calibration poses one of the primary challenges in deploying wireless sensor networks (WSNs), particularly in uncontrolled environments. While existing deep learning methods have demonstrated their effectiveness ...
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This research investigates the vulnerability of onboard navigation sensors in autonomous vehicles to cybersecurity attacks. By exploiting the interconnectedness of these systems, adversaries can manipulate sensor data...
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A current trend in service-oriented architectures is to break coarse-grained monolith systems, encapsulating all function capabilities, down into small-scale and fine-grained microservices, which work in concert. The ...
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ISBN:
(纸本)9798350330328
A current trend in service-oriented architectures is to break coarse-grained monolith systems, encapsulating all function capabilities, down into small-scale and fine-grained microservices, which work in concert. The microservices resulting from the decomposition can be independently deployed on physically distributed machines, and an extremely challenging and complex task is to ensure that the behavior emerging from their distributed interaction is equivalent to the original monolith system. Specifically, the price to be paid for the gained distribution is that the emerging microservices interaction may exhibit not only deadlocking behavior, but also extra behavior, which is undesired with respect to the original monolith. In this paper, we propose a method for automatically (i) detecting both deadlocking interactions and extra behavior, and (ii) synthesizing distributed coordinators that when interposed among the resulting microservices avoid deadlocks and undesired interactions.
This paper focuses on the fault detection problem for uncertain linear systems with distributedsensor networks. The fault detection algorithm is designed by using of distributed interval observer(DIO), which only use...
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ISBN:
(纸本)9798350322514
This paper focuses on the fault detection problem for uncertain linear systems with distributedsensor networks. The fault detection algorithm is designed by using of distributed interval observer(DIO), which only uses the outputs of each agent and the communication from its neighbors to estimate the upper and lower bounds (ULBs) of the state of the whole system. The distributed interval estimation would be able, starting from coordinate transformation and vector rearrangement, to detect the fault by monitoring the unreasonable interval in each agent. The effectiveness of the proposed fault detection strategy has been demonstrated theoretically and numerically.
This paper provides a state-of-the-art review regarding matching-based distributed computation offloading frameworks for Fog-enabled IoT systems. Given the powerful tool of matching theory, its full capability is stil...
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ISBN:
(纸本)9781665462686
This paper provides a state-of-the-art review regarding matching-based distributed computation offloading frameworks for Fog-enabled IoT systems. Given the powerful tool of matching theory, its full capability is still unexplored and unexploited in the literature. We thereby discover and discuss existing challenges and corresponding solutions that the matching theory can be applied to resolve them. Furthermore, new problems and open issues for application scenarios of modern fog-enabled IoT systems are also investigated.
Many Machine Learning (ML) based phishing detection algorithms are not adept to recognise "concept drift";attackers introduce small changes in the statistical characteristics of their phishing attempts to su...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
Many Machine Learning (ML) based phishing detection algorithms are not adept to recognise "concept drift";attackers introduce small changes in the statistical characteristics of their phishing attempts to successfully bypass detection. This leads to the classification problem of frequent false positives and false negatives, and a reliance on manual reporting of phishing by users. Profiler is a distributed phishing risk assessment tool that combines three email profiling dimensions: (1) threat level, (2) cognitive manipulation, and (3) email content type to detect email phishing. Unlike pure ML-based approaches, Profiler does not require large data sets to be effective and evaluations on real-world data sets show that it can be useful in conjunction with ML algorithms to mitigate the impact of concept drift.
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