Run-time monitoring using bounded constraint instance discovery within big data streams

Drusinsky, Doron

doi:10.1007/s11334-015-0270-6

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…This section presents the contributions related to the monitoring of service-oriented systems. The market for monitoring systems and tools that observe the run-time behavior is evolving rapidly [8,19,21]. However, most of existing solutions have not been designed and implemented with SOA applications in focus.…”

Section: Related Workmentioning

confidence: 99%

“…If the RM U module obtains the client's request, to which the response has already been saved, then such a saved response is sent to the client, and there is no necessity to send this request once again to the service (in this way the mechanism of communication history is also used to ensure idempotency of all requests). However, when client B invokes service X (client and invoked service have separate default RM U modules), then a client obtains the URI of requested service default RM U from its default RM U (7,8), and sends back this information to the CIM (9), which reissues the request to a proper RM U (10,11).…”

Section: Outline Of Reserve Processingmentioning

confidence: 99%

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On Monitoring and Self-Adaptation to Dynamic Nature of SOA in ReServE Environment

Kobusińska

2018

Foundations of Computing and Decision Sciences

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Reliability is one of the bigest challenges faced by service-oriented systems. Therefore, to solve this problem, we have proposed ReServE - Reliable Service Environment. ReServE increases fault-tolerance of SOA systems and ensures consistent processing despite failures. However, the proposed environment imposes also the performance overhead. Thus, in this paper, we extended ReServE and added a monitoring feature provided by the M3 service. As a consequence, the extended environment can adjust appropriately the load of its modules to the changing interaction and behaviour patterns of service oriented systems. We have experimentally shown that the proposed solution, while providing the required level of reliability, decreases significantly the performance overhead.

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Section: Related Workmentioning

confidence: 99%

Section: Outline Of Reserve Processingmentioning

confidence: 99%

On Monitoring and Self-Adaptation to Dynamic Nature of SOA in ReServE Environment

Kobusińska

2018

Foundations of Computing and Decision Sciences

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“…DStreams can be created either from input data stream from sources such as Kafka, Flume, and Twitter, or by applying high-level operations on other DStreams. More details about Spark Streaming are presented by Drusinsky (2016).…”

Section: Related Workmentioning

confidence: 99%

A distributed real-time data prediction framework for large-scale time-series data using stream processing

Zhu

et al. 2017

IJICC

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Purpose The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand. Design/methodology/approach First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework. Findings Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable. Originality/value This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

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Run-time monitoring using bounded constraint instance discovery within big data streams

Cited by 2 publications

References 2 publications

On Monitoring and Self-Adaptation to Dynamic Nature of SOA in ReServE Environment

On Monitoring and Self-Adaptation to Dynamic Nature of SOA in ReServE Environment

A distributed real-time data prediction framework for large-scale time-series data using stream processing

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