Operator placement for efficient distributed complex event processing in MANETs

Starks, Fabrice; Plagemann, Thomas

doi:10.1109/wimob.2015.7347944

Cited by 15 publications

(31 citation statements)

References 15 publications

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“…The unconstrained optimization phase is performed in a centralized manner, while the constrained optimization phase is performed in a distributed manner. [38] X Pietzuch et al [40] X Bonfils et al [9] X Chatzimilioudis et al [11] X Chatzimilioudis et al [13] X Chatzimilioudis et al [12] X F. Starks et al [45] X…”

Section: Constrained Optimizationmentioning

confidence: 99%

“…Another approach suggested in [45] uses the location of the data sources that will host the leaves of the operator graph, to direct the distributed placement scheme. Only relevant candidates for processing a part of the operator graph participate in the placement scheme.…”

Section: Decentralized Placement Algorithmmentioning

confidence: 99%

“…All publications include at least one simulation study, except for two publications based on emulation that employ both simulated and real components [41,45]. As a result, 14 of 19 evaluation reports are based on either simulation (11 reports) or emulation (three reports).…”

Section: Performance Evaluation For Mobile Dcepmentioning

confidence: 99%

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Mobile Distributed Complex Event Processing—Ubi Sumus? Quo Vadimus?

Starks

Goebel

Kristiansen

et al. 2017

Lecture Notes on Data Engineering and Communications Technologies

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One important class of applications for the Internet of Things is related to the need to gain timely and continuous situational awareness, like smart cities, automated traffic control, or emergency and rescue operations. Events happening in the real-world need to be detected in real-time based on sensor data and other data sources. Complex Event Processing (CEP) is a technology to detect complex (or composite) events in data streams and has been successfully applied in high volume and high velocity applications like stock market analysis. However, these application domains faced only the challenge of high performance, while the Internet of Things and Mobile Big Data introduce a new set of challenges caused by mobility. This chapter aims to explain these challenges and to give an overview on how they are solved respectively how far state-of-the-art research has advanced to be useful to solve Mobile Big Data problems. At the infrastructure level the main challenge is to trade performance against resource consumption and energy efficiency and operator placement is the most dominant mechanism to address these problems. At the application and consumer level, mobile queries pose a new set of challenges for CEP related to continuously changing positions of consumers and data sources, and the need to adapt the query processing to these changes. Finally, proper methods and tools for systematical testing and reproducible performance evaluation for mobile distributed CEP are needed but not yet available.

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Section: Constrained Optimizationmentioning

confidence: 99%

Section: Decentralized Placement Algorithmmentioning

confidence: 99%

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Mobile Distributed Complex Event Processing—Ubi Sumus? Quo Vadimus?

Starks

Goebel

Kristiansen

et al. 2017

Lecture Notes on Data Engineering and Communications Technologies

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“…[17], [12], [14] propose a Distributed Mobile CEP which each mobile device has a specific role. In [17], the system supports group creation through MANETs (Mobile Adhoc Networks), but does not share data to fog servers as well as does not process complex events indeed.…”

Section: State Of the Artmentioning

confidence: 99%

“…Their approach uses 3G network as tolerance-fault mechanism increasing the response time. [12] and [14] present a mobile architecture to CEP system, but their strategy fit only for a restrict area, where publishers and consumers are static.…”

Section: State Of the Artmentioning

confidence: 99%

A policy-based coordination architecture for distributed complex event processing in the internet of things

Lyra

Ferraz

Gama

2016

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

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The dissemination of powered communication devices has instigated a new technological paradigm called Internet of Things (IoT). These devices are present in our life, capturing information about people's routines and using them as data stream to support many decision-making processes. Nevertheless, many IoT applications take into consideration only the information related to their local context and the data analysis is done by a cloud server, increasing the feedback information latency to the data consumers and reducing the information quality.This work adopts the use of a distributed complex event processing (CEP) to analyse data considering all the IoT devices to execute data processing and distribution through a policy-based coordination architecture, building a µDCEP , called GiTo. The policies define rules for coordination processing which are monitored through the distributed CEP engine. This approach focus on information quality improvement and time reduction between data generation and information acquired by the consumers.

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