@proceedings {Dautov2018792,
	title = {Pushing intelligence to the edge with a stream processing architecture},
	journal = {Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017},
	year = {2017},
	note = {cited By 3; Conference of Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 ; Conference Date: 21 June 2017 Through 23 June 2017; Conference Code:134517},
	pages = {792-799},
	publisher = {Institute of Electrical and Electronics Engineers Inc.},
	address = {Exeter, UK - 21-23 June 2017},
	abstract = {<p>The cloud computing paradigm underpins the Internet of Things (IoT) by offering a seemingly infinite pool of resources for processing/storing extreme amounts of data generated by complex IoT systems. The cloud has established a convenient and widely adopted approach, where raw data are vertically offloaded to cloud servers from resource-constrained edge devices, which are only seen as simple data generators, not capable of performing more sophisticated processing activities. However, there are more and more emerging scenarios, where the amount of data to be transferred over the network to the cloud is associated with increased network latency, making the results of the computation obsolete. As various categories of edge devices are becoming more and more powerful in terms of hardware resources - specifically, CPU and memory - the established way of off-loading computation to the cloud is not always seen as the most convenient approach. Accordingly, this paper presents a Stream Processing architecture for spreading workload among a local cluster of edge devices to process data in parallel, thus achieving faster execution and response times. The experimental results suggest that such a distributed in-memory approach to data processing at the very edge of a computational network has a potential to address a wide range of IoT-related scenarios. {\textcopyright} 2017 IEEE.</p>
},
	keywords = {Apache NiFi, cloud computing, Cluster computing, Computational networks, Data handling, Edge computing, Green Computing, Hardware resources, Horizontal offloading, Internet of thing (IOT), Internet of Things, Memory architecture, Network architecture, Network latencies, Processing activity, Stream processing},
	isbn = {9781538630655},
	doi = {10.1109/iThings-GreenCom-CPSCom-SmartData.2017.121},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047094836\&doi=10.1109\%2fiThings-GreenCom-CPSCom-SmartData.2017.121\&partnerID=40\&md5=4e5a4b0eaffa179565af183066520cdf},
	author = {Rustem Dautov and Salvatore Distefano and Dario Bruneo and Francesco Longo and Giovanni Merlino and Antonio Puliafito}
}
@proceedings {Bruneo201562,
	title = {A framework for the 3-D cloud monitoring based on data stream generation and analysis},
	journal = {Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)},
	year = {2015},
	note = {cited By 0; Conference of 14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015 ; Conference Date: 11 May 2015 Through 15 May 2015; Conference Code:113524},
	pages = {62-70},
	publisher = {IEEE Computer Society},
	address = {Ottawa, Canada, 11-15 May 2015},
	abstract = {<p>Cloud monitoring is one important aspect for effective cloud management. Currently, cloud monitoring solutions can be classified into three groups: the ones not considering multiple layers or real time data analysis, the ones considering multiple layers but not real time data analysis, and the ones only considering real time data analysis. However, all these solutions fail to provide frameworks able to combine together monitoring in multiple layers and data stream analysis for detecting situations where multiple management actions are applicable in different layers of the cloud environment. This paper addresses this gap and proposes the Ceiloesper framework. Such a framework extends the OpenStack Ceilometer technology with Esper CEP and enables collection and analysis of information according to the principles defined in the 3-D cloud monitoring model, proposed in a previous work. The main contributions of this paper are: (i) the definition of the concept of Situation of Interest (SoI) leading to multiple management actions; (ii) the Ceiloesper architecture for a monitoring solution combining traditional monitoring elements with CEP; (iii) extensions to the Ceilometer OpenStack technology. We tested the Ceiloesper framework on a scenario based on the Wordpress application and the experimental results show its effectiveness. {\textcopyright} 2015 IEEE.</p>
},
	keywords = {Cloud environments, Cloud managements, Cloud monitoring, Data communication systems, Data handling, Data stream, Different layers, Information analysis, Meteorological instruments, monitoring, Multiple layers, Network management, Real time data analysis, Scenario-based},
	isbn = {9783901882760},
	doi = {10.1109/INM.2015.7140277},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84942574737\&partnerID=40\&md5=9727c6a66767d639f5c5cfd3bd7d96c9},
	author = {Dario Bruneo and Francesco Longo and Clarissa Marquezan}
}
