Pushing Analytics to the Edge

Kamath, Goutham; Pavan, Agnihotri; Valero, Maria; Sarker, Krishanu; Song, Wen-Zhan

doi:10.1109/glocom.2016.7842181

Cited by 37 publications

(18 citation statements)

References 12 publications

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“…Artificial Intelligence and Machine Learning [345]. presents edge stochastic gradient descent (EdgeSGD), a decentralized SGD algorithm for solving linear regression problem with the objective of estimating the feature vector on the edge node.…”

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confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,116

613

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With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

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mentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,116

613

View full text Add to dashboard Cite

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“…Analytics are derived by models dealing with dynamic optimal decisions for data deliver in light of communication efficiency [27], [6]. Several schemes exploit the computational capability of edge nodes to launch algorithms directly at the data sources [2], [9], [16].…”

Section: Related Workmentioning

confidence: 99%

Knowledge-centric Analytics Queries Allocation in Edge Computing Environments

Sagkriotis

Kolomvatsos

Anagnostopoulos

et al. 2019

2019 IEEE Symposium on Computers and Communications (ISCC)

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The Internet of Things (IoT) involves a huge number of devices that collect data and deliver them to the Cloud. The processing of data at the Cloud is characterized by increased latency in providing responses to analytics queries defined by analysts or applications. Hence, Edge Computing (EC) comes into the scene to provide data processing close to the source. The collected data can be stored in edge devices and queries can be executed there to reduce latency. In this paper, we envision a case where entities located in the Cloud undertake the responsibility of receiving analytics queries and decide on the most appropriate edge nodes for queries execution. The decision is based on statistical signatures of the datasets of nodes and the statistical matching between statistics and analytics queries. Edge nodes regularly update their statistical signatures to support such decision process. Our performance evaluation shows the advantages and the shortcomings of our proposed schema in edge computing environments.

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“…Such approach enforces nodes to adopt the same regression algorithm, which is not required in our approach providing the flexibility of hiring different regression models in EDs; our approach relies on the prediction performance of local models independently of the adopted regression algorithms on EDs. Recently, approaches for pushing analytics to the edge are proposed [12] either reduced to distributed parametric regression [16] (whose limitations are discussed above) or to selective data forwarding [15], [11], [13]. Specifically, [15] deals with time-optimized data forwarding among EDs and EGs in light of maximizing the quality of RA.…”

Section: B Related Work and Contributionmentioning

confidence: 99%

Edge-Centric Efficient Regression Analytics

Harth

Anagnostopoulos

2018

2018 IEEE International Conference on Edge Computing (EDGE)

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We introduce an edge-centric parametric predictive analytics methodology, which contributes to real-time regression model caching and selective forwarding in the network edge where communication overhead is significantly reduced as only model's parameters and sufficient statistics are disseminated instead of raw data obtaining high analytics quality. Moreover, sophisticated model selection algorithms are introduced to combine diverse local models for predictive modeling without transferring and processing data at edge gateways. We provide mathematical modeling, performance and comparative assessment over real data showing its benefits in edge computing environments.

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Pushing Analytics to the Edge

Cited by 37 publications

References 12 publications

All one needs to know about fog computing and related edge computing paradigms: A complete survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Knowledge-centric Analytics Queries Allocation in Edge Computing Environments

Edge-Centric Efficient Regression Analytics

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