Practical Edge Analytics: Architectural Approach and Use Cases

Anand, Narendra; Chintalapally, Anuraag; Puri, Colin; Tung, Teresa

doi:10.1109/ieee.edge.2017.43

Cited by 5 publications

(7 citation statements)

References 1 publication

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“…Deploying the analytics solution in Docker container can provide ease of service management and orchestration for edge nodes. Anand et al [26] use Docker container to deploy a practical, edge analytics framework in resources-constrained heterogenous environments. It provides an agnostic logical abstraction layer residing over existing hardware and software layers enabling ease of orchestration.…”

Section: Container For Edge Computingmentioning

confidence: 99%

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Chen

Zhou

2021

Processes

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Industry 4.0 promotes manufacturing and process industry towards digitalization and intellectualization. Edge computing can provide delay-sensitive services in industrial processes to realize intelligent production. Lightweight virtualization technology is one of the key elements of edge computing, which can implement resource management, orchestration, and isolation services without considering heterogenous hardware. It has revolutionized software development and deployment. The scope of this review paper is to present an in-depth analysis of two such technologies, Container and Unikernel, for edge computing. We discuss and compare their applicability in terms of migration, security, and orchestration for edge computing and industrial applications. We describe their performance indexes, evaluation methods and related findings. We then discuss their applications in industrial processes. To promote further research, we present some open issues and challenges to serve as a road map for both researchers and practitioners in the areas of Industry 4.0, industrial process automation, and advanced computing.

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Section: Container For Edge Computingmentioning

confidence: 99%

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Chen

Zhou

2021

Processes

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“…The fog resources may be made available as-a-service, similar to cloud resources. These may be virtualized or non-virtualized infrastructure [8], with containers offering a useful alternative to hypervisor-based VMs that may be too heavy-weight for lower-end fog resources [3]. However, there is a still a lack of a common platform, and programming models are just emerging [25,38].…”

Section: Characteristicsmentioning

confidence: 99%

“…Data can also be filtered or aggregated to send only the necessary subset to the cloud. Lastly, metadata describing the entities in the eco-system will be essential for information integration from diverse domains [3]. These can be static or slow changing data, or even complex knowledge or semantic graphs that are constructed.…”

Section: Role Of Big Datamentioning

confidence: 99%

“…Tracking and managing content across edge, fog and cloud will be a key challenge. Part of this is the result of devices in IoT acting as data sources and compute platforms, which necessitates coordination across the cyber and physical worlds [3]. The generation of event streams that are transient and need to be processed in a timely manner poses additional challenges to the velocity dimension of big data [34].…”

Section: Data Managementmentioning

confidence: 99%

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Big Data and Fog Computing

Simmhan¹

2018

Encyclopedia of Big Data Technologies

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Cloudlets DefinitionsFog Computing A model of distributed computing comprising of virtualized, heterogeneous, commodity computing and storage resources for hosting applications, analytics, content and services, that are accessible with low latency from the edge of wide area networks where clients are present, while also having back-end connectivity to cloud computing resources. BackgroundEver since computers could connect over a network, computing paradigms have under-gone cyclical phases on where within the network the computation is performed. While the original mainframes till the 1970's were large, centralized time-sharing machines accessed by multiple users through remote terminals, the Personal Computers (PCs) of the 1980's heralded local processing for individuals [35]. The growth of Local-Area Networks (LAN), the Internet, and the World Wide Web (WWW) brought about client-server models in the 1990's where many clients could access content hosted on individual servers, though most of the processing was still done on the PC [48]. Web services and eCommerce of the 2000's lead to the growth of cloud computing, where computation once again skewed to centralized data centers, but with server-farms rather than single servers hosting services that were consumed by PCs [27]. A complementary phenomenon in that decade was Peer-to-Peer (P2P) systems where PCs distributed across the Internet would work collaboratively for content sharing, to tackle bandwidth limitations of the Internet [32]. Both cloud computing and P2P signaled the arrival of the Big Data age, where the ability to collect large enterprise, scientific and web datasets and media content put an emphasis on being able to share and process them at large scales. The decade of 2010 is seeing a similar cyclical shift, but at a faster pace due to several technology advances. Starting with a more centralized cloud computing model hosting thousands of Virtual Machines (VMs), we have seen the roll-out of pervasive broadband Internet and cellular network communication combined with the rapid growth of smart phones as general-purpose computing platforms backed by cloud computing. Internet of Things (IoT) is yet another paradigm, enabled by the convergence of these other technologies [21]. Sensors and constrained devices connected to the Internet are being deployed to support vertical IoT domains such as personal fitness using wearables, smart utilities using metering infrastructure, and even self-driving cars. Both smart phones and IoT mark the advent of Edge Computing (or mobile cloud computing). Here, ubiquitous devices numbering in the billions are present at the edge of the Wide-Area Network (WAN) that is the Internet, and host applications that

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“…Massive raw data transfer is needed for building and updating such models. Since this is prohibitive for IoT environments due to constraints like limited network bandwidth, computational power, latency and energy, edge computing comes into play [10], [8], [6]. Such paradigm can be adopted to cope with this challenge by pushing as much intelligent computing logic for analytics as possible close to computing & sensing Edge Devices (EDs) [1], [7].…”

Section: Introductionmentioning

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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Practical Edge Analytics: Architectural Approach and Use Cases

Cited by 5 publications

References 1 publication

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Big Data and Fog Computing

Edge-Centric Efficient Regression Analytics

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