EMMA: Distributed QoS-Aware MQTT Middleware for Edge Computing Applications

Rausch, Thomas; Nastić, Stefan; Dustdar, Schahram

doi:10.1109/ic2e.2018.00043

Cited by 70 publications

(54 citation statements)

References 16 publications

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“…More recently, there have been a number of other studies on messaging middleware for edge computing applications. The middleware investigated in [299] optimized diverse user QoS requirements and orchestrated connections between users and brokers, [300] leveraged SDN to monitor network conditions for resilient data exchange of mission-critical applications, and the messaging middleware proposed in [301] enabled the development and deployment of emerging applications in distributed and heterogeneous edge computing systems.…”

Section: ) Middleware For Edge Computingmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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Section: ) Middleware For Edge Computingmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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“…34 The popular middleware for message passing between producers and consumers of data is MQTT protocol. Several authors [38][39][40][41] used MQTT brokers as middleware between producer's and consumer's data in fog environment. Out of these works, authors 38,39 utilized a centralized MQTT broker to enable telemetry services in fog-enabled environment.…”

Section: Related Workmentioning

confidence: 99%

“…These metrics values are in a certain range close to real variation of FNC. The formal representation of class is defined as [20,30,40,50,60,70,80] and l=1… size of (Z[]). We have generated 504 distinct classes.…”

Section: Implementation Of Fnc and Their Servicesmentioning

confidence: 99%

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

2020

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Large gathering events such as open air rock concerts and religious gatherings require a semipermanent communication and computation infrastructure for providing IoT-based smart solutions. In these places, the installation of wired network is not a cost-effective solution to provide connectivity. Wireless mesh network (WMN) presents itself as a quickly deployable and dismantable solution to provide wireless connectivity to IoT devices. In this paper, we have proposed a "wireless-fog mesh" computing framework for installation of microservices in network devices of WMN. This requires continuous monitoring of residual resources of devices and associated network characteristics. Our proposed fog controller exploits underutilized resources of mesh devices and network metrics for mapping a microservice to a fog node. This controller uses a DHT-based overlay of fog nodes (also acting as distributed MQTT broker) for disseminating resource profiles of fog nodes. The network metrics like path cost, routing loads, number of stations, and delay are used to implement a policy-based FNS algorithm for electing a fog node to run microservice. We have designed and implemented a testbed that consists of 10 OpenWrt-based Raspberry Pi3 node to form a WMN. We have deployed various microservices such as MQTT broker, air quality, web servers, annd video surveillance in fog nodes (mesh routers and access points) to test the FNS algorithm. The results establish that the FNS algorithm significantly outperforms the random node selection (RNS) algorithm and advocates the applicability of our wireless-fog mesh framework for semipermanent use cases.

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“…Beyond the geo-context related work, there is also related work on (IoT) data distribution approaches and techniques; some of these also consider a subset of the above introduced geo-context dimensions. We see the most closely related approaches in the area of pub/sub, e.g., in IoT [26,27,28,29,30] but also for applications ranging from enterprise to web computing [31,32,33,34,35,36,37,38,39]. In addition, there is also non-pub/sub related work on data distribution in general, e.g., [40,41,42]; neither of these explicitly captures geo-context information.…”

Section: Rwmentioning

confidence: 99%

GeoBroker: Leveraging geo-contexts for IoT data distribution

Hasenburg

Bermbach

2020

Computer Communications

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In the Internet of Things, the relevance of data often depends on the geographic context of data producers and consumers. Today's data distribution services, however, mostly focus on data content and not on geo-context, which could help to reduce the dissemination of excess data in many IoT scenarios. In this paper, we propose to use the geo-context information associated with devices to control data distribution. We define what geocontext dimensions exist and compare our definition with concepts from related work.Furthermore, we designed GeoBroker, a data distribution service that uses the location of things, as well as geofences for messages and subscriptions, to control data distribution. This way, we enable new IoT application scenarios while also increasing overall system efficiency for scenarios where geo-contexts matter by delivering only relevant messages. We evaluate our approach based on a proof-of-concept prototype and several experiments.

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EMMA: Distributed QoS-Aware MQTT Middleware for Edge Computing Applications

Cited by 70 publications

References 16 publications

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Wireless‐Fog Mesh: A framework for in‐network computing of microservices in semipermanent smart environments

GeoBroker: Leveraging geo-contexts for IoT data distribution

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