Router-Based Brokering for Surrogate Discovery in Edge Computing

Gedeon, Julien; Meurisch, Christian; Bhat, Disha; Stein, Michael; Wang, Lin; Mühlhäuser, Max

doi:10.1109/icdcsw.2017.61

Cited by 22 publications

(14 citation statements)

References 18 publications

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“…21 This can be extended to a distributed hash table (DHT) as well, for notifying arrival and departure and for scheduling tasks. 75 Some also suggest a hierarchical discovery approach for edges partitioned into fog parents, and fogs themselves reporting to higher-level fogs, all of which is accumulated at a discovery server. 76 Some also use the transport-level protocols, 77 eg, by having a leader device to broadcast an 802.11 WiFi beacon frame to notify spatially proximate devices that wish to join about the location of the leader to contact.…”

Section: Figurementioning

confidence: 99%

Characterizing application scheduling on edge, fog, and cloud computing resources

Varshney

Simmhan

2019

Softw Pract Exp

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Summary Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, edge and fog computing resources have emerged on the wide‐area network as part of Internet of things (IoT) deployments. These three resource abstraction layers are complementary, and offer distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and data flow models offering a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still maturing, and can benefit from learnings on cloud resources. At the same time, there is also value in using these resources cohesively for application execution. In this article, we offer a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. We first characterize the resource capabilities and limitations of these infrastructure and offer a taxonomy of application models, quality‐of‐service constraints and goals, and scheduling techniques, based on a literature review. We also tabulate key research prototypes and papers using this taxonomy. This survey benefits developers and researchers on these distributed resources in designing and categorizing their applications, selecting the relevant computing abstraction(s), and developing or selecting the appropriate scheduling algorithm. It also highlights gaps in literature where open problems remain.

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Section: Figurementioning

confidence: 99%

Characterizing application scheduling on edge, fog, and cloud computing resources

Varshney

Simmhan

2019

Softw Pract Exp

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“…Hence, it is not uncommon for requests to be directed to distant data centers. Many works have presented empirical measurements of network latencies and motivated edge computing based on those numbers [4], [12], [50]. For instance, in [12] the authors measure the mean round-trip times between New York and Berkeley to be 85 ms.…”

Section: A Promises Benefits and Drawbacks Of Edge Computingmentioning

confidence: 99%

“…To connect to wireless gateways, client devices use different access technologies. The most common are WiFi [50] or cellular [78] connections. In this domain, we can observe development in two aspects: (i) new communication standards emerge.…”

Section: B Access Technologies and Communication Patternsmentioning

confidence: 99%

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What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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“…Conventional addressing schemes are not suited to match the service-centric nature of the targeted context. Discovery procedures are typically performed with the help of an application-layer broker mechanism [32,33] that matches the service request with the best available node that is able to execute the service based on specified requirements and discovered capabilities. These broker entities, however, further complicate the reference scenario.…”

Section: Fog Computing: Key Pillars and Issuesmentioning

confidence: 99%

Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms

et al. 2019

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By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.Smart home, smart building, smart transportation, and smart energy are just a few examples of IoT application domains. In these contexts, the cloud and fog computing paradigms will play a crucial role in supporting the variety of expected services (e.g., data aggregation, filtering, manipulation for monitoring, and surveillance purposes) and in fulfilling their requirements in the presence of a huge number of heterogeneous connected devices. According to References [3,4], fog and cloud computing will complement each other to form a service continuum between the cloud and the IoT devices by providing computing, storage, control, and networking resources.On the one side, by leveraging remote data centers and centralized control, the cloud becomes the perfect candidate for managing long-term data storage and for executing complex analytics algorithms. On the other side, the fog brings computing and storage services close to the end-users, where data are generated and/or consumed. The advantages are clear: enabling interactive and delay-sensitive applications, reducing the amount of traffic reaching the core network segment, and guaranteeing high performance also in the presence of limited network bandwidth and intermittent connectivity. In the most ambitious scenario, network edge routers and hosts, ranging from access points, backhaul nodes, and Points of Presence (PoPs) to IoT gateways and user devices like smartphones and tablets, will turn into fog elements that execute services on demand.An ...

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Router-Based Brokering for Surrogate Discovery in Edge Computing

Cited by 22 publications

References 18 publications

Characterizing application scheduling on edge, fog, and cloud computing resources

Characterizing application scheduling on edge, fog, and cloud computing resources

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms

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