Towards Edge Computing over Named Data Networking

Mtibaa, Abderrahmen; Tourani, Reza; Misra, Satyajayant; Burke, Jeff; Zhang, Lixia

doi:10.1109/edge.2018.00023

Cited by 43 publications

(18 citation statements)

References 12 publications

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“…Recently, NDN has been considered an enabler of edge and fog computing because it can support provider-agnostic in-network computing and caching services [34,35]. According to this new vision, interest packets can be overhauled to let a consumer request by name not only a content but also a specific computation service.…”

Section: Ndn As Enabler Of Fog Computingmentioning

confidence: 99%

“…Data packets can be overhauled to carry computation results that can be stored in the CS of NDN nodes to satisfy future requests. This is a radical departure from IP-based fog computing approaches, which usually rely on specific entities to resolve an application-layer request into the IP address of a node able to perform the computation [34].…”

Section: Ndn As Enabler Of Fog Computingmentioning

confidence: 99%

“…• Naming schemes for identifying both contents and services [6,36,37]. • Discovery schemes for allowing NDN nodes to identify services and resources supported by their neighbours, e.g., CPU, energy, and storage [8,34].…”

Section: Ndn As Enabler Of Fog Computingmentioning

confidence: 99%

“…The work in Reference [34] shows the opportunities of enabling edge computing over NDN. It assumes that fog nodes advertise the services they support and their resource utilization (e.g., CPU, GPU, memory, storage, and energy).…”

Section: Distributed Mechanismsmentioning

confidence: 99%

“…Moreover, unlike the previously discussed works on distributed service orchestration [6,7,10,34], which mainly focused on an NDN edge scenario, here, we consider the interactions between the edge nodes and the cloud computing facilities, in alignment with the cloud-to-things continuum concept of fog computing. In the conceived framework, NDN nodes first try to execute computing services along the path towards the data providers and, in case of failure, they delegate the services to the cloud or to a node in the path towards it in a transparent manner for the consumer.…”

Section: Contributionmentioning

confidence: 99%

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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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Section: Ndn As Enabler Of Fog Computingmentioning

confidence: 99%

Section: Ndn As Enabler Of Fog Computingmentioning

confidence: 99%

Section: Ndn As Enabler Of Fog Computingmentioning

confidence: 99%

Section: Distributed Mechanismsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

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

et al. 2019

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Enhanced dynamic fine‐grained popularity‐based caching algorithm for ICN‐based edge computing networks

Banerjee,

Biswash

2024

Internet Technology Letters

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In this letter, we propose an Enhanced Dynamic Fine‐Grained Popularity‐based Caching (ED‐FGPC) algorithm to efficiently cache static contents in ICN‐based edge computing networks. The content popularity changes with time. Thus, the ED‐FGPC algorithm considers the number of incoming interests, the practical file size, and the available cache size to adjust the content popularity threshold. This allows dynamic adjustment of the content popularity threshold. Additionally, ED‐FGPC incorporates a single bit called cached bit (CB) in the header field of the content. This eliminates caching redundancy in the on‐path routers. The cache eviction policy jointly considers the content popularity and the average content access timestamp for content replacement. Based on the content access pattern, this allows finer adjustment of the content eviction policy. We use analytical and mathematical modeling to derive the effectiveness and efficiency of our contribution. Results present a better hit ratio due to dynamic adjustment of the content popularity threshold and the elimination of caching redundancy from the on‐path routers.

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