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

Abstract

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 ...

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

“…(ii) IoT workload distribution: The workload distribution parameter determines the distribution of IoT requests over the network. However, the availability of openly available datasets about the distribution of requests in real IoT scenarios is scarce [78]. Therefore, considering literature [22], [79], this paper explores the effect of two distributions that consist of a random (denoted as Rand in the experimental results) [80] and a Beta distribution [81] as Beta (2.0, 5.0) on the performance results.…”

Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things

“…• IoT workload distribution: The workload distribution parameter determines the distribution of IoT requests over the network. Though, the availability of openly available datasets about the distribution of requests in real IoT scenarios in scarce [73]. Therefore, considering literature [27], [74], this paper explores the effect of two different distributions that consist of a random distribution (denoted as Rand in the experimental results) [75] and a Beta distribution [76] as Beta (2.0, 5.0) on the performance results.…”

In-depth analysis and open challenges of Mist Computing

“…On the other side, more advanced applications exploit the distributed of new generation Internet networks based on Named Data Networking (NDN) fundamentals [49], which are seen as enablers of Fog and Edge smart use cases [50] since addressing and storing are combined to generate more flexible services. A similar locally limited approach is Data Distribution Services (DDS) 2 , which establishes a virtual bus to deliver many messages with high performance and to control QoS, and it has been widely used in collaborative IIoT applications such as a smart platform for the energy industry [51].…”