1,720,997 research outputs found
Migration of Multi-container Services in the Fog to Support Things Mobility
No full textIntegration between fog computing and the Internet of Things (IoT) paves the way to a plethora of promising opportunities. Device mobility might however impair fog computing benefits (e.g., low latency), which are indeed an outcome of fog proximity to end users/devices. A solution to this problem is to migrate the fog service across the fog infrastructure, thus to keep the distance to the served mobile device as low as possible. In this paper, we consider a fog service to be implemented as the combination of two containers, and we detail the demo through which we plan to show the impact of fog service migration on application performance. To this purpose, we plan to deploy an Augmented Reality (AR) application that detects vehicles in video frames and augments the latter with bounding boxes built around the detected vehicles. We offer to the audience the possibility to: (i) interact with the employed testbed by triggering device mobility; (ii) visualise the difference between migrating and not migrating the fog service in response to device mobility
Enhanced Support of LWM2M in Low Power and Lossy Networks
No full textThe Internet of Things (IoT) extends the Internet connectivity into devices and everyday objects. This huge volume of connected devices needs to be managed considering the severe energy, memory, processing, and communication limitations of IoT devices and networks. OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices and related service enablement. In this work we propose the introduction of a LWM2M Proxy in between IoT devices and the management server, in order to control the flow of LWM2M requests sent to IoT devices over a low-power and lossy network, and therefore avoid that device and network resources get overloaded. We evaluate the proposed solution by simulation and show that it strongly improves the performance of LWM2M in terms of service delay as compared to the standard case with no Proxy
The Impact of Container Migration on Fog Services as Perceived by Mobile Things
No full textThe integration between fog computing and the Internet of Things (IoT) creates plenty of new opportunities. Fog computing nodes run complex tasks on behalf of IoT devices, and the topological proximity of fog computing to the IoT enables several advantages (e.g., low latency). However, some IoT devices are mobile, and mobility may compromise the fog advantages. When a device moves, the communication path to the corresponding fog service may increase, with an impact on the fog advantages (which are a consequence of fog proximity) and overall performance. To overcome this issue, the fog service may be migrated across the fog computing infrastructure and maintained close enough to the served IoT device(s). It is worth noting, though, that service migration comes at a cost and may affect application Quality of Service (QoS). In this paper, we consider a fog service to be implemented as multiple containers, having one of them encapsulating an MQTT broker. Our contribution is the evaluation of the impact of container migration, which is considered in various flavours, on application QoS as perceived by mobile things. To this purpose, we consider an augmented reality application based on the MQTT protocol and conduct a set of experiments over a real fog computing testbed. Results show how migrating the fog service gives some benefits on the experienced QoS with respect to a case where no migration is performed
Rapid Prototyping of IoT Solutions: A Developer's Perspective
No full textMany new Internet-of-things (IoT) devices and solutions appear in the market every day. Although commercial IoT products are the majority, Do-It-Yourself (DIY) solutions implemented by independent developers still represent a significant driving force. In this scenario, the availability of development tools for both less experienced developers and professionals to reduce the time to create prototypes is crucial. In this paper, we first review the tools available to implement all the components of a typical IoT architecture in different programming languages, then, we analyze how Python can be used to implement all the components of a typical IoT architecture. As a practical example, we illustrate the implementation of a smart home system built exploiting low-cost off-the-shelf hardware and programmed only through Python
Joint Device Association and Resource Allocation for Time-critical IoT Applications in MEC-empowered 5G Networks
No full textMulti-access edge computing (MEC) is emerging as an effective solution to fulfil the requirements of time-critical Internet of Things (IoT) applications. However, to improve the network efficiency and QoS support of MEC systems, it is essential to jointly optimise edge resource management, IoT data collection and IoT device association in the context of heterogeneous services and edge resources. In this study, we address these challenges by formulating the resource allocation, device association and data routing problem in a multi-cell MEC network as a mixed-integer non-linear programming problem. We also propose a best-fit greedy heuristic method to determine an approximate solution to the optimisation problem for online resource management. Simulation results confirm the effectiveness of the proposed algorithm compared to three alternative benchmarks
Application-aware resource allocation and data management for MEC-assisted IoT service providers
Full text linkTo support the growing demand for data-intensive and low-latency IoT applications, Multi-Access Edge Computing (MEC) is emerging as an effective edge-computing approach enabling the execution of delay-sensitive processing tasks close to end-users. However, most of the existing works on resource allocation and service placement in MEC systems overlook the unique characteristics of new IoT use cases. For instance, many IoT applications require the periodic execution of computing tasks on real-time data streams that originate from devices dispersed over a wide area. Thus, users requesting IoT services are typically distant from the data producers. To fill this gap, the contribution of this work is two-fold. Firstly, we propose a MEC-compliant architectural solution to support the operation of multiple IoT service providers over a common MEC platform deployment, which enables the steering and shaping of IoT data transport within the platform. Secondly, we model the problem of service placement and data management in the proposed MEC-based solution taking into account the dependencies at the data level between IoT services and sensing resources. Our model also considers that caches can be deployed on MEC hosts, to allow the sharing of the same data between different IoT services with overlapping geographical scope, and provides support for IoT services with heterogeneous QoS requirements, such as different frequencies of periodic task execution. Due to the complexity of the optimisation problem, a heuristic algorithm is proposed using linear relaxation and rounding techniques. Extensive simulation results demonstrate the efficiency of the proposed approach, especially when traffic demands generated by the service requests are not uniform
Extending ETSI MEC Towards Stateful Application Relocation Based on Container Migration
No full textEdge computing allows to run microservices in close proximity to end user devices. This proximity lets edge computing support emerging 5G application scenarios that need low latency and high bandwidth (e.g., augmented reality, autonomous vehicles). Given its interest, edge computing is fastly gaining momentum and is currently being standardised by the European Telecommunications Standards Institute (ETSI) as Multi-Access Edge Computing (MEC). Notwithstanding its strengths, edge computing is significantly challenged by device mobility, as this can reduce proximity to the edge microservice, putting edge
computing benefits at risk. A way to solve this problem is to migrate the edge microservice across edge servers, to let it follow the application component running on the mobile device. Besides, if the microservice is stateful (i.e., it maintains a state associated to the user), its state needs to be migrated as well. Within ETSI MEC, this concept is expressed as stateful application relocation. The standard identifies three different high-level ways to transfer the application state. However, all of them assume that it is up to the application to actually relocate the state. In this work, we assume that applications at the edge run as containers, and we extend ETSI MEC to let it support stateful application relocation
by leveraging container migration techniques. This approach allows to transfer the application state in a transparent way to the application itself. We implemented our solution and tested it over a small-scale edge computing testbed to extract initial results
Discriminating Quantum States in the Presence of a Deutschian CTC: A Simulation Analysis
No full textIn an article published in 2009, Brun et al. proved that in the presence of a 'Deutschian' closed timelike curve, one can map K distinct nonorthogonal states (hereafter, input set) to the standard orthonormal basis of a K-dimensional state space. To implement this result, the authors proposed a quantum circuit that includes, among SWAP gates, a fixed set of controlled operators (boxes) and an algorithm for determining the unitary transformations carried out by such boxes. To our knowledge, what is still missing to complete the picture is an analysis evaluating the performance of the aforementioned circuit from an engineering perspective. The objective of this article is, therefore, to address this gap through an in-depth simulation analysis, which exploits the approach proposed by Brun et al. in 2017. This approach relies on multiple copies of an input state, multiple iterations of the circuit until a fixed point is (almost) reached. The performance analysis led us to a number of findings. First, the number of iterations is significantly high even if the number of states to be discriminated against is small, such as 2 or 3. Second, we envision that such a number may be shortened as there is plenty of room to improve the unitary transformation acting in the aforementioned controlled boxes. Third, we also revealed a relationship between the number of iterations required to get close to the fixed point and the Chernoff limit of the input set used: the higher the Chernoff bound, the smaller the number of iterations. A comparison, although partial, with another quantum circuit discriminating the nonorthogonal states, proposed by Nareddula et al. in 2018, is carried out and differences are highlighted
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