Demanding latency-sensitive IoT applications have stringent requirements, such as low latency, better privacy, and security. To meet such requirements, researchers proposed a new paradigm, i.e., edge computing. Edge computing consists of distributed computational resources and enables the execution of IoT applications closer to the edge of the network. However, the distributed nature of this paradigm makes the application deployment and development process more challenging since the developer must divide the application’s functionality into multiple parts, assigning for each a set of requirements. As a result, the developer must: 1) define the application’s requirements and validate them at design time and 2) find a deployment strategy on the target edge computing platform. In this article, we propose EdgeFlow, a new IoT framework capable of assisting the developer in the application development process. Specifically, we introduce a methodology for latency-sensitive IoT applications development and deployment, consisting of three different stages, i.e., the development, validation, and deployment. To this end, we propose an extension of the flow-based programming paradigm with new timing requirements and provide a resource allocation technique to assist with the deployment and validation of latency-sensitive IoT applications. Finally, we evaluate EdgeFlow by: 1) presenting the application development methodology and 2) performing a quantitative evaluation demonstrating our resource allocation technique’s capabilities to find feasible and optimal deployment strategies. The experimental results illustrate the effectiveness of our methodology to assist the developer throughout the entire application development process.

Edge computing offers the possibility of deploying applications at the edge of the network. To take advantage of available devices’ distributed resources, applications often are structured as microservices, often having stringent requirements of low latency and high availability. However, a decentralized edge system that the application may be intended for is characterized by high volatility, due to devices making up the system being unreliable or leaving the network unexpectedly. This makes application deployment and assurance that it will continue to operate under volatility challenging. We propose an adaptive framework capable of deploying and efficiently maintaining a microservice-based application at runtime, by tackling two intertwined problems: (i) finding a microservice placement across device hosts and (ii) deriving invocation paths that serve it. Our objective is to maintain correct functionality by satisfying given requirements in terms of end-to-end latency and availability, in a volatile edge environment. We evaluate our solution quantitatively by considering performance and failure recovery.

Satisfying the software requirements of emerging service-based Internet of Things (IoT) applications has become challenging for cloud-centric architectures, as applications demand fast response times and availability of computational resources closer to end-users. Meeting application demands must occur at runtime, facing uncertainty and in a decentralized manner, something that must be reflected in system deployment. We propose a decentralized resource management technique and accompanying technical framework for the deployment of service-based IoT applications at the edge. Faithful to services engineering, applications we consider are composed of interdependent tasks, which in the IoT setting may be concretized as containerized microservices or serverless functions. A deployment for an arbitrary application is found at runtime through satisfiability; the mapping produced is compliant with tasks’ individual resource requirements and latency constraints by construction. Our approach ensures seamless deployment at runtime, assuming no design-time knowledge of device resources or the current network topology. We evaluate the applicability and realizability of our technique over single-board computers as edge devices, particularly in the absence of cloud resources.

Converging Information Technology (IT) and Operations Technology (OT) in modern factories remains a challenging task. Several approaches such as Cloud, Fog or Edge computing aim to provide possible solutions for bridging OT that requires strict real-time processing with IT that targets computing functionality. In this context, this paper contributes to ongoing Fog computing research by presenting three industrial use cases with a specific focus on consolidation of functionality. Each use case exemplifies scenarios on how to use the computational resources closer to the edge of the network provided by a Fog Computing Platform (FCP). All use-cases utilize the same proposed FCP, which allows drawing a set of requirements on future FCPs, e.g. hardware, virtualization, security, communication and resource management. The central element of the FCP is the Fog Node (FN), built upon commercial off-the-shelf (COTS) multicore processors (MCPs) and virtualization support. Resource management tools, advanced security features and state of the art communication protocols complete the FCP. The paper concludes by outlining future research challenges by comparing the proposed FCP with the identified requirements.

Demanding IoT application requirements such as high dependability and low latency, cannot be satisfied by centralized cloud computing when deploying these applications. Edge computing is emerging as an alternative to deploy demanding IoT applications closer to the edge of the network. However, with edge computing, available resources are distributed among different resource-constrained devices, which cannot host large monolithic applications. We propose a new hierarchical IoT application model, suitable for the distributed nature of edge computing. Thus, a task in the application is modeled using multiple configurations of smaller tasks, each with their own functionality level and resource requirements. For deployment we use a decentralized resource technical framework that finds a satisfiable task mapping on edge devices. Its functionality is inspired by an auction house, having the objectives of (i) deploying an application such that its requirements are met and (ii) empowers edge devices to be in control of their available resources. For the latter, we propose a new decision policy to help edge devices take better decisions regarding the use of local available resource. Our solution enables efficient device resource utilization when deploying IoT applications at the edge.

Edge computing has been recently introduced as an intermediary between Internet of Things (IoT) deployments and the cloud, providing data or control facilities to participating IoT devices. This includes actively supporting IoT resource discovery, something particularly pertinent when building large-scale, distributed and heterogeneous IoT systems. Moreover, edge devices supporting resource discovery are required to meet the stringent requirements prevalent in IoT systems including high availability, low-latency, and privacy. To this end, we present a resource discovery platform for IoT resources situated at the edge of the network. Our approach aims at providing a seamless discovery process that is able to (i) extend the covered area by deploying additional edge nodes and (ii) assist in the development of new IoT applications that target already available resources. Within our proposed platform, devices located in a certain proximity connect and form an edge-to-edge network that we call an edge neighborhood - our edge-to-edge metadata replication platform enables participating devices to discover available resources. Our solution is characterized by absence of centralization, as edge nodes exchange metadata about available resources within their scope in a peer-to-peer manner.

The automotive electronic architectures have moved from federated architectures, where one function is implemented in one ECU (Electronic Control Unit), to distributed architectures, consisting of several multicore ECUs. In addition, multicore ECUs are being adopted because of better performance, cost, size, fault-tolerance and power consumption. Automotive manufacturers use AUTomotive Open System ARchitecture (AUTOSAR) as the standardized software architecture for ECUs. With AUTOSAR, the functionality is modeled as a set of software components composed of subtasks, called runnables. In this paper we propose an approach for the automatic software functionality assignment to multicore distributed architectures, implemented as a software tool. The AUTOMAP, decides: the (i) mapping of software components to multicore ECUs, (ii) the assignment of runnables to the ECU cores, (iii) the clustering of runnables into tasks and (iv) the mapping of tasks to 'OS-Applications', such that timing and mapping constraints are satisfied. AUTOMAP has been developed to handle large industrialsized use cases, fine-grained realistic mapping and timing constraints, and to produce outputs that support the system engineer in the mapping task. We have successfully evaluated AUTOMAP on several realistic use cases from Volvo Trucks.