Near real-time optimization of fog service placement for responsive edge computing

In recent years, computing workloads have shifted from the cloud to the fog, and IoT devices are becoming powerful enough to run containerized services. While the combination of IoT devices and fog computing has many advantages, such as increased efficiency, reduced network traffic and better end user experience, the scale and volatility of the fog and edge also present new problems for service deployment scheduling.Fog and edge networks contain orders of magnitude more devices than cloud data centers, and they are often less stable and slower. Additionally, frequent changes in network topology and the number of connected devices are the norm in edge networks, rather than the exception as in cloud data centers.This article presents a service scheduling algorithm, labeled "Swirly", for fog and edge networks containing hundreds of thousands of devices, which is capable of incorporating changes in network conditions and connected devices. The theoretical performance is explored, and a model of the behaviour and limits of fog nodes is constructed. An evaluation of Swirly is performed, showing that it is capable of managing service meshes for at least 300.000 devices in near real-time

Addressing the Node Discovery Problem in Fog Computing

In recent years, the Internet of Things (IoT) has gained a lot of attention due to connecting various sensor devices with the cloud, in order to enable smart applications such as: smart traffic management, smart houses, and smart grids, among others. Due to the growing popularity of the IoT, the number of Internet-connected devices has increased significantly. As a result, these devices generate a huge amount of network traffic which may lead to bottlenecks, and eventually increase the communication latency with the cloud. To cope with such issues, a new computing paradigm has emerged, namely: fog computing. Fog computing enables computing that spans from the cloud to the edge of the network in order to distribute the computations of the IoT data, and to reduce the communication latency. However, fog computing is still in its infancy, and there are still related open problems. In this paper, we focus on the node discovery problem, i.e., how to add new compute nodes to a fog computing system. Moreover, we discuss how addressing this problem can have a positive impact on various aspects of fog computing, such as fault tolerance, resource heterogeneity, proximity awareness, and scalability. Finally, based on the experimental results that we produce by simulating various distributed compute nodes, we show how addressing the node discovery problem can improve the fault tolerance of a fog computing system

Self-organizing fog support services for responsive edge computing

Recent years have seen fog and edge computing emerge as new paradigms to provide more responsive software services. While both these concepts have numerous advantages in terms of efficiency and user experience by moving computational tasks closer to where they are needed, effective service scheduling requires a different approach in the geographically widespread fog than it does in the cloud. Additionally, fog and edge networks are volatile, and of such a scale that gathering all the required data for a centralized scheduler results in prohibitively high memory use and network traffic. Since the fog is a geographically distributed computational substrate, a suitable solution is to use a decentralized service scheduler, deployed on all nodes, which can monitor and deploy services in its neighbourhood without having to know the entire service topology. This article presents a fully decentralized service scheduler, labeled "SoSwirly", for fog and edge networks containing hundreds of thousands of devices. It scales service instances as required by the edge, based on available resources and flexibly defined distance metrics. A mathematical model of fog networks is presented, along with a theoretical analysis and an empirical evaluation which indicate that under the right conditions, SoSwirly is highly scalable. It is also explained how to achieve these conditions by carefully selecting configuration parameters. Concretely, only 15 MiB of memory is required on each node, and network traffic in the evaluations is less than 4 Kbps on edge nodes, while 4-6% more service instances are created than by a centralized algorithm

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

Edge Offloading in Smart Grid

The energy transition supports the shift towards more sustainable energy alternatives, paving towards decentralized smart grids, where the energy is generated closer to the point of use. The decentralized smart grids foresee novel data-driven low latency applications for improving resilience and responsiveness, such as peer-to-peer energy trading, microgrid control, fault detection, or demand response. However, the traditional cloud-based smart grid architectures are unable to meet the requirements of the new emerging applications such as low latency and high-reliability thus alternative architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge offloading can play a pivotal role for the next-generation smart grid AI applications because it enables the efficient utilization of computing resources and addresses the challenges of increasing data generated by IoT devices, optimizing the response time, energy consumption, and network performance. However, a comprehensive overview of the current state of research is needed to support sound decisions regarding energy-related applications offloading from cloud to fog or edge, focusing on smart grid open challenges and potential impacts. In this paper, we delve into smart grid and computational distribution architec-tures, including edge-fog-cloud models, orchestration architecture, and serverless computing, and analyze the decision-making variables and optimization algorithms to assess the efficiency of edge offloading. Finally, the work contributes to a comprehensive understanding of the edge offloading in smart grid, providing a SWOT analysis to support decision making.Comment: to be submitted to journa

Energy-aware and adaptive fog storage mechanism with data replication ruled by spatio-temporal content popularity

Data traffic demand increases at a very fast pace in edge networking environments, with strict requisites on latency and throughput. To fulfil these requirements, among others, this paper proposes a fog storage system that incorporates mobile nodes as content providers. This fog storage system has a hybrid design because it does not only bring data closer to edge consumers but, as a novelty, it also incorporates in the system other relevant functional aspects. These novel aspects are the user data demand, the energy consumption, and the node distance. In this way, the decision whether to replicate data is based on an original edge service managed by an adaptive distance metric for node clustering. The adaptive distance is evaluated from several important system parameters like, distance from consumer to the data storage location, spatio-temporal data popularity, and the autonomy of each battery-powered node. Testbed results evidence that this flexible cluster-based proposal offers a more responsive data access to consumers, reduces core traffic, and depletes in a fair way the available battery energy of edge nodes.info:eu-repo/semantics/acceptedVersio