With the rise of the Internet of Things (IoT), fog computing has emerged to help traditional cloud computing in meeting scalability demands. Fog computing makes it possible to fulfill real-time requirements of applications by bringing more processing, storage, and control power geographically closer to edge devices. However, since fog computing is a relatively new field, there is no standard platform for research and development in a realistic environment, and this dramatically inhibits innovation and development of applications suitable for the fog. In response to these challenges, we propose the FDK: A Fog Development Kit for software-defined edge-fog systems. By providing high-level interfaces for allocating computing and networking resources, the FDK abstracts the complexities of fog computing from developers and enables rapid development of edge-fog systems. Also, the FDK supports the utilization of virtualized devices to create a highly realistic emulation environment, allowing fog application prototypes to be built with zero additional costs and enabling portability to a physical infrastructure. We evaluate the resource allocation performance of the FDK using a testbed, including eight edge devices, four fog nodes, and five OpenFlow switches. Our evaluations show that the delay of resource allocation and deallocation is less than 279ms and 256ms for 95 of edge-fog transactions, respectively. Besides, we demonstrate that resource allocations are appropriately enforced and guaranteed, even amidst extreme network congestion.
Resource allocation is key to the success of edge-fog systems, and many fog architectures involving automated resource allocation mechanisms have been proposed. Skarlat @cite_20 created a resource provisioning system for IoT services in fog networks using a fog-cloud middleware component. The middleware oversees the activity of fog colonies, which are micro data centers consisting of fog cells where tasks and data can be distributed and shared among the cells. This system merely manages fog computing resources, and it does not those resources, nor does it perform any allocation of network resources.
@cite_20
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Fog computing provides a decentralized approach to data processing and resource provisioning in the Internet of Things (IoT). Particular challenges of adopting fog-based computational resources are the adherence to geographical distribution of IoT data sources, the delay sensitivity of IoT services, and the potentially very large amounts of data emitted and consumed by IoT devices. Despite existing foundations, research on fog computing is still at its very beginning. A major research question is how to exploit the ubiquitous presence of small and cheap computing devices at the edge of the network in order to successfully execute IoT services. Therefore, in this paper, we study the placement of IoT services on fog resources, taking into account their QoS requirements. We show that our optimization model prevents QoS violations and leads to 35 less cost of execution if compared to a purely cloud-based approach. Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users’ QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price. The advent of the Internet of Things (IoT) leadsto the pervasion of business and private spaces with ubiquitous, networked computing devices. These devices do not simply actas sensors, but feature computational, storage, and networkingresources. These resources are close to the edge of the network, and it is a promising approach to exploit them in order to executeIoT services. This concept is known as fog computing.Despite existing theoretical foundations, the adoption of fogcomputing is still at its very beginning. Especially, there is alack of approaches for the leasing and releasing of resources. Toresolve this shortcoming, we present a conceptual framework forfog resource provisioning. We formalize an optimization problemwhich is able to take into account existing resources in fog IoTlandscapes. The goal of this optimization problem is to providedelay-sensitive utilization of available fog-based computationalresources. We evaluate the resource provisioning model to showthe benefits of our contributions. Our results show a decrease indelays of up to 39 compared to a baseline approach, yieldingshorter round-trip times and makespans. Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.