Multi-dimensional Payment Plan in Fog Computing with Moral Hazard

As the most notably emerging wave of Internet deployments, Internet of Things (IoTs) requires mobility support, location awareness and low latency. Fog Computing, also termed edge computing, is a promising solution for IoTs by extending the Cloud Computing paradigm to the edge of Internet. But how to locate fog nodes' sites and determine the scale of each fog node is a main challenge of Fog Computing systems, especially for time sensitive Fog Computing systems. In this paper, we try to deal with this problem by proposing an improved Fast Search and Find of Density Peaks-based fog node location strategy to locate the fog nodes' sites and determine the resources for each located fog node. To this end, we firstly formulate the fog node location of Fog Computing systems as a clustering problem with multi-constraints. Then we propose an improved Fast Search and Find of Density Peaks-based fog node location algorithm, which introduces the time sensitive feature of IoT applications and improves the Fast Search and Find of Density Peaks clustering algorithm to make this clustering algorithm more robustness and adaptability. The experiment results show that our fog node location strategy not also can avoid the NP-hard problem of the traditional server placement strategies, but also has low time complexity.

Deep neural networks (DNNs) are the key techniques to enable edge/fog intelligence. By far, it remains challenging to conduct distributed deployment of DNN models onto resource-constrained fog nodes with low latency. Existing solutions adopt either model compression techniques to reduce the computation loads on fog nodes, or horizontal model partition techniques, which exploit particular communication and computation patterns to partition different layers of DNNs onto fog nodes. Nonetheless, sometimes even resource demands of particular layers can be unaffordable to fog nodes, which makes horizontal partition inadequate and calls for the joint design of vertical and horizontal model partition. Besides, model partition and compression may lead to degraded inference accuracy, but approaches to compensate such accuracy loss remain unexplored.In this paper, we propose an integrated efficient distributed deep learning (EDDL) framework to address the above challenges. Particularly, we adopt balanced incomplete block design (BIBD) methods to reduce computation loads on fog nodes by removing some data flows in DNNs in a systematic and structured manner. By leveraging grouped convolution techniques, we propose a practical scheme to conduct horizontal and vertical model partition jointly. Moreover, we integrate multi-task learning and ensemble learning techniques to further improve the inference accuracy. Simulation results verify the effectiveness of EDDL framework in achieving notable reduction in computation load and memory footprint with mild loss of inference accuracy.

Future Internet of Things (IoT) networks enabled with fog computing is promising to achieve lower processing delay and lighter link burden, by effectively offloading the computing tasks of the terminal nodes (TNs) to nearby fog nodes (FNs) at the network edge. Existing researches for the energy consumption in fog-enabled networks mostly focused on the minimization of the overall energy consumed by the task offloading services. However, fair offloading among multiple FNs while maintaining a satisfactory energy efficiency is of great significance for the sustainability of the fog-enabled IoT networks, especially in the scenarios with battery-powered FNs. In this paper, we propose a fair and energy-minimized task offloading (FEMTO) algorithm based on a fairness scheduling metric, taking three important characteristics into consideration, which include the task offloading energy consumption, the FN's historical average energy and the FN priority. The analytical results of the optimal target FN, the optimal TN transmission power, and the optimal subtask size are obtained in a fair and energy-minimized manner. Extensive simulations are carried out for the heterogeneous fog-enabled IoT network, and the numerical results indicate that the proposed FEMTO algorithm effectively determines the FN feasibility and the minimum energy consumption for the task offloading services. Moreover, a high and robust fairness level for the FNs' energy consumptions is obtained by the proposed FEMTO algorithm.

The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.

With the rapid growth of terminal equipments, the data traffic in the network has grown exponentially. In order to relieve the pressure of cloud computing on link delay, congestion and energy consumption, the promising fog computing is proposed. The fog network consists of several fog clusters. We consider a fog cluster in which a fog controller (FC) aims to schedule the idle fog nodes (FNs) to serve the task node (TN) while guaranteeing the quality of service (QoS) requirements of the TN. We design an ascending-bid auction mechanism to achieve this goal. In this mechanism, the FC is the auctioneer with the reward prices as its strategy and the FNs play the role of bidders with the task sizes as their strategies. The FC uses the bid prices to motivate the FNs to process more data for the TN. The utility function of FNs is proposed, considering the payment from the FC, the cost of task computational delay and energy consumption. The FNs determine the data sizes to be processed by maximizing their utilities. Numerical simulations indicate the satisfactory performance and verify the theoretical analysis, thereby our proposed mechanism results in a win-win solution under the condition of meeting the QoS.

Fog node discovery and selection: A Systematic literature review

Unmanned aerial vehicles (UAVs) are considered a promising solution for carrying communications and computational facilities to increase the flexibility of cloud-to-thing continuum, where short-range and long-range wireless links are adopted to connect mobile devices to the fog node and the fog node to the remote data center, respectively. Most existing UAV-involved resource allocation algorithms focus mainly on the radio resource allocation problem, and much less attention has been paid to the allocation of computational resources. Moreover, the dynamic arrival of tasks and the queueing delay at each computation entity is usually neglected. In this paper, a joint optimization problem is formulated that takes the weighted sum of energy consumption and delay experienced by tasks as the objective function. Processing frequencies and transmission powers of mobile devices and the fog node are the decision variables in the problem. To solve this problem, three decision-making algorithms are presented. The first one is used to decide the UAV's position. The processing frequency, transmission power, and task assignment results at mobile devices are determined by the second algorithm. The last one is adopted by the fog node to optimize its processing frequency and transmission power. A series of simulation experiments are conducted to evaluate the effectiveness of the proposed algorithms. Compared with the random task assignment scheme with fixed parameters, the combination of our three algorithms always perform much better for a wide range of parameter settings.

Recent advances in the areas of Internet of Things (IoT), big data, and machine learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as smart devices prevail more in our daily life. Ensuring quality of service (QoS) for delay-sensitive applications is a must, and fog computing is seen as one of the primary enablers for satisfying such tight QoS requirements, as it puts compute, storage, and networking resources closer to the user. In this paper, we first introduce FOGPLAN, a framework for QoS-aware dynamic fog service provisioning (QDFSP). QDFSP concerns the dynamic deployment of application services on fog nodes, or the release of application services that have previously been deployed on fog nodes, in order to meet low latency and QoS requirements of applications while minimizing cost. FOGPLAN framework is practical and operates with no assumptions and minimal information about IoT nodes. Next, we present a possible formulation (as an optimization problem) and two efficient greedy algorithms for addressing the QDFSP at one instance of time. Finally, the FOGPLAN framework is evaluated using a simulation based on real-world traffic traces.

Computation offloading over fog computing has the potential to improve reliability and reduce latency in future networks. This paper considers a scenario where roadside units (RSUs) are installed for offloading tasks to the computation nodes including nearby fog nodes and a cloud center. To guarantee the reliable communication, we formulate the first subproblem of power allocation, and leverage the conditional value-at-risk approach to analyze the successful transmission probability in the worse-case channel condition. To complete computation tasks with low latency, we formulate the second subproblem of task allocation into a multi-period generalized assignment problem (MPGAP), which aims at minimizing the total delay by offloading tasks to the ‘right’ fog nodes at ‘right’ period. Then, we propose a modified branch-and-bound algorithm to derive the optimal solution and a heuristic greedy algorithm to obtain approximate performance. In addition, the master problem is proposed as a non-convex optimization problem, which considers both the reliability-guaranteed and delay-sensitive requirements. We design the Lagreedy algorithm by combining the subgradient algorithm and the heuristic algorithm. Comprehensive evaluations demonstrate that the Lagreedy is able to obtain the shortest delay with a high power consumption, while the branch-and-bound algorithm can achieve both shorter delay and lower power consumption with reliability guarantees.

Fog computing provides distributed processing and storage solutions for real-time applications that demand low latency and fast response times. This makes fog solutions suitable for internet-of-things (IoT) devices that request various network functions. To offer multiple functions for IoT applications, fog nodes can leverage network function virtualization (NFV) for a scalable and elastic function modification, i.e., without the need for dedicated hardware. Despite the saliencies achieved from the synergistic combination between fog and NFV technologies, a key challenge here is the limited resources at the fog nodes. This makes the latter susceptible to rapid node saturation and network congestion at high traffic volumes. Along this, efficient resource utilization and load distribution mechanisms are necessary to enhance admission rates and quality-of-service (QoS). Hence, this paper proposes novel load migration schemes for NFV-based fog networks that aim to reduce the overhead of the migration process. Namely, a coarse-grained load diffusion scheme is adopted to reduce migration frequencies, incurred delay, and cost. Further, destination nodes are selected based on least-load (LL) or least-delay (LD) mechanisms to accommodate delay-sensitive, delay-tolerant, and computation-intensive IoT applications.

Joint failure recovery, fault prevention, and energy-efficient resource management for real-time SFC in fog-supported SDN

The Internet of Things (IoT) has boosted fog computing, which complements the cloud. This is critical for applications that need close user proximity. Efficient allocation of IoT applications to the fog, as well as fog device scheduling, enabling the realistic execution of IoT application deployment in the fog environment. The scheduling difficulties are multi-objective in nature, since they must handle the issues of avoiding resource waste, network latency, and maximising Quality of Service (QoS) on fog nodes. In this research, the Hybrid Multi-Objective Marine Predators Algorithm-based Clustering and Fog Picker (HMMPACFP) Technique is developed as a combinatorial model for tackling the problem of fog node allocation, with the goal of achieving dynamic scheduling using lightweight characteristics. Utilised Fog Picker to allocate IoT components to fog nodes based on QoS parameters. Simulation trials of the proposed HMMPACFP scheme utilising iMetal and iFogSim with Hypervolume (HV) and Generational Distance (IGD) demonstrated its superiority over the benchmarked methodologies utilised for evaluation. The combination of Fog Picker with the suggested HMMPACFP scheme resulted in 32.18% faster convergence, 26.92% more solution variety, and a better balance between exploration and exploitation rates.

Mobile Crowd-Sensing and Fog Computing are fundamental Internet of Things technologies tailored for smart cities. The former enables user’s devices to collect and share data in urban environments. The latter shifts the computation close to end users, lightening the work that their devices have to perform to communicate sensed data in the Cloud. In a fog-based MCS campaign a large number of devices with heterogeneous resources executes sensing tasks generally distributed by remote servers. A careful selection of some of these users’ devices for sensing operations can bring benefits to the whole platform in terms of computational costs and energy saving. In this paper, we propose a novel users’ recruitment model based on distance, computational capacity, and residual battery of devices. The selection process is carried out in a scenario where devices of the MCS campaign periodically share their battery and Central Processing Unit status to fog nodes through their short-range communication interfaces. Based on this information, fog nodes select devices suitable for performing specific tasks. To verify the effectiveness of the proposed model, we compare our solution with a selection model based only on distances, using an MCS simulator suitably modified for fog-based scenarios as testbed. Results show that our model is able to achieve a more accurate task resolution and a more effective recruitment selection, detecting those devices that can perform sensing operations better than others, thus, guaranteeing an overall average saving of computational and energy resources.

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

Environmental Uncertainty and IT Governance: A Moral Hazard Perspective