Fog Cloud Research Articles

An Intelligent Task Scheduling Model for Hybrid Internet of Things and Cloud Environment for Big Data Applications

One of the most significant issues in Internet of Things (IoT) cloud computing is scheduling tasks. Recent developments in IoT-based technologies have led to a meteoric rise in the demand for cloud storage. In order to load the IoT services onto cloud resources efficiently even while satisfying the requirements of the applications, sophisticated planning methodologies are required. This is important because several processes must be well prepared on different virtual machines to maximize resource usage and minimize waiting times. Different IoT application tasks can be difficult to schedule in a cloud-based computing architecture due to the heterogeneous features of IoT. With the rise in IoT sensors and the need to access information quickly and reliably, fog cloud computing is proposed for the integration of fog and cloud networks to meet these demands. One of the most important necessities in a fog cloud setting is efficient task scheduling, as this can help to lessen the time it takes for data to be processed and improve QoS (quality of service). The overall processing time of IoT programs should be kept as short as possible by effectively planning and managing their workloads, taking into account limitations such as task scheduling. Finding the ideal approach is challenging, especially for big data systems, because task scheduling is a complex issue. This research provides a Deep Learning Algorithm for Big data Task Scheduling System (DLA-BDTSS) for the Internet of Things (IoT) and cloud computing applications. When it comes to reducing energy costs and end-to-end delay, an optimized scheduling model based on deep learning is used to analyze and process various tasks. The method employs a multi-objective strategy to shorten the makespan and maximize resource consumption. A regional exploration search technique improves the optimization algorithm’s capacity to exploit data and avoid becoming stuck in local optimization. DLA-BDTSS was compared to other well-known task allocation methods in accurate trace information and the CloudSim tools. The investigation showed that DLA-BDTSS performed better than other well-known algorithms. It converged faster than different approaches, making it beneficial for big data task scheduling scenarios, and it obtained an 8.43 percent improvement in the outcomes. DLA-BDTSS obtained an 8.43% improvement in the outcomes with an execution time of 34 s and fitness value evaluation of 76.8%.

Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy

Abstract. Cloud radars are capable of providing continuous high-resolution observations of clouds and now offer new capabilities within fog layers thanks to the development of frequency-modulated continuous-wave 95 GHz cloud radars. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z=a⋅LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z–LWC relation for a continuous cloud system is complicated. Additional information such as liquid water path (LWP) from a co-located microwave radiometer (MWR) is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of fog and warm clouds using 95 GHz cloud radar–microwave radiometer synergy in a variational framework is presented. This paper also aims to propose an algorithm configuration that retrieves the LWC of clouds and fog using radar reflectivity and a climatology of the power law parameters. To do so, variations in the scaling factor ln a (the logarithm of pre-factor a from power law relation) when MWR observations are available are allowed in each cloud profile to build a climatology of the scaling factor ln a that can be used when MWR observations are not available. The algorithm also accounts for attenuation due to cloud droplets. In this algorithm formulation, the measure of uncertainty in the observations, the forward model, and the a priori information of desired variables acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information because LWP is point information for the whole cloud column. By further investigating the sensitivity analysis of various biases in LWP information, it was found that it is beneficial to incorporate LWP, even if it is biased, rather than not assimilate any LWP. The algorithm is then implemented in various cloud and fog cases at the SIRTA observatory to estimate LWC and the scaling factor. The scaling factor (ln a) changes for each cloud profile, and the range of ln a is consistent with suggested values in the literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (southwest of France, October 2019 to March 2020), in situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud–fog heterogeneity played a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for the scaling factor are proposed for fog and clouds. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of the scaling factor as a priori information to estimate the LWC of warm clouds. This method allows the LWC estimation using only radar reflectivity and climatology of the scaling factor.

Adaptive secure malware efficient machine learning algorithm for healthcare data

AbstractMalware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT‐based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content‐aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content‐aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre‐ and post‐train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML outperforms machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes.

Guest Editorial Special Issue on Empowering the Future Generation Systems: Opportunities by the Convergence of Cloud, Edge, AI, and IoT

The future generation of the Internet of Things (IoT) systems is characterized by the fusion of technologies—from edge–fog–cloud computing to artificial intelligence (AI) and blockchain—closing the gap between the physical and digital worlds [A1]. Although these technologies have been developed separately over time, the synergy among them has taken a giant leap. We are witnessing a fast-paced convergence of these technologies resulting in a fundamental paradigm shift unlocking vast benefits and opportunities across vertical markets. However, there are still several barriers, such as a lack of consensus toward any reference models or best practices, hindering the full fusion of these technologies [A1]. To tackle these challenges and facilitate this promising transformation, this special issue was organized to provide a holistic multidisciplinary reference for solutions, architectures, protocols, services, and applications addressing all aspects of the future generation of IoT systems via the fusion of edge, cloud, AI, and blockchain, while considering the corresponding challenges. Thanks to the enormous support from the Editor-in-Chief, Prof. Honggang Wang, and the dedicated work of many reviewers, after a rigorous review process, 27 excellent articles out of 125 submissions were accepted for inclusion in this special issue of the IEEE Internet of Things Journal. We introduce these papers and highlight their key contributions below.

Fusion of IoT, AI, Edge–Fog–Cloud, and Blockchain: Challenges, Solutions, and a Case Study in Healthcare and Medicine

The digital transformation is characterized by the convergence of technologies—from the Internet of Things (IoT) to edge–fog–cloud computing, artificial intelligence (AI), and Blockchain—in multiple dimensions, blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next-generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of the existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus toward any reference models or best practices. This article provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this article by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

Locality-aware deployment of application microservices for multi-domain fog computing

In fog computing customers’ microservices may demand access to connected objects, data sources and computing resources outside the domain of their fog provider In practice, the locality of connected objects renders mandatory a multi-domain approach in order to broaden the scope of resources available to a single-domain fog provider. We consider a scenario where assets from other domains can be leased across a federation of cloud–fog infrastructures. In this context, a fog provider aims to minimize the quantity of external resources to be rented to satisfy the applications’ demands while meeting their requirements. We first introduce a general framework for the deployment of applications across multiple domains owned by multiple cloud–fog providers. Hence, the resource allocation problem is formulated in the form of an integer linear program. We provide a novel heuristic method that explores the resource assignment space in a breadth-first fashion to ensure that locality constraints are met. Extensive numerical results evaluate deployment costs and feasibility of the proposed solution demonstrating that it outperforms the standard approaches adopted in the literature.

A Dynamic Adaptive Bio-Inspired Multi-Agent System for Healthcare Task Deployment

The use of the Internet of Things (IoT) in healthcare is increasing significantly, bringing high-quality health services, but it still generates massive data with massive energy consumption. Due to the limited resources of fog servers and their impact on limiting the time needed for health data analysis tasks, the need to handle this problem in a fast way has become a necessity. To address this issue, many optimization and IoT-based approaches have been proposed. In this paper, a dynamic and adaptive healthcare service deployment controller using hybrid bio-inspired multi-agents is proposed. This method offers optimal energy costs and maintains the highest possible performance for fog cloud computing. At first, IGWO (Improved Grey Wolf Optimization) is used to initialize the deployment process using the nearest available fog servers. Then, an efficient energy-saving task deployment was achieved through Particle Swarm Optimization (PSO) to reduce energy consumption, increase rewards across multiple fog servers, and improve task deployment. Finally, to ensure continuous control of underloaded and overloaded servers, the neighborhood multi-agent coordination model is developed to manage healthcare services between the fog servers. The developed approach is implemented in the iFogSim simulator and various evaluation metrics are used to evaluate the effectiveness of the suggested approach. The simulation outcome proved that the suggested technique provides has better performance than other existing approaches.

A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA)

These days, the ratio of cancer diseases among patients has been growing day by day. Recently, many cancer cases have been reported in different clinical hospitals. Many machine learning algorithms have been suggested in the literature to predict cancer diseases with the same class types based on trained and test data. However, there are many research rooms available for further research. In this paper, the studies look into the different types of cancer by analyzing, classifying, and processing the multi-omics dataset in a fog cloud network. Based on SARSA on-policy and multi-omics workload learning, made possible by reinforcement learning, the study made new hybrid cancer detection schemes. It consists of different layers, such as clinical data collection via laboratories and tool processes (biopsy, colonoscopy, and mammography) at the distributed omics-based clinics in the network. The study considers the different cancer classes such as carcinomas, sarcomas, leukemias, and lymphomas with their types in work and processes them using the multi-omics distributed clinics in work. In order to solve the problem, the study presents omics cancer workload reinforcement learning state action reward state action “SARSA” (OCWLS) schemes, which are made up of an on-policy learning scheme on different parameters like states, actions, timestamps, reward, accuracy, and processing time constraints. The goal is to process multiple cancer classes and workload feature matching while reducing the time it takes to process in clinical hospitals that are spread out. Simulation results show that OCWLS is better than other machine learning methods regarding+ processing time, extracting features from multiple classes of cancer, and matching in the system.

A new hybrid encryption in fog–cloud environment for secure medical data-sharing

A new hybrid encryption in fog–cloud environment for secure medical data-sharing

HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments

Cloud computing is a mainstay of modern technology, offering cost-effective and scalable solutions to a variety of different problems. The massive shift of organization resource needs from local systems to cloud-based systems has greatly increased the costs incurred by cloud providers in expanding, maintaining, and supplying server, storage, network, and processing hardware. Due to the large scale at which cloud providers operate, even small performance degradation issues can cause energy or resource usage costs to rise dramatically. One way in which cloud providers may improve cost reduction is by reducing energy consumption. The use of intelligent task-scheduling algorithms to allocate user-deployed jobs to servers can reduce the amount of energy consumed. Conventional task scheduling algorithms involve both heuristic and metaheuristic methods. Recently, the application of Artificial Intelligence (AI) to optimize task scheduling has seen significant progress, including the Gated Graph Convolution Network (GGCN). This paper proposes a new approach called HunterPlus which examine the effect of extending the GGCN’s Gated Recurrent Unit to a Bidirectional Gated Recurrent Unit. The paper also studies the utilization of Convolutional Neural Networks (CNNs) in optimizing cloud–fog task scheduling. Experimental results show that the CNN scheduler outperforms the GGCN-based models in both energy consumption per task and job completion rate metrics by at least 17 and 10.4 percent, respectively.

Dynamic Load Balancing Techniques in the IoT: A Review

The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.

Coordinated Control of Networked Nonlinear Multiagent Systems Using Variable Horizon Learning Predictors via Cloud Edge Computing

Networked multiagent systems use network technology to realize the interconnection, intercommunication, and mutual control of things. The coordinated control problem of networked nonlinear multiagent systems via cloud edge computing is investigated in this article. A mist–fog–cloud predictive control scheme is proposed for the coordinated control of complex large-scale networked multiagent systems by making use of the advantages of cloud edge computing. This scheme actively compensates for communication delays and achieves desired coordination performance of individual agents. Variable horizon learning predictors are presented to predict the outputs of the unknown nonlinear dynamical agents within different horizons. The design of coordinated control optimizes a performance index function presented to measure the coordination between agents. The analysis on a networked nonlinear multiagent system using the mist–fog–cloud predictive control scheme results in the conditions of simultaneous consensus and stability of the entire closed-loop system. An example demonstrates the effectiveness of the proposed scheme.

A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud–Fog Computing

Cloud–fog computing is a large-scale service environment developed to deliver fast, scalable services to clients. The fog nodes of such environments are distributed in diverse places and operate independently by deciding on which data to process locally and which data to send remotely to the cloud for further analysis, in which a Service-Level Agreement (SLA) is employed to govern Quality of Service (QoS) requirements of the cloud provider to such nodes. The provider experiences varying incoming workloads that come from heterogeneous fog and Internet of Things (IoT) devices, each of which submits jobs that entail various service characteristics and QoS requirements. To execute fog workloads and meet their SLA obligations, the provider allocates appropriate resources and utilizes load scheduling strategies that effectively manage the executions of fog jobs on cloud resources. Failing to fulfill such demands causes extra network bottlenecks, service delays, and energy constraints that are difficult to maintain at run-time. This paper proposes a joint energy- and QoS-optimized performance framework that tolerates delay and energy risks on the cost performance of the cloud provider. The framework employs scheduling mechanisms that consider the SLA penalty and energy impacts of data communication, service, and waiting performance metrics on cost reduction. The findings prove the framework’s effectiveness in mitigating energy consumption due to QoS penalties and therefore reducing the gross scheduling cost.

Regional atmospheric circulation patterns driving consecutive fog events in the United Arab Emirates

In this study, the link between the occurrence of consecutive fog days in the United Arab Emirates (UAE) and the associated synoptic-scale circulation is investigated. This is particularly pertinent, as such a link may provide an important predictive skill for a phenomenon that has a pronounced impact on road and air traffic but is still poorly simulated by numerical models. A cluster analysis of all consecutive fog days from January 1983 to December 2021 indicated that the positive phase of the East Atlantic/Western Russia teleconnection pattern, Eastern Pacific La Nina events, and the circumglobal wavenumber 5 pattern promote the occurrence of multiple fog days in the UAE. The fog's radiative impacts, as estimated from satellite data, revealed that the fog in the UAE is more optically thick than that observed elsewhere. A trend analysis over the period 1983 to 2021 revealed that consecutive fog events have become more frequent and longer-lasting but less intense (i.e., associated with higher values of visibility). The fog's spatial extent over the UAE at its mature stage has also decreased over time. An analysis of the trends in the surface and top of atmosphere (TOA) radiative fluxes indicated that over the period 2000–2021, the fog clouds have likely become less reflective, with a statistically significant decrease in the surface downward shortwave and TOA upward longwave radiation fluxes. Long-term measurements of fog microphysics in the region are needed to better understand the variability in the properties of the fog cloud droplets.

CD/CV: Blockchain-based schemes for continuous verifiability and traceability of IoT data for edge–fog–cloud

This paper presents a continuous delivery/continuous verifiability (CD/CV) method for IoT dataflows in edge–fog–cloud. A CD model based on extraction, transformation, and load (ETL) mechanism as well as a directed acyclic graph (DAG) construction, enable end-users to create efficient schemes for the continuous verification and validation of the execution of applications in edge–fog–cloud infrastructures. This scheme also verifies and validates established execution sequences and the integrity of digital assets. CV model converts ETL and DAG into business model, smart contracts in a private blockchain for the automatic and transparent registration of transactions performed by each application in workflows/pipelines created by CD model without altering applications nor edge–fog–cloud workflows. This model ensures that IoT dataflows delivers verifiable information for organizations to conduct critical decision-making processes with certainty. A containerized parallelism model solves portability issues and reduces/compensates the overhead produced by CD/CV operations. We developed and implemented a prototype to create CD/CV schemes, which were evaluated in a case study where user mobility information is used to identify interest points, patterns, and maps. The experimental evaluation revealed the efficiency of CD/CV to register the transactions performed in IoT dataflows through edge–fog–cloud in a private blockchain network in comparison with state-of-art solutions.

Edge-Fog-Cloud Computing Hierarchy for Improving Performance and Security of NB-IoT-Based Health Monitoring Systems.

This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.

Multi-objective fuzzy approach to scheduling and offloading workflow tasks in Fog–Cloud computing

Fog–Cloud computing environments have become attractive focus areas for scheduling workflow automation of different processes in different domains. The planning of workflow containing tasks having different characteristics, in Fog-Cloud computing, can affect the gain in terms of resources. The main objective of this article is to efficiently schedule a workflow in such heterogeneous environments under conflicting constraints between the user and the resource provider. In this paper, we propose a new approach to designing an environment that combines partitioning, sequencing, and scheduling algorithms while guaranteeing a multi-objective optimization of users’ and providers’ conflicting requirements. Our approach also integrates a distributed resolution with Multi-Agent System (MAS), in order to reduce the complexity of the problem, and a fuzzy inference system to deal with the uncertainty problem. To study and confirm the effectiveness of our approach, we conducted an experimental study on different data collections. We compared our approach with the main related approaches that we studied. The analysis of the obtained results clearly shows the limits of these studied approaches and confirms the performance of our approach, as well as its effectiveness in planning workflows on Fog–Cloud computing while taking into account the utilization of resources. In terms of Makespan, our solution recorded a reduction of 48% compared to the Energy Makespan Multi-Objective Optimization (EM-MOO) approach, and 41% compared to the Multi-Agent System based Genetic Algorithm (MAS-GA), while respecting the time constraint.

Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment

Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics.

Fully Homomorphic Enabled Secure Task Offloading and Scheduling System for Transport Applications

With an emerging development in transport technologies, many applications in vehicular environments access roadside unit (RSU) services for tasks offloading. These applications use different types of paid services (e.g., communication, WiFi, and computing nodes) for their execution. The vehicular fog cloud computing (VFCN) is a cooperative computing environment to handle vehicle applications' mobility and resource allocation. However, the aforementioned collaborative VFCN environment still suffers from mobility and security issues in the existing systems. To address the aforementioned issues, in this work we present a cost-efficient and secure VFCN which consists of a mobility-aware multi-scenario offloading phase (MAMSOP) to deal with the mobility and offloading costs. The objective of the work is to execute applications with minimum delays and costs in a secure way. For the security concern, we propose a security scheme based on fully-homomorphism encryption, which encrypts and decrypts data locally and involves computation on encrypted data rather than decrypting it into its original form. As most of the applications have deadline and mobility constraints, so we propose the fully polynomial-time approximation scheme (FPTAS) based search task scheduling (STS) to ensure the execution of all applications within their given constraints. The goal of STS-FPTAS is to determine the delay and cost-aware scheduling into knapsack-aware resources. The knapsack allows all tasks to be allocated to the available length of resources. The results show that the proposed work optimizes the costs by 40% compared to existing systems.

Optimal cross-layer resource allocation in fog computing: A market-based framework

The potential upsides of fog computing are undeniable, including better near-real-time response, reduced transmission costs and analysis for IoT, which will further have a significant impact on operation of factories, enterprises, organizations and even the quality of our life. However, due to the resource-constrained nature, heterogeneity and distributed nodes, how to manage the extraordinarily complex fog computing resources is necessary to improve the QoE of users. This paper investigates the rational resource allocation for fog computing from the cross-layer point of perspective, and formulates the resource allocation scenario as Cloud–Fog–User market which is composed of cloud center devices, fog nodes, and user devices. Firstly, the resource market is divided into Cloud–Fog market containing cloud layer and fog layer, and Fog–User market containing fog layer and user layer based on different scenarios separately. The paper proposes a market-based framework for fog computing networks, which enables the cloud layer and the fog layer to allocate their resources in the form of pricing, payment and supply–demand relationship, while analyzes the relationship among resource allocation participants in the market. Then the paper investigates the utility optimization models from the viewpoints of both the participants of respective markets, so as to optimize optimal payment and optimal resource allocation via convex optimization techniques, and proposes a gradient-based iterative algorithm to optimize the utilities, respectively. Finally, experimental results have displayed the performance of convex analysis results and effectiveness of the proposed algorithm.