Fog-cloud Environment Research Articles

An IoT-based bag-of-tasks scheduling framework for deadline-sensitive applications in fog-cloud environment

An IoT-based bag-of-tasks scheduling framework for deadline-sensitive applications in fog-cloud environment

An efficient deep reinforcement learning based task scheduler in cloud-fog environment

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.

Location-aware job scheduling for IoT systems using cloud and fog

The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An "intelligent gateway" based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.

A multi-objective approach for optimizing IoT applications offloading in fog–cloud environments with NSGA-II

A multi-objective approach for optimizing IoT applications offloading in fog–cloud environments with NSGA-II

Efficient Pareto based approach for IoT task offloading on Fog–Cloud environments

In recent times, a new paradigm has emerged in the field of Cloud computing, namely Fog computing. This paradigm has proven to be highly useful in a wide range of domains where both delay and cost were important metrics. Notably, the Internet of Things (IoT) strongly benefits from this, as small devices can gain access to strong computation power quickly and at a low cost. To achieve this, task offloading is used to decide which task should be executed on which node. The development of an efficient algorithm to address this problem could significantly enhance the sustainability of systems in various industrial, agricultural, autonomous vehicle, and other domains. This paper proposes a new variant of the Niche Pareto Genetic Algorithm (NPGA) called Local search Drafting-NPGA (LD-NPGA) to optimize resource allocation in a Cloud/Fog environment, with the objective of minimizing makespan and cost simultaneously. It generates Pareto solutions allowing the user to make choices closer to its intentions. Thus, it addresses various shortcomings identified in the state of the art, including scalability and aggregation formula. A drafting step is implemented to maintain diversity in the population of solutions, resulting in a more varied Pareto set than basic NPGA. LD-NPGA significantly outperforms state-of-the-art metaheuristics in makespan and cost by 15%. Finally, the scalability of our approach and the variety of solutions generated are confirmed in the different experiments.

Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm

Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm.

A truthful double auction framework for security-driven and deadline-aware task offloading in fog-cloud environment

Emerging as an alternative to cloud computing, fog computing is expected to fulfill the resource demand of mobile users and provide low-latency and high-bandwidth services for ever-increasing Internet of Things (IoT) applications. Offloading time-critical and computation-intensive tasks to the fog can significantly reduce response time. Due to dynamicity, heterogeneity, and limited resources, fog may fail to provide services with guaranteed performance. This paper addresses the problem of task offloading and resource allocation in a fog-cloud environment for IoT applications. The proposed Truthful Double Auction Task Offloading (TDATO) framework provides incentives for fog providers to maximize their revenues, execute as many as possible requests with guaranteed performance, and maintain a high reputation. Depending on the user requirements in terms of security level and maximum latency tolerance for task execution, we propose two deadline- and security-related algorithms for winners' determination in the auction: Deadline-Minimum Security (DMS) and Deadline-Preferred Security (DPS). Moreover, two assignment algorithms are analyzed, Reputation-Based (RB) and Random Assignment (RA). The proposed double auction framework assures that all winning users' requirements in terms of deadlines and security level are satisfied while winning providers’ resources are allocated to the users that value them the most. Furthermore, the assignment mechanism aims to maximize resource utilization for fog providers with the highest reputation. We conducted a theoretical analysis to demonstrate that TDATO satisfies truthfulness, individual rationality, and budget balance and has polynomial-time computation complexity. Extensive simulation experiments are performed to evaluate performance.

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision- making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.

Task Scheduling Optimization in Cloud-Fog-MCS Environment Using Genetic Algorithm and Game Theory

Fog-cloud computing is a promising platform for processing Mobile Crowdsensing (MCS) tasks that come with different requirements. A fog environment is more suitable for processing time-sensitive tasks due to its proximity to the MCS layer. On the other hand. the cloud environment provides powerful resources to handle large tasks. However, due to the heterogeneity of the computing nodes, scheduling MCS tasks in a fog-cloud environment is a challenging issue. This paper presents a non-cooperative game theoretical model for the task scheduling problem of MCS tasks in the fog-cloud environment. Then, the paper presents an improved genetic algorithm to efficiently solve the problem of task scheduling game model with main enhancements including a new strategy to generate a diverse initial population, incorporating the utility function of the game theoretical model with system fitness function, and finally, the paper introduces a new strategy for population sorting and grouping with applying adaptive crossover operator to meet the specific needs of each group. This improves the exploration of the unseen regions of the search space, as well as exploiting the already-found promising solutions, ultimately leading to a faster convergence toward the optimal solution. The experimental results demonstrate that the proposed approach has better performance in terms of reducing the makespan by 26%, decreasing the energy consumption by 32.4%, decreasing total system cost by 28%, and decreasing the degree of imbalance by 21.53% as compared with other scheduling approaches such as Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II), Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimization (GWO, Time-Cost Aware Scheduling (TCaS), Moth Flame Optimization (MFO), and Bees Life Algorithm (BLA).

A Novel Offloading Mechanism Leveraging Fuzzy Logic and Deep Reinforcement Learning to Improve IoT Application Performance in a Three-Layer Architecture Within the Fog-Cloud Environment

A Novel Offloading Mechanism Leveraging Fuzzy Logic and Deep Reinforcement Learning to Improve IoT Application Performance in a Three-Layer Architecture Within the Fog-Cloud Environment

Def-DReL: Towards a sustainable serverless functions deployment strategy for fog-cloud environments using deep reinforcement learning

Modern cloud applications are composed of tens of thousands of environment-agnostic serverless functions that can be deployed in either a fog or cloud environment. The key to sustaining fog computing is to offload the maximum amounts of computation to the cloud, and accommodate as many users as possible without compromising quality of service (QoS). However, recent research mainly focuses on assigning maximum resources to serverless applications from the fog node and not taking full advantage of the cloud environment, leading to a lack of sustainability in fog computing. As a way to fill this research gap, we explored what percentage of a user’s request should be handled by fog and cloud. As a result, we proposed Def-DReL, a Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning, by taking into account several real-life parameters, including distance from a nearby fog node and latency, priority of the user, priority of serverless applications, and resource usage. Def-DReL’s performance is further compared with that of recent related algorithms. Simulation and comparison results clearly demonstrate a lesser number of serverless functions from each user (with approximately 10% improvement) being deployed in the fog node, resulting in accommodating limited fog resources to more number of users. The other simulation results show its superiority over other algorithms as well as its applicability to real-life scenarios.

DCSP: A delay and cost-aware service placement and load distribution algorithm for IoT-based fog networks

The service placement problem in a fog-cloud pertains to the intricate task of ascertaining the optimal placement locations for diverse service replicas that constitute an Internet of Things (IoT) application within a distributed computing architecture. Nonetheless, due to the heterogeneous nature of fog nodes and cloud data centers, the constantly evolving nature of IoT applications, the pressing demand for real-time data processing with minimal latency, and the imperative of cost optimization, service placement remains an arduous and intricate challenge. Heuristic algorithms are widely employed to derive near-optimal solutions. The present study introduces a novel heuristic approach to service placement and load distribution in fog-cloud environments called Delay and Cost-aware Service Placement (DCSP). This approach takes into account both delay and cost and thereby enables the optimal utilization of resources, delay, and cost for delay-sensitive services. DCSP features continual adjustment of the number of service replicas based on workload and resource availability, ensuring efficient usage of fog and cloud computing resources and optimal delay for each request. The results of simulation experiments that compare various algorithms demonstrate the superiority of DCSP, which outperforms other methods in cost, average delay, and resource utilization. The experimental results not only illustrate the exceptional superiority of our method over MOHGA, MVC, and the Local method in terms of delay and utilization but also demonstrate our efforts to maintain cost at a comparable level. Our method surpasses all of them in utilization, achieving remarkable improvements ranging from 7.4 % to 49.7 %. Furthermore, it outperforms them in terms of delay, with significant enhancements ranging from 2.1 % to 19.3 %.

MicroFog: A framework for scalable placement of microservices-based IoT applications in federated Fog environments

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework to operate within federated environments, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework compatible with cloud-native technologies such as Docker, Kubernetes and Istio. MicroFog provides an extensible and configurable control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition, thus enabling users to easily incorporate new placement policies and evaluate their performance. The capabilities of the MicroFog framework, such as the scalability and flexibility of the design and deployment architecture of MicroFog and its ability to ensure the deployment and composition of microservices across distributed fog–cloud environments, are validated using multiple use cases. Experiments also demonstrate MicroFog’s ability to integrate and evaluate novel placement policies and load-balancing techniques. To this end, we integrate multiple microservice placement policies to demonstrate MicroFog’s ability to support horizontally scaled placement, service discovery and load balancing of microservices across federated environments, thus reducing the application service response time up to 54%.

DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

DE-GWO: A Multi-objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment

Genetic-Based Algorithm for Task Scheduling in Fog–Cloud Environment

Genetic-Based Algorithm for Task Scheduling in Fog–Cloud Environment

Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system’s vulnerability comprehensively. To enhance the system’s capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system’s perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system’s ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model’s ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.

Hybrid VGG19 and 2D-CNN for intrusion detection in the FOG-cloud environment

Fog computing (FC) extends cloud technology by providing services, storage, and network interaction between data centers and end devices. By putting storage and processing closer to the network edge, fog computing may improve IDS effectiveness and efficiency. We develop a cloud-fog intrusion detection system (CFIDS) that employs cloud and fog resources to identify threats in the current research. However, the attack affects data packets from Fog layer nodes. The majority of intrusive cyber-attacks are close variants of previously identified cyber attacks with duplicate data and attributes. In addition, the data packet must be analyzed to determine the intrusion. SYN packets, used to initiate TCP connections, flood a victim's network in a syn threat. To mitigate the impact of syn attacks in a FC environment, it is essential to implement the necessary security measures. Deep learning-based IDS are suitable for protecting fog computing layers from attacks and addressing current issues. The authors of this paper have put forth a new hybrid architecture for DL intrusion detection in fog computing, which combines the use of DL models. Due to the large dimensionality of network data, Radial basis function-based support vector regression (RBFSVR)is initially used to minimize the dimensionality of the data and reduce the training time. Then, on the cloud server, the integrated VGG19 and 2DCNN are utilized to complete the dataset's training and transfer it to the fog layer, where data transmission is observed and threats are recognized. Experiments with the UNSW-NB15, CICIDS2017, and CICIDS2018 datasets demonstrate that the techniques presented in this paper outperforms other comparable techniques in terms of detection rate, F-score, precision, recall, FAR, and accuracy, thereby solving the problem of intrusion detection. In addition, by analyzing irregular patterns of network traffic, flow duration isutilized to avoid harmful attacks in a fog computing environment.

An efficient resource allocation of IoT requests in hybrid fog–cloud environment

An efficient resource allocation of IoT requests in hybrid fog–cloud environment

Reduce Energy Consumption by Intelligent Decision-Making in a Fog-Cloud Environment

Reduce Energy Consumption by Intelligent Decision-Making in a Fog-Cloud Environment