Resource Management In Fog Research Articles

Optimizing resource allocation for cluster D2D-assisted fog computing networks: A three-layer Stackelberg game approach

Resource management in fog computing is a highly challenging issue. Resource constraints of fog devices, the dynamic and diverse nature of workloads such as data-intensive, computationally intensive, and latency-sensitive applications, and the unpredictability of fog computing environments make it more challenging. How to collaboratively schedule edge, and end multi-layer ubiquitous computing resources to break through single node scale and resource bottlenecks to further improve computing performance also needs to be addressed. In this paper, we leverage idle resources on the user side, and study the resource allocation problems in the D2D-assisted fog computing networks. Firstly, we use an improved Louvain algorithm to cluster users and select the D2D device with the highest priority in each cluster as the cluster head to assist fog computing, where cluster heads can act as both servers and relays. Secondly, considering that fog nodes and D2D devices have limited resources and are not obligated to share their resources for free, we propose a price incentive method to construct a three-layer Stackelberg game model that maximizes the benefits of fog nodes and cluster heads while minimizing user costs. Then, we use the Lagrange multiplier method and the Karush–Kuhn–Tucker (KKT) conditions to solve the equilibrium point of the model to achieve our goal. Finally, the simulation results show that our proposed scheme markedly decreases the overall system cost in comparison to the four benchmark schemes. Moreover, in various scenarios the total cost of our scheme consistently remains lower than the other four schemes, affirming the efficiency of the scheme proposed in this paper.

A holistic mathematical cyber security model in fog resource management in computing environments

In the evolving landscape of computing environments, fog computing has emerged as a pivotal technology for handling the increasing demands of Internet of Things (IoT) applications. Fog computing, with its decentralized approach, brings computational power closer to IoT devices, reducing latency and bandwidth constraints. However, this distributed architecture poses significant cybersecurity challenges, necessitating a holistic mathematical model to ensure the security and integrity of fog resources. This review presents a comprehensive mathematical cyber security model that is tailored to fog resource management in computing environments. The models generally encompass a wide array of security aspects, including data confidentiality, integrity, availability, and authentication. Leveraging advanced mathematical techniques, the models offer a robust framework for assessing and enhancing security in fog computing systems. The key components include threat modeling, risk assessment, and dynamic resource allocation strategies. By systematically analyzing potential threats and vulnerabilities specific to fog computing, these models assist in devising effective countermeasures and security policies. Moreover, there is a need to incorporate an adaptive resource allocation algorithm to dynamically allocate computing resources based on security priorities and real-time threat assessments. This review demonstrates the effectiveness of the mathematical cyber security model in bolstering the resilience of fog computing environments. The review highlights the need for safeguarding the next generation of fog computing systems against cyber threats, fostering the continued growth and adoption of transformative technology.

No-regret learning for repeated non-cooperative games with lossy bandits

This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback. Since it is difficult to give an explicit model of the utility functions in dynamic environments, the players’ actions can only be learned with bandit feedback. Moreover, due to unreliable communication channels or privacy protection, the bandit feedback may be lost or dropped at random. Therefore, we study the asynchronous online learning strategy of the players to adaptively adjust the next actions for minimizing the long-term regret loss. The paper provides a novel no-regret learning algorithm, called Online Gradient Descent with lossy bandits (OGD-lb). We first give the regret analysis for concave games with differentiable and Lipschitz utilities. Then we show that the action profile converges to a Nash equilibrium with probability 1 when the game is also strictly monotone. We further provide the mean-squared convergence rate ONpi−2k−1/3 when the game is β-strongly monotone, where N denotes the number of players and pi is the update probability. In addition, we extend the algorithm to the case when the loss probability of the bandit feedback is unknown, and prove its almost sure convergence to Nash equilibrium for strictly monotone games. Finally, we take the resource management in fog computing as an application example, and carry out numerical experiments to empirically demonstrate the algorithm performance.

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

AbstractThe energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

REVIEW OF SOLUTIONS TO THE PROBLEM OF RESOURCE ALLOCATION IN FOG COMPUTING

Despite the high popularity of cloud computing, they are not recommended for use in applications and systems that are critical to network delays. Therefore, given the increasing volumes of data circulating over global networks, the concept of fog computing has become very relevant, as it allows to significantly offload cloud services, while minimizing latency and expanding computing capabilities of cloud technologies. As an intermediate layer between cloud and end user, fog computing improves network performance by taking on some of the load. The efficiency of fog computing as well as efficiency of any computer network, directly depends on the efficiency of resource allocation for service requests. Given this fact and taking into account the mobility, heterogeneity and dynamic nature of fog computing, there is an urgent challenge of organizing the distribution of resources in the fog to provide high quality user service. This review provides a full and comprehensive view of the available solutions to the problem of resource allocation in the fog. The article presents works classified with respect to quality of service metrics, policy types and classes of resource allocation algorithms. Approaches to resource management in fog computing for Internet of things, that is critical to delays. Analysis of results presented in the work overview with research and practical nature, made it possible to formulate the significance of solving this problem, to identify key challenges in the development of approaches to fog resource allocation and key metrics for their effeiciancy and performance evaluation. The conclusions obtained in the present study not only testify to the relevance and importance of the resource allocation problem in fog computing, but also allows to determine key factors that must be taken into account when solving it.

RETRACTED: Distributed IoT services placement in fog environment using optimization-based evolutionary approaches

Fog computing is a promising computing paradigm for the processing and storage of massive data generated by Internet of Things (IoT) devices. Fog computing devices have limited resources compared to the cloud. Meanwhile, the dynamism and heterogeneity of applications requested from IoT devices require quick decisions on where to process services on the fog, the cloud, or the hybrid. This problem is known as Fog Service Placement (FSP), which is a computationally NP-hard problem. Therefore, the efficient allocation of fog resources to IoT services by non-deterministic algorithms is emphasized. In this paper, a Quality of Service (QoS)-aware deployment scheme is proposed using an evolutionary approach to address the FSP problem. Here, several new evolutionary approaches such as Teaching Learning-Based Optimization (TLBO), Honey Badger Algorithm (HBA) and Harris Hawks Optimizer (HHO) are analyzed to solve the problem. We improve the evolutionary approach used to solve the FSP problem with a shared parallel architecture and perform distributed fog resource management. In addition, we bring the processing closer to the fog network by considering the priority of service execution. Here, we formulate a multi-objective function as an optimization problem that simultaneously considers delay cost, delay violation, service cost and fog utilization. All the parameters needed to configure the objective function are collected based on the incoming traffic. The simulations show the superiority of HBA for solving the FSP problem. The evaluation results show that the proposed placement scheme improves the cost and delay compared to the best existing method by 2.6% and 2.8%, respectively.

Machine learning-based solutions for resource management in fog computing

Machine learning-based solutions for resource management in fog computing

A holistic mathematical cyber security model in fog resource management in computing environments

Now a days Cloud computing isn’t common sense for some Internet of things applications, fog enrolling is every now and again used. Its proper methodology keeps an eye on the necessities of IoT and present-day IoT, as well as the enormous proportion of splendid data sensors and IoT gadgets, produce, which would be extreme and drawn-out to send off the cloud for dealing with and examination. Fog enrolling diminishes the information move limit required and reduces the unstable correspondence among sensors and the cloud, which can antagonistically impact IoT execution. Fog is exceptionally conveyed and comprises a wide number of independent end gadgets, which add to the handling. In any case, the range of gadgets presented across various clients is not examined. Consequently, the security of fog computing is a main pressing issue that ought to come into thought. Thusly, to give the vital security to fog computing, there is a need to comprehend what the security concerns are with respect to fog. Fog computing is a decentralized enlisting establishment where data, handling, storing, and applications are seen as some place near the data source and the cloud. Like edge enrolling, murkiness figuring brings the advantages and power of the cloud closer to where data is made and circled back to. With fog computing, close-by data amassing and examination of time-delicate data become more direct. With this the aggregate and the distance of data passed to the cloud are diminished, thus lessening the impact of well-being and security issues.

An Efficient Resource Management Mechanism Based on Developed Political Optimizer in Fog Computing

Due to the advanced technologies related to Internet of Things (IoT), fog computing becomes an emerging distributed computing model for supporting the functionalities of the IoT. Basically, the workload of IoT-based services is not constant over time, and it is challenging to manage the resources associated with them by fog computing. In this regard, resource management mechanisms are necessary in order to improve the quality-of-service requirements and solve resource shortage problems in the fog computing environment. This article presents an Efficient Resource Management mechanism based on Developed Political Optimizer for fog computing environment (ERM-DOP). ERM-DOP aims at efficient resource allocation and maximum resource utilization in fog environment. In addition, other objectives are considered in ERM-DOP: latency reduction, energy saving, response time reduction, and bandwidth saving. The simulation results in CloudSim and MATLAB show the superiority of the proposed mechanism compared to state-of-the-art methods, such as GFC, VRP, and RMT-PSO. The evaluation results show that ERM-DOP effectively deals with resource management in fog and improves performance between 7.5% and 23.6% in terms of various metrics, such as latency cost, response time, network bandwidth, and energy consumption.

Extensibility-aware Fog Computing Platform configuration for mixed-criticality applications

In this paper, we consider that critical control applications and Fog applications share a Fog Computing Platform (FCP). Critical control applications are implemented as periodic hard real-time tasks and messages and have stringent timing and safety requirements, and require safety certification. Fog applications are implemented as aperiodic tasks and messages and are not critical. Such applications need different approaches to guarantee their timing and dependability requirements. We formulate an optimization problem for the joint configuration of critical control and Fog applications, such that (i) the deadlines and Quality-of-Control (QoC) of control applications are guaranteed at design-time, (ii) the configuration is extensible and supports the addition of future new control applications without requiring costly re-certification, and (iii) the design-time configuration together with the runtime Fog resource management mechanisms, can successfully accommodate multiple dynamic responsive Fog applications. We evaluate our approach on several test cases assuming scenarios for hosting both Fog applications and future critical control applications. The results show that our approach generates extensible schedules which enables Fog nodes to handle Fog applications with a shorter response time and a larger number of future control applications.

XFogSim: A Distributed Fog Resource Management Framework for Sustainable IoT Services

Streaming large amounts of data to cloud data centers cause network congestion resulting in high network and energy consumption. The concept of fog computing is introduced to reduce workload from backbone networks and support delay-sensitive Internet of Things (IoT) applications. The concept places compute, storage, and network services closer to the source of the requests. In general fog-based simulators are used for better understanding and optimum fog resource allocation. Unfortunately, most of the simulators lack core features like network delay, latency, packet error rate, energy consumption, and distributed fog node management. In this paper, we propose a fog simulation framework termed as xFogSim to support latency-sensitive applications at the fog layer with multi-objective optimization to trade-off cost, availability, and performance among the fog federation. Moreover, during peak load, the framework provides locality-aware distributed broker node management that enables borrowing resources from nearby fog locations to meet service and energy requirements. The results show that the framework is lightweight, configurable, and scalable, capable of handling a large number of user requests using dynamic resource provisioning across the fog federation.

Design, Resource Management, and Evaluation of Fog Computing Systems: A Survey

A steady increase in Internet-of-Things (IoT) applications needing large-scale computation and long-term storage has lead to an overreliance on cloud computing. The resulting network congestion in the cloud, coupled with the distance of cloud data centers from IoT, contributes to unreliable end-to-end response delay. Fog computing has been introduced as an alternative to cloud, providing low-latency service by bringing processing and storage resources to the network edge. In this survey, we sequentially present the phases required in the implementation and realization of practical fog computing systems: 1) design and dimensioning of a fog infrastructure; 2) fog resource provisioning for IoT application use and IoT resource allocation to fog; 3) installation of fog frameworks for fog resource management; and 4) evaluation of fog infrastructure through simulation and emulation. Our focus is on determining the implementation aspects required to build a practical large-scale fog computing infrastructure to support the general IoT landscape.

Improving fog computing performance via Fog-2-Fog collaboration

Abstract In the Internet of Things (IoT) era, a large volume of data is continuously emitted from a plethora of connected devices. The current network paradigm, which relies on centralised data centres (aka Cloud computing), has become inefficient to respond to IoT latency concern. To address this concern, fog computing allows data processing and storage “close” to IoT devices. However, fog is still not efficient due to spatial and temporal distribution of these devices, which leads to fog nodes’ unbalanced loads. This paper proposes a new F og-2- F og ( F 2 F ) collaboration model that promotes offloading incoming requests among fog nodes, according to their load and processing capabilities, via a novel load balancing known as Fog Resource manAgeMEnt Scheme (FRAMES). A formal mathematical model of F 2 F and FRAMES has been formulated, and a set of experiments has been carried out demonstrating the technical doability of F 2 F collaboration. The performance of the proposed fog load balancing model is compared to other load balancing models.

Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

With the recent development in information and communication technology, more and more smart devices penetrate into people's daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.