Effective Cache Placement for Content Delivery Networks in Fog Computing

In the fog computing (FC) models, a fog node supports application processes to calculate output data on input data from sensors and other fog nodes and sends the output data to target fog nodes which can calculate on the output data. In this paper, we consider the MPSFC (Mobile topic-based PS (publish/subscribe) FC) model where mobile fog nodes communicate with one another by publishing and subscribing messages in wireless networks. Subscription topics of a fog node denote input data on which the fog node can calculate and publication topics of a message show data carried by the message. A fog node only receives a message whose publication topics shares a common topic with the subscription topics. In the TBDT protocol proposed in our previous studies, a fog node only publishes a message of the output data to a target fog node in the communication range. Here, while a fewer number of messages are transmitted, the delivery ratio of messages in the TBDT protocol is smaller than the epidemic routing protocol. In this paper, we propose an ETBDT (Epidemic and Topic-Based Data Transmission) protocol in order to increase the delivery ratio, where mobile fog nodes forward messages to not only target nodes but also non-target nodes in the communication range. In the evaluation, we show the delivery ratio in the ETBDT protocol is larger than the TBTD protocol.

In the fog computing model of the IoT, subprocesses of an application process to handle sensor data are performed on fog nodes. Since the IoT is scalable, we have to reduce the electric energy consumption. In the tree-based fog computing (TBFC) model, fog nodes are hierarchically structured. In this paper, we propose a fault-tolerant TBFC (FTBFC) model. In addition, we newly propose a pair of fault-tolerant strategies. In one data transmission strategy, data processed by disconnected fog nodes is sent to a new parent fog node. Here, we propose a minimum energy (ME) algorithm to select a new parent fog node whose energy consumption is minimum. In another subprocess transmission strategy, the subprocess of the faulty fog node is sent to another fog node. In the evaluation, the energy consumption and execution time of a new parent fog node can be reduced by the ME algorithm.

Fog computing is an emerging paradigm that extends computation, communication, and storage facilities toward the edge of a network. Compared to traditional cloud computing, fog computing can support delay-sensitive service requests from end-users (EUs) with reduced energy consumption and low traffic congestion. Basically, fog networks are viewed as offloading to core computation and storage. Fog nodes in fog computing decide to either process the services using its available resource or send to the cloud server. Thus, fog computing helps to achieve efficient resource utilization and higher performance regarding the delay, bandwidth, and energy consumption. This survey starts by providing an overview and fundamental of fog computing architecture. Furthermore, service and resource allocation approaches are summarized to address several critical issues such as latency, and bandwidth, and energy consumption in fog computing. Afterward, compared to other surveys, this paper provides an extensive overview of state-of-the-art network applications and major research aspects to design these networks. In addition, this paper highlights ongoing research effort, open challenges, and research trends in fog computing.

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 computing is seen as a promising approach to perform distributed, low-latency computation for supporting Internet of Things applications. However, due to the unpredictable arrival of available neighboring fog nodes, the dynamic formation of a fog network can be challenging. In essence, a given fog node must smartly select the set of neighboring fog nodes that can provide low-latency computations. In this paper, this problem of fog network formation and task distribution is studied considering a hybrid cloud-fog architecture. The goal of the proposed framework is to minimize the maximum computational latency by enabling a given fog node to form a suitable fog network, under uncertainty on the arrival process of neighboring fog nodes. To solve this problem, a novel approach based on the online secretary framework is proposed. To find the desired set of neighboring fog nodes, an online algorithm is developed to enable a task initiating fog node to decide on which other nodes can be used as part of its fog network, to offload computational tasks, without knowing any prior information on the future arrivals of those other nodes. Simulation results show that the proposed online algorithm can successfully select an optimal set of neighboring fog nodes while achieving a latency that is as small as the one resulting from an ideal, offline scheme that has complete knowledge of the system. The results also show how, using the proposed approach, the computational tasks can be properly distributed between the fog network and a remote cloud server.

FellowMe Cache: Fog Computing approach to enhance (QoE) in Internet of Vehicles

With the development in computing technologies, fog computing is developing as public and robust computing, which complements cloud computing to provide services, computation, and storage on the edge network. The future growth and support of 5G access networks additional advance the viability and implementation of fog networks and widen the scope of devices that can participate and serve in IoT communication. However, scaling fog computing, the number of end-users increases. Hence, the workload between fog nodes needs to be distributed efficiently. Otherwise, some of the nodes will be overloaded, and others will be under-loaded. Consequently, one of the critical factors for managing resources in fog computing efficiently and avoiding overloaded or under-loaded is load balancing. Therefore, load balancing between these resources is a challenge in fog computing. There are different techniques to balance the load, such as optimization algorithms or machine learning. This paper proposes a load balancing model in fog computing based on a many-objective particle swarm optimization (PSO) algorithm with support vector regression (SVR). The proposed load balancing model considered four metrics to optimize them while distributing the load: response time, energy consumption, resource utilization, and throughput. Besides, It combines SVR with PSO to improve PSO performance. The proposed model has been simulated and tested to evaluate the performance from different aspects. The experiments show that the proposed model efficiently balances the load with optimizing the four metrics. In addition, it improves the performance of PSO, which is used to balance the load.

Fog computing is an encouraging technology in the coming generation to pipeline the breach between cloud data centers and Internet of Things (IoT) devices. Fog computing is not a counterfeit for cloud computing but a persuasive counterpart. It also accredits by utilizing the edge of the network while still rendering the possibility to interact with the cloud. Nevertheless, the features of fog computing are encountering novel security challenges. The security of end users and/or fog nodes brings a major dilemma in the implementation of real life scenario. Although there are several works investigated in the security challenges, physical layer security (PLS) in fog computing is not investigated in the above. The distinctive and evolving IoT applications necessitate new security regulations, models, and evaluations disseminated at the network edge. Notwithstanding, the achievement of the current cryptographic solutions in the customary way, many aspects, i.e., system imperfections, hacking skills, and augmented attack, has upheld the inexorableness of the detection techniques. Hence, we investigate PLS that exploits the properties of channel between end user and fog node to detect the impersonation attack in fog computing network. Moreover, it is also challenging to achieve the accurate channel constraints between end user and fog node. Therefore, we propose Q-learning algorithm to attain the optimum value of test threshold in the impersonation attack. The performance of the propose scheme validates and guarantees to detect the impersonation attack accurately in fog computing networks.

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.

Fog computing is used to enrich the ability of cloud computing applications. Fog is a kind of buffer area placed between the data processing location and the data storage equipment in the network and plays a significant role in processing the real time data. The lack of resource provisioning approaches and high demand for IoT services make the fog node overloaded. Load balancing is a method to realize efficient resource utilization to avoid bottlenecks, overload, and fog node failure. This study suggests a concept to compute the probabilistic overloading state of a fog node and identification of fog node for load sharing. Each fog node computes Fstate and sends the message at regular intervals to the fog node coordinator (FNC). FNC maintains a fog that is utilized for offloading in case of fog overloading. A comparative study shows that the proposed model avoids an overloading state by the transfer of a certain number of requests to an underloaded fog node before actual overloading occurs. Numerical results validate theoretical investigation and efficiency of the proposed study.

Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature.

In order to reduce the traffic of networks and servers in the IoT, types of the fog computing (FC) models are proposed, which are composed of fog nodes. A fog node supports application processes to calculate output data on sensor data and forward the output data to servers. A topic-based PS (publish/subscribe) model is a new contents-aware, event-driven model of a distributed system. Here, a process publishes a message whose contents are denoted by publication topics. A process specifies subscription topics and only is delivered messages whose publication topics include some of the subscription topics. In our previous studies, the MPSFC (Mobile PS (publish/subscribe) Fog Computing) model is proposed where mobile fog nodes like vehicles are interconnected and fog nodes communicate with one another in wireless networks by the PS model. In this paper, we propose a TBDT (Topic-based Data Transmission) protocol for mobile fog nodes to deliver messages to target nodes. In the evaluation, we show the number of messages in the TBDT protocol is fewer than the epidemic routing protocol while the delivery ratio is smaller.

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

Fog computing is an emerging paradigm in provisioning computing and storage resources for the Internet-of-Things (IoT) devices. In a fog computing system, all devices can offload their data or computationally intensive tasks to nearby fog nodes, instead of to the distant cloud. As compared with cloud computing, fog computing can significantly reduce the transmission delay between IoT devices and computing servers. However, the current fog system is rather susceptible to malicious attacks. To increase the security level, we propose to partition the fog system into fog node clusters (FNCs), with fog nodes (FNs) in one cluster sharing the same access control list (ACL) which is protected by a blockchain. Generating blockchains requires tremendous computing power and can rapidly drain the computing capacities of FNs. In this article, we first customize the blockchain for FNC to reduce the required computing power consumption and storage spaces. Second, a new scheme is designed for the blockchain-based FNC (BFNC) to recover ACL automatically. In addition, we propose a heuristic algorithm to reduce the time to acquire hash values of blocks by computing cooperatively with all available devices. The simulation results have demonstrated that using the cooperative computing strategy can reduce the time of computing a block hash than noncooperative strategies.

Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog node and cloud or users when fog computing introduces blockchain. The proposed model practices the reward and punishment mechanism of the blockchain to boost the fog nodes to contribute resources actively. The behavior of the fog node in contributing resources and the completion degree of the task also for contributing resources are packaged into blocks and stored in the blockchain system to form a transparent, open, and tamper-free service evaluation index. The differential game method is employed to model and solve the above process and address the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal resource contribution strategy. Indirectly, this service evaluation index also brings long-term economic benefits to fog service providers. Besides, taking advantage of the performance characteristics of the collective maintenance of blockchain and the ability to establish a credible consensus mechanism in an untrusted environment, fog computing nodes, under the proposed architecture, can have specific security protection capabilities.