A Task Scheduling Algorithm Based on Classification Mining in Fog Computing Environment

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.

As an extension of cloud computing, fog computing is a promising computing paradigm. By placing resources near terminals, users can access the fog servers with lower latency than cloud and utilize server resources to complete offloading tasks for optimizing QoE. Task scheduling in fog computing is to map requested tasks to appropriate fog node, so as to improve resource utilization efficiency and drop the cost of IoT devices. In this paper, we focused on the scheduling problem of offloaded tasks in fog scenarios with multiple users. We first formulated the problem as a combinatorial optimization problem, and proposed an improved integer particle swarm optimization method to solve the problem in a reasonable time. Experimental results showed that the proposed algorithm can reduce the running time by 90% compared with the traversal method, while resulting an approximate optimal value. It has good performance in the optimization of task scheduling problems.

Fog computing is one of the rapidly growing platforms with an exponentially increasing demand for real-time data processing. The fog computing market is expected to reach USD 8358 million by the year 2030 with a compound annual growth of 50%. The wide adaptation of fog computing by the industries worldwide is due to the advantages like reduced latency, high operational efficiency, and high-level data privacy. The highly distributed and heterogeneous nature of fog computing leads to significant challenges related to resource management, data security, task scheduling, data privacy, and interoperability. The task typically represents a job generated by the IoT device. The action indicates the way of executing the tasks whose decision is taken by the scheduler. Task scheduling is one of the prominent issues in fog computing which includes the process of effectively scheduling the tasks among fog devices to effectively utilize the resources and meet the Quality of Service (QoS) requirements of the applications. Improper task scheduling leads to increased execution time, overutilization of resources, data loss, and poor scalability. Hence there is a need to do proper task scheduling to make optimal task distribution decisions in a highly dynamic resource-constrained heterogeneous fog computing environment. Flow Q learning (FQL) is a potential form of reinforcement learning algorithm which uses the flow matching policy for action distribution. It can handle complex forms of data and multimodal action distribution which make it suitable for the highly volatile fog computing environment. However, flow Q learning struggles to achieve a proper trade-off between the expressive flow model and a reduction in the Q function, as it relies on a one-step optimization policy that introduces bias into the estimated Q function value. The Tiny Language Model (TLM) is a significantly smaller form of a Large Language Model (LLM) which is designed to operate over the device-constrained environment. It can provide fair and systematic guidance to disproportionally biased deep learning models. In this paper a novel TLM guided flow Q learning framework is designed to address the task scheduling problem in fog computing. The neutrality and fine-tuning capability of the TLM is combined with the quick generable ability of the FQL algorithm. The framework is simulated using the Simcan2Fog simulator considering the dynamic nature of fog environment under finite and infinite resources. The performance is found to be good with respect to parameters like execution time, accuracy, response time, and latency. Further the results obtained are validated using the expected value analysis method which is found to be satisfactory.

<span>The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.</span>

With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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.

Cloud computing technology is getting more advanced these days. Cloud computing is a pay-as-you-go service since it allows for variable resource allocation for services. Task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing among cloud resources. For scheduling resources in cloud environment, different tasks need to be executed simultaneously by the available resources in order to minimize the completion time. To achieve this, we proposed a new technique called Enhanced Max-Min Algorithm (EMM). This algorithm takes less time to complete all the tasks with minimum completion time. In the proposed algorithm, small size tasks are concurrently executed and resource allocation is done accordingly. When compared to Min-Min algorithm the Enhanced Max-Min algorithm provides high resource utilization and reduces the makespan.

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.

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.

The problems of energy-constrained and time-constrained task scheduling on multiple heterogeneous computers are investigated as combinatorial optimization problems. For a given set of independent tasks, our strategy is to find a schedule of the tasks first, and then find a power allocation to the tasks, where the power allocation is performed in such a way that the total task execution time or the total energy consumption is minimized. We are able to find an optimal partition of a given workload and use a modified list scheduling (MLS) algorithm to generate a partition of the set of tasks that is an approximation of the optimal workload partition. Our simulation results demonstrate that when compared with optimal solutions, the MLS algorithm has excellent expected performance in solving the problems of energy-constrained and time-constrained scheduling of independent tasks on multiple heterogeneous computers. For precedence constrained tasks represented by a directed acyclic graph (dag), our level-by-level modified list scheduling (LL-MLS) algorithm schedules tasks level by level (LL) and schedules tasks in the same level by using the MLS algorithm. We are able to solve the problems of optimal energy/time allocation to the levels and optimal workload partition for all levels in a given dag. Our simulation results demonstrate that when compared with optimal solutions, the LL-MLS algorithm has excellent expected performance in solving the problems of energy-constrained and time-constrained scheduling of precedence constrained tasks on multiple heterogeneous computers.

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.

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 extended version of cloud computing, which is used to serve huge data in the Internet of Things scenario. The development of fog computing improves the equipment’s computational power and delay by using modern information technology. Additionally, this fog computing improves calculation, communique, and storage services towards the edge of a network. The applications of fog computing are smart grid, street line parking automation system, smart health-care monitoring system, mobile big data analytics, etc. Additionally, the fog computing is used to solve the issues faced by the rapid development of IoT. The acceptable quality of experience is difficult to obtain among the concentrated applications and resource constrained gadgets. The aforementioned issues is resolved by using the task scheduling in fog computing. This paper discusses the classification of job scheduling and conventional methods that are used for job scheduling in fog computing along with its advantages and limitations. This detailed analysis will help researchers to obtain a better solution for the current concerns faced in fog computing in big data processing.

Exploring and Optimizing the Fog Computing in Different Dimensions

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.