Comparison of Task Scheduling Algorithms for Traffic Surveillance Application Using Fog Computing

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. However, due to the limited capacity of the fog devices, a large number of tasks will still be offloaded to the cloud for processing. This can overload the backbone network and cause excessive delays. In addition, the distribution of IoT devices is uneven. This paper groups fog nodes into a fog federation to increase the number of tasks that fog nodes can handle. The boom in fog computing has made fog pricing an important issue. But so far, no one has set a price for the federation. This article uses the Stackelberg model to simulate the interaction process between fog federation and IoT devices. First, fog devices bundle certain computing power, memory, and some other resources into a computing resource block (CRB). Then, fog devices act as a leader to release the price of CRB, and IoT devices act as a follower to determine the amount of CRB purchased. This article introduces how IoT can buy the right amount of CRB to minimize its cost and how CRB is priced to maximize the revenue of the fog federation.KeywordsFog computingCloud computingStackelbergInternet of Things

Fog computing is an emerging paradigm which extends the functionality of the cloud near to the end users. Its introduction helped in running different real-time applications where latency is a critical factor. This paradigm is motivated by the fast growth of Internet of Things (IoT) applications in different fields. By running Virtual Machines (VMs) on fog devices, different users will be able to offload their computational tasks to fog devices to get them done in a smooth, transparent, and faster manner. Nevertheless, the performance of real-time applications might suffer if no proper live virtual machine migration mechanism is adopted. Live VM migration aims to move the running VM from one physical fog node to another with minimal or zero downtime due to mobility issues. Many efforts have been made in this field to solve the challenges facing live VM migration in fog computing. However, there are remaining issues that require solutions and improvements. In this paper, the following presents the research outcomes: An extensive survey of existing literature on live VM migration mechanisms in fog computing. Also, a new novel classification approach for categorizing live VM migration mechanisms based on conventional and Artificial Intelligence (AI) approaches to address live VM migration challenges is presented. Moreover, an identification of research gaps and in the existing literature and highlighting the areas where further investigation is required is done and finally a conclusion with a discussion of potential future research directions is drawn

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.

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.

The performance of the multiprocessor system and time-sharing system rely upon CPU scheduling algorithm. Some of the well known efficient CPU scheduling algorithms are Round Robin scheduling (RR), Shortest job first(SJF), Preemptive version of SJF(SRTF), First Come First Serve (FCFS) etc. As of now, the Round Robin scheduling algorithm is considered as the efficient process scheduling algorithm among all the existing CPU scheduling algorithms. However, in RR the shortest one have to wait for a longer time and in SRTF longer process behaves as a suspended process as short tasks keep on executing. This paper proposes a new scheduling process algorithm ESRR (Efficient Shortest Remaining Time Round Robin) that consolidate two of the preemptive version of existing scheduling algorithms namely RR and SRTF. ESRR reduces total waiting time and turn around time in compare to RR and reduces the waiting time of the shorter process and it also provides a longer process to execute faster than SRTF.

Resource allocation in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.

<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>

CPU scheduling is the basis of multi-programmed operating systems. The scheduler is accountable for multiplexing processes on the CPU. By switching the CPU among processes, the operating system results in the computer more prolific. Various CPU scheduling algorithms exist for a multi-programmed operating system like First Come First Served (FCFS), Shortest Job First (SJF), Shortest Remaining Time Next (SRTN) and Round Robin (RR) scheduling algorithm. This paper has threefold objective. Firstly, it throws light on a new-fangled CPU scheduling algorithm called MIN-MAX whichbehaves as both preemptive and non-preemptive algorithm basis on the burst time. Secondly, it would helps to understand how MIN-MAXalgorithm improves the CPU efficiency in multi programming operating system and also trims down the starvation problem amongprocesses. Thirdly, it focuses on the comparative study of the existing algorithms on basis of various scheduling parameters with the proposed algorithm MIN-MAX.

Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment

SummaryFog computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage and application services to end users. In this article, we elaborate the motivation and advantages of Fog computing and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state of the art of Fog computing and similar work under the same umbrella. Distinguished from other reviewing work of Fog computing, this paper further discloses the security and privacy issues according to current Fog computing paradigm. As an example, we study a typical attack, man‐in‐the‐middle attack, for the discussion of system security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device. In addition, we discuss the authentication and authorization techniques that can be used in Fog computing. An example of authentication techniques is introduced to address the security scenario where the connection between Fog and Cloud is fragile. Copyright © 2015 John Wiley & Sons, Ltd.

Electricity consumption for cooling purpose is known to be the most expensive operational cost factor in data centers. Inefficient cooling leads to high temperature and this in turn leads to hardware failure. This paper proposes a Thermal Aware Modified Least Slack Time Round Robin Based (MLST-RR) scheduling algorithm that can avoid high thermal stress circumstances such as large hotspots, thermal violations as well as reduce electricity consumption for cooling in data center labs. The experimental results show that, the thermal aware MLST-RR is able to significantly decrease the electricity consumption while maintaining competitive performance. Specifically, the thermal aware scheduling algorithm saves electricity consumption as much as 15000KW compared to the benchmark job scheduling algorithms such as Round Robin (RR) and First Come First Serve (FCFS); this is an electrical saving of 8.4%.

Cloud Computing provides services, on-demand access, infrastructure, storage of data and application. It possesses the reliability, availability and the scalability. One of the issues in cloud computing is Energy Saving. In this paper, the proposed work is Energy Saving Task Scheduling (ESTS) methodology. The aim of this methodology is to show the performance comparison of all the task scheduling types. Task scheduling or Job scheduling is referred to as policies that control the work order to be performed by a computer system. Types of Task Scheduling are Shortest Job First (SJF), First Come First Serve (FCFS), Round Robin (RR) and Priority Scheduling. In each type of schedule, the processes used by the parameters were calculated. Finally, the performance comparison is made in scheduling algorithms and shows better results. This method is implemented in net beans toolkit.

Fog computing assits the development of distributed real-time systems. This offers solutions to a quicker response systems for developing disaster monitoring, prevention and detection models into existence. This paper proposes the integration of Fog computing and Convolutional Neural Networks (CNN) with Unmanned Aerial Vehicles (UAV) to detect the forest fire at an early stage. A highly efficient CNN model has been used for fire image recognition due to its proven ability for such recognition tasks. By using AlexNet and other architectures in the proposed model, image recognition tasks have become more capable, to an extent that a pre-trained model has an ability equal to a primate. Using these architectures, we trained our model and deployed the same on a Fog device, which has resulted in achieving higher accuracy and response time.

In a distributed system, fog computing (FC) is an emerging computer technique. The goal of FC is to position cloud-based services in close proximity to endpoints. The method is meant to meet the minimal latency requirement for Iot - based healthcare equipment. There is a wide range of healthcare data volumes produced by Iot - based healthcare equipment. Network congestion and increased delay are the direct effects of this massive influx of data. Patient information is rendered useless and insufficient for end-users when round-trip time delays rise due to huge data transfer and increasing hop counts between IoTs and cloud servers. In the healthcare industry, real-time data is essential for time-sensitive applications. The medical IoT devices and their users have stringent requirements for latency, and traditional cloud servers just can’t provide them. Therefore, it is important to decrease network delay, compute latency, and communications latency while transmitting data through the Internet of Things. FC allows data to be stored, processed, and analyzed in the cloud and at the network’s edge, where the latency is lower. This article proposes an innovative approach to solving the aforementioned issue. It combines an FC-based analytical model with a hybrid fuzzy-based RL algorithm. High latency in healthcare IoTs, between users and cloud servers, is something that has to be mitigated. Allocation and selection of data packets in an IoT-FC setting are handled with the help of a fuzzy inference system, reinforcement learning, and neural network evolution techniques provided by the suggested smart FC model parameters and algorithms. The method is put through its paces on the iFogSim (Net-Beans) as well as Spyder simulations (Python). The acquired findings demonstrated that the suggested strategy outperformed the state-of-the-art techniques.

Task scheduling is crucial for offering the users of an operating system the impression that the system’s response is direct and in real time. However, it is a complicated process that is characterized by vagueness and uncertainty. A solution to this problem is the usage of fuzzy logic. Therefore, in this paper a rule-based fuzzy scheduling method, which considers both the execution time and the waiting time for each task, is presented. Fuzzy sets are used to describe both criteria. The operation of the presented scheduling method is based on a rule-based reasoner that decides dynamically about the priority of the tasks that wait to be executed. This reasoner is triggered each time a change occurs (i.e. the execution of a task ends, a new task arrives etc.). It has been compared with Short Job First (SJF) and First Come First Served (FCFS) scheduling algorithms for a large number of different sets of tasks. The results showed that the presented fuzzy scheduling algorithm in all the cases has the same or almost the same average waiting time with SJF, which is considered as the scheduling algorithm with the best (minimum) average waiting time. However, the presented fuzzy scheduling algorithm ensures that priority is given not only to tasks with short execution time, but also to tasks that remain into the queue waiting to be executed for a long time. In this way, the starvation, which may be caused by SJF algorithm, is eliminated.