Problem Of Task Scheduling Research Articles

Efficient deep reinforcement learning based task scheduler in multi cloud environment

Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm. Many authors developed Task Scheduling algorithms by using metaheuristic techniques, hybrid approaches but all these algorithms give near optimal solutions but still TSP is a highly challenging and dynamic scenario as it resembles NP hard problem. Therefore, to tackle the TSP in cloud computing paradigm and schedule the tasks in an effective way in cloud paradigm, we formulated Adaptive Task scheduler which segments all the tasks comes to cloud console as sub tasks and fed these to the scheduler which is modeled by Improved Asynchronous Advantage Actor Critic Algorithm(IA3C) to generate schedules. This scheduling process is carried out in two stages. In first stage, all incoming tasks are segmented as sub tasks. After segmentation, all these sub tasks according to their size, execution time, communication time are grouped together and fed to the (ATSIA3C) scheduler. In the second stage, it checks for the above said constraints and disperse them onto the corresponding suitable processing capacity VMs resided in datacenters. Proposed ATSIA3C is simulated on Cloudsim. Extensive simulations are conducted using both fabricated worklogs and as well as realtime supercomputing worklogs. Our proposed mechanism evaluated over baseline algorithms i.e. RATS-HM, AINN-BPSO, MOABCQ. From results it is evident that our proposed ATSIA3C outperforms existing task schedulers by improving makespan by 70.49%. Resource cost is improved by 77.42%. Energy Consumption is improved over compared algorithms 74.24% in multi cloud environment by proposed ATSIA3C.

Probabilistic Multi-Robot Task Scheduling for the Antarctic Environments with Crevasses

This paper deals with the problem of multi-robot task scheduling in the Antarctic environments with crevasses. Because the crevasses may cause hazardous situations when robots are operated in the Antarctic environments, robot navigation should be planned to safely avoid the positions of crevasses. However, the positions of the crevasses may be inaccurately measured due to the lack of sensor performance, the asymmetry of sensor data, and the possibility of crevasses drifting irregularly as time passes. To overcome these uncertain and asymmetric problems, this paper proposes a probabilistic multi-robot task scheduling method based on the Nearest Neighbors Test (NNT) algorithm and the probabilistic modeling of the positions of crevasses. The proposed method was tested with a Google map of the Antarctic environments and showed a better performance than the Ant Colony Optimization (ACO) algorithm and the Genetic Algorithm (GA) in the context of total cost and computational time.

A systematic literature review for load balancing and task scheduling techniques in cloud computing

Cloud computing is an emerging technology composed of several key components that work together to create a seamless network of interconnected devices. These interconnected devices, such as sensors, routers, smartphones, and smart appliances, are the foundation of the Internet of Everything (IoE). Huge volumes of data generated by IoE devices are processed and accumulated in the cloud, allowing for real-time analysis and insights. As a result, there is a dire need for load-balancing and task-scheduling techniques in cloud computing. The primary objective of these techniques is to divide the workload evenly across all available resources and handle other issues like reducing execution time and response time, increasing throughput and fault detection. This systematic literature review (SLR) aims to analyze various technologies comprising optimization and machine learning algorithms used for load balancing and task-scheduling problems in a cloud computing environment. To analyze the load-balancing patterns and task-scheduling techniques, we opted for a representative set of 63 research articles written in English from 2014 to 2024 that has been selected using suitable exclusion-inclusion criteria. The SLR aims to minimize bias and increase objectivity by designing research questions about the topic. We have focused on the technologies used, the merits-demerits of diverse technologies, gaps within the research, insights into tools, forthcoming opportunities, performance metrics, and an in-depth investigation into ML-based optimization techniques.

Efficient Scheduling of Energy-Constrained Tasks in Internet of Things Edge Computing Networks

We offer task scheduling algorithms that are economical in terms of energy consumption for edge computing networks that are supported by the Internet of Things (IoT). The challenges of spectrum utilization and energy-efficient work scheduling that lead to novel design are not addressed in this study, despite the fact that it provides encouraging results for task offloading. There is a possibility that the larger homogeneous fog computing architecture will include all homogeneous nodes, in addition to additional spectrum for node-to-node and device-to-device communications and work scheduling. We create a fog computing architecture that is efficient in terms of energy consumption for edge computing networks that are supported by the Internet of Things. By utilizing this approach, user-device nodes are able to collaborate while simultaneously reaping the benefits of diverse computing and network resources. In addition to this, we provide a solution to the problem of task scheduling that maximizes energy efficiency across all of the help nodes.

A novel hybrid Artificial Gorilla Troops Optimizer with Honey Badger Algorithm for solving cloud scheduling problem

Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users’ Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12–22.76% and 6.2–18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46–30.97% makespan reduction and 8.51–33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems.

An enhanced meta-heuristic algorithm used for energy conscious priority-based task scheduling problems in heterogeneous multiprocessor systems

The Task Scheduling Problem (TSP) in Multiprocessor Systems (MPS) is an emerging research area in heterogeneous distributed computing environments. Managing complex tasks and achieving optimal efficiency while scheduling tasks in MPS presents challenges. Although adding more processors to a single system greatly increases its processing power, the energy these processors produce is the main drawback. Here, we employed a multi-objective optimization strategy to reduce the makespan and Energy Consumption (EC). To reduce processor EC, we considered an effective Dynamic Voltage and Frequency Scaling (DVFS) technique. Three distinct energy sources are considered, originating from the processors' communication, idle, and active states. The scheduling sequence of tasks and the assignment of tasks to processors are two vital aspects of TSP. Here, the tasks are arranged based on priority using the Upward Rank technique. To minimize the makespan and EC while allocating tasks to processors, we use the population-based metaheuristic method called Enhanced Honey Badger Optimisation (EHBO). We proposed three improvements to the HBO algorithm in EHBO. Initially, we addressed opposite learning-based population initialization to exclude the least suitable candidates and generate a candidate scheduling population that adheres to task precedence. Subsequently, the levy-flight technique is employed to improve the local and global search and preserve their ideal healthy balance. Finally, using dynamic values rather than constant value improves the ability to obtain maximum food. Several experiments are conducted on random task graphs and real-life data sets. Additionally, the results are compared with other upgraded meta-heuristic algorithms, demonstrating the superiority of EHBO.

Improved synergistic swarm optimization algorithm to optimize task scheduling problems in cloud computing

Cloud computing has emerged as a cornerstone technology for modern computational paradigms due to its scalability and flexibility. One critical aspect of cloud computing is efficient task scheduling, which directly impacts system performance and resource utilization. In this paper, we propose an enhanced optimization algorithm tailored for task scheduling in cloud environments. Building upon the foundation of the Jaya algorithm and Synergistic Swarm Optimization (SSO), our approach integrates a Levy flight mechanism to enhance exploration-exploitation trade-offs and improve convergence speed. The Jaya algorithm's ability to exploit the current best solutions is complemented by the SSO's collaborative search strategy, resulting in a synergistic optimization framework. Moreover, the incorporation of Levy flights injects stochasticity into the search process, enabling the algorithm to escape local optima and navigate complex solution spaces more effectively. We evaluate the proposed algorithm against state-of-the-art approaches using benchmark task scheduling problems in cloud environments. Experimental results demonstrate the superiority of our method in terms of solution quality, convergence speed, and scalability. Overall, our proposed Improved Jaya Synergistic Swarm Optimization Algorithm offers a promising solution for optimizing TSCC (TSCC), contributing to enhanced resource utilization and system performance in cloud-based applications. The proposed method got 88 % accuracy overall and 10 % enhancement compared to the original method.

SMITS: Social and Mobility aware Intelligent Task Scheduling in Vehicular Fog Computing — A Federated DRL Approach

Vehicular fog computing (VFC) has become an enticing research hot spot to provide resources for the Internet of Vehicles (IoV) application requests. Moreover, with the massive expansion of services in vehicular fog networks such as augmented reality, 3-D gaming, and autonomous driving, the allocation of fog computing resources to IoV applications constraining the deadline and energy consumption of the vehicles is becoming challenging. In this paper, we investigate a task scheduling problem in vehicular fog networks to jointly optimize the success rate of vehicular tasks and energy consumption of vehicles by considering the moving paths of the vehicles and the social relationships among the vehicles. In this problem, we model the social relationship among the vehicles based on their communication patterns and social characteristics to improve the success rate of the tasks. We also incorporate the mobility of vehicles using the Markov renewal process (MRP) technique. Initially, we formulate a mixed integer nonlinear programming (MINLP) problem for the proposed problem and further prove it to be an NP-hard problem. To solve the proposed problem, we design a federated deep reinforcement learning (FDRL) mechanism by converting the problem into a Markov decision process (MDP) problem. We compare our proposed algorithm with existing scheduling approaches, and simulation results show that the proposed algorithm performs efficiently for task scheduling problems. The proposed algorithm achieved a substantial improvement of 12% in the maximization of the success rate and 36% in the minimization of the energy consumption compared to the existing algorithms.

A scheduling optimization method for multiple automated guided vehicle systems

When constructing a multiple automated guided vehicle (multi-AGV) system, it is essential to calculate the potential values of the system’s feature parameters. This article presents two time-cost calculators and an improved simulation platform for scientifically estimating the cost of constructing a multi-AGV system. By inputting the parameters of the number of automated guided vehicles (AGVs), the velocity of the AGV, the number of tasks, scheduling interval and path length, the designed calculator can output the system’s time expense when all tasks are completed. The designed simulation platform can display a specific multi-AGV system’s time cost and how the entire progress works in a visual interface. An advanced scheduling method based on handover mode and A* algorithm is used to solve task scheduling problems in a multi-AGV system. The overall performance of the newly integrated scheduling system is compared with other scheduling systems to validate its superiority and shortcomings in different corresponding work scenarios. The final results present a robust solution to improve a multi-AGV system’s effectiveness.

Multi-UAV Cooperative Task Offloading and Resource Allocation in 5G Advanced and Beyond

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Optimizing Task Scheduling and Resource Allocation in Computing Environments using Metaheuristic Methods

Optimizing system performance in dynamic and heterogeneous environments and the efficient management of computational tasks are crucial. This paper therefore looks at task scheduling and resource allocation algorithms in some depth. The work evaluates five algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FA) and Simulated Annealing (SA) across various workloads achieved by varying the task-to-node ratio. The paper identifies Finish Time and Deadline as two key performance metrics for gauging the efficacy of an algorithm, and a comprehensive investigation of the behaviors of these algorithms across different workloads was carried out. Results from the experiments reveal unique patterns in algorithmic behaviors by workload. In the 15-task and 5-node scenario, the GA and PSO algorithms outclass all others, completing 100 percent of tasks before deadlines, Task 5 was a bane to the ACO algorithm. The study proposes a more extensive system that promotes an adaptive algorithmic approach based on workload characteristics. Numerically, the GA and PSO algorithms triumphed completing 100 percent of tasks before their deadlines in the face of 10 tasks and 5 nodes, while the ACO algorithm stumbled on certain tasks. As it is stated in the study, The above-mentioned system offers an integrated approach to ill-structured problem of task scheduling and resource allocation. It offers an intelligent and aggressive scheduling scheme that runs asynchronously when a higher number of tasks is submitted for the completion in addition to those dynamically aborts whenever system load and utilization cascade excessively. The proposed design seems like full-fledged solution over project scheduling or resource allocation issues. It highlights a detailed method of the choice of algorithms based on semantic features, aiming at flexibility. Effects of producing quantifiable statistical results from the experiments on performance empirically demonstrate each algorithm performed under various settings.

Nature inspired algorithms in dynamic task scheduling: A review

The process of scheduling involves allocating shared resources gradually so that tasks can be completed effectively within the allotted time. In task scheduling and resource allocation, the terms are used independently for tasks and resources, respectively. In computer science and operational management, scheduling is a hot topic. Efficient schedules guarantee system effectiveness, facilitate sound decision-making, reduce resource waste and expenses, and augment total productivity. Selecting the most accurate resources to complete work items and schedules for computing and business process execution is typically a laborious task. Particularly in dynamic real-world systems, where scheduling different dynamic tasks involves multiple tasks, is a difficult problem. Emerging technology known as "nature inspired algorithms" has the ability to dynamically solve the problem of optimal task and resource scheduling. This review paper discusses a study that looked at algorithms inspired by nature and used them to schedule tasks dynamically. The Nature Inspired Algorithms used in dynamic task scheduling and a comparative analysis of those methods are used in this paper to address the study's findings.

Task scheduling for heterogeneous agents pickup and delivery using recurrent open shop scheduling models

We study the transport-pick agents task scheduling (TPTS) problem in heterogeneous agents pickup and delivery (HAPD). Two functionally heterogeneous agent types, transport agents and pick agents, collaborate to execute multi-goal tasks subjecting to complex-schedule dependency. The objective is to plan a collective time-extended task schedule with the minimization of total completion time. To bridge the gap between robot task scheduling and general scheduling theory, a novel recurrent open shop scheduling (ROSS) problem variant with unique sequence structure is defined. New sequence and schedule models are extended to accommodate for it. Afterwards, the problem-specific append-beam-Christofides (ABC) constructive heuristic, greedy local search (GLS) and simulated annealing (SA) metaheuristic algorithms are designed accordingly. Theoretically, we rigorously analyze sequence and schedule structures, and algorithmic properties; empirically, we study the influence of different algorithm settings on a comprehensive dataset. Design guidelines and parameter settings of these algorithms are provided. The application conditions of the proposed methodology is discussed along with a baseline algorithm TEAMWISE. The proposed methodology could be utilized in various industrial heterogeneous multi-robot or collaborative human–robot systems.

An intelligent and interpretable rule-based metaheuristic approach to task scheduling in cloud systems

Metaheuristic algorithms can be very successful in solving scheduling problems. However, these methods can be slow for time-critical applications due to the iterative stochastic processes they perform. This problem becomes even more pronounced in dynamic environments such as cloud systems. This paper proposes an interpretable rule-based solution to minimize this problem. Combining metaheuristic task scheduling solutions and machine learning techniques, this method uses a two-phase mechanism, semi-offline and online. In the semi-offline phase, we first archive metaheuristic solutions to previously randomly generated or encountered task-scheduling problems. The basic idea of the proposed method is to reuse these previously obtained successful metaheuristic solution patterns for future similar problems. Finding similar solution patterns from this extensive archive dataset is done by automatically extracting rule sets through machine learning techniques. These interpretable rule sets are used to identify the type of task scheduling problem encountered in the online phase and to find the optimal solution pattern. The performance of this method, which dramatically reduces execution time and enables the use of metaheuristics in time-critical applications, has been tested and proven for various cloud task-scheduling scenarios.

Collaborative Scheduling Algorithm for Dependent Tasks based on Multi-Agent RL in VEC Environment

To address the problem of limited computing power of vehicles, Vehicle Edge Computing (VEC) allows vehicles to schedule tasks to edge nodes with sufficient resources. In this paper, we propose a multi-agent reinforcement learning (MARL) approach to solve the multi-task scheduling problem in a dynamic VEC environment. First, we model the cooperative scheduling problem of dependent tasks in the VEC environment, considering the task priority and edge node load balancing in the task scheduling process. We define the optimization objective as minimizing the task processing delay and find it is an NP-hard problem. Then, we design a distributed algorithm SCMA based on MARL. The algorithm enables vehicles to find the optimal scheduling strategy by cooperating and sharing resources with each other. Finally, we use SUMO to simulate the road network topology and generate vehicle traffic trajectories. We construct heterogeneous vehicular applications for simulation experiments using the DAG generator. Compared with existing algorithms, the simulation results validate the superiority of the SCMA algorithm.

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

AbstractIn a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.

ACO intelligent task scheduling algorithm based on Q-learning optimization in a multilayer cognitive radio platform

With the rapid development of cognitive radio technology, multilayer heterogeneous cognitive radio computing platforms with large computing, high-throughput, ultralarge bandwidth and ultralow latency have become a research hotspot. Aiming at the core scheduling problems of multilayer heterogeneous computing platforms, this paper abstracts the bidirectional interconnection topology, node computing capacity, and internode communication capability of the heterogeneous computing platform into an undirected graph model and abstracts the nodes with dependencies, nodes’ computing requirements, and internode communication requirements in streaming tasks into a directed acyclic graph (DAG) model so as to transform the task-scheduling problem into a deployment-scheduling problem from DAG to undirected graph. To efficiently solve this graph model, this paper calculates and forms a component scheduling sequence based on the dependencies of functional components in streaming domain tasks. Then, according to the scheduling sequence, ant colony optimization (ACO) algorithms, such as ant colonies and Q-learning select functional components, deploy components to different computing nodes, calculate the scheduling cost, guide the solution space search of agents, and complete the scenario migration adaptation of the scheduling algorithms to intelligent scheduling of domain tasks. So, this paper proposes the ACO field task intelligent scheduling algorithm based on Q-learning optimization (QACO). QACO uses the Q-table matrix of Q-learning as the initial pheromone of the ant colony algorithm, which not only solves the dimensional disaster of the Q-learning algorithm but also accelerates the convergence speed of the ant colony intelligent scheduling algorithm, reduces the task scheduling length, and further enhances the search ability of the existing scheduling algorithm to solve the spatial set. Based on the randomly generated DAG domain task map, three experimental test scenarios are designed to verify the algorithm performance. The experimental results show that compared with the Q-learning, ACO, and genetic algorithms (GA) algorithms, the proposed algorithm improves the convergence speed of the solution by 72.3%, 63.4%, and 64% on average, reduces the scheduling length by 2.8%, 2.2%, and 0.9% on average, and increases the parallel acceleration ratio by 2.8%, 2.1%, and 0.9% on average, respectively. The practical application value of the algorithm is analyzed through typical radar task simulation, but the load balancing of the algorithm needs to be further improved.

Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan.

Optimising stochastic task allocation and scheduling plans for mission workers subject to learning-forgetting, fatigue-recovery, and stress-recovery effects

This study addresses the stochastic bi-objective task allocation and scheduling problems for mission workers subject to the complex (and joint) effect of learning-forgetting, fatigue-recovery, and stress-recovery processes. The mission consists of work-rest cycles with different types of tasks. Some task types are repetitive, not necessarily back-to-back, with some predecessors for other task types. The workers are multi-skilled, whose experience, learning, fatigue, and stress levels are updated, affecting their performance dynamically. A Markov Decision Process (MDP) is applied to formulate this stochastic problem, considering speed and accuracy as two measures of workforce performance. A decision could be “to repeat a task of a certain type” or “take a rest break till another worker becomes available”. The developed MDP model finds the optimal task allocation and work-break schedule for workers by minimising the sum of the tasks’ completion times and maximising their quality. Completion time is a continuous variable, and the quality index is a binary random variable, i.e., high or moderate, having a continuous probability of occurrence. The model is of a general form with a potential application in similar settings. The paper used the Sequential Greedy Assignment (SGA) and the Monte-Carlo Tree Search (MCTS) to solve the problem with their results compared. Numerical results with those from a sensitivity analysis are discussed.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.