Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling...Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.展开更多
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource...The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.展开更多
Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been prop...Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.展开更多
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task ...In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often overlooked.It is frequently assumed that vehicles can be accurately modeled during actual motion processes.However,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios.Taking into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading process.The optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming problem.Due to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission.Finally,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization problem.Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading.Meanwhile,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).展开更多
More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud com...More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.展开更多
A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process u...A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.展开更多
This paper reviews task scheduling frameworks,methods,and evaluation metrics of central processing unit-graphics processing unit(CPU-GPU)heterogeneous clusters.Task scheduling of CPU-GPU heterogeneous clusters can be ...This paper reviews task scheduling frameworks,methods,and evaluation metrics of central processing unit-graphics processing unit(CPU-GPU)heterogeneous clusters.Task scheduling of CPU-GPU heterogeneous clusters can be carried out on the system level,nodelevel,and device level.Most task-scheduling technologies are heuristic based on the experts’experience,while some technologies are based on statistic methods using machine learning,deep learning,or reinforcement learning.Many metrics have been adopted to evaluate and compare different task scheduling technologies that try to optimize different goals of task scheduling.Although statistic task scheduling has reached fewer research achievements than heuristic task scheduling,the statistic task scheduling still has significant research potential.展开更多
Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and r...Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and resource management becomes paramount.At the core of this efficiency lies task scheduling,a complex process that determines how tasks are allocated and executed across cloud resources.While extensive research has been conducted in the area of task scheduling,optimizing multiple objectives simultaneously remains a significant challenge due to the NP(Non-deterministic Polynomial)Complete nature of the problem.This study aims to address these challenges by providing a comprehensive review and experimental analysis of task scheduling approaches,with a particular focus on hybrid techniques that offer promising solutions.Utilizing the CloudSim simulation toolkit,we evaluated the performance of three hybrid algorithms:Estimation of Distribution Algorithm-Genetic Algorithm(EDA-GA),Hybrid Genetic Algorithm-Ant Colony Optimization(HGA-ACO),and Improved Discrete Particle Swarm Optimization(IDPSO).Our experimental results demonstrate that these hybrid methods significantly outperform traditional standalone algorithms in reducing Makespan,which is a critical measure of task completion time.Notably,the IDPSO algorithm exhibited superior performance,achieving a Makespan of just 0.64 milliseconds for a set of 150 tasks.These findings underscore the potential of hybrid algorithms to enhance task scheduling efficiency in cloud computing environments.This paper concludes with a discussion of the implications of our findings and offers recommendations for future research aimed at further improving task scheduling strategies,particularly in the context of increasingly complex and dynamic cloud environments.展开更多
To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony sy...To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.展开更多
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.展开更多
In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem i...In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.展开更多
Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of...Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.展开更多
A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval(SI) time, which is the update interval of the radar invoking the schedu...A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval(SI) time, which is the update interval of the radar invoking the scheduling algorithm, to be a fixed value,it is modeled as a fuzzy set to improve the scheduling flexibility.The scheduling algorithm exploits the fuzzy set model in order to intelligently adjust the SI time. The idle time in other SIs is provided for SIs which will be overload. Thereby more request tasks can be accommodated. The simulation results show that the proposed algorithm improves the successful scheduling ratio by 16%,the threat ratio of execution by 16% and the time utilization ratio by 15% compared with the highest task mode priority first(HPF)algorithm.展开更多
The rise of multi-cloud systems has been spurred.For safety-critical missions,it is important to guarantee their security and reliability.To address trust constraints in a heterogeneous multi-cloud environment,this wo...The rise of multi-cloud systems has been spurred.For safety-critical missions,it is important to guarantee their security and reliability.To address trust constraints in a heterogeneous multi-cloud environment,this work proposes a novel scheduling method called matching and multi-round allocation(MMA)to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints.The method is divided into two phases for task scheduling.The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance,security,and reliability in a multi-cloud environment;the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time.The proposed algorithm,the modified cuckoo search(MCS),hybrid chaotic particle search(HCPS),modified artificial bee colony(MABC),max-min,and min-min algorithms are implemented in CloudSim to create simulations.The simulations and experimental results show that our proposed method achieves shorter makespan,lower cost,higher resource utilization,and better trade-off between time and economic cost.It is more stable and efficient.展开更多
How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data cente...How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.展开更多
Cloud-as-the-center computing paradigms face multiple challenges in the 5G and Internet of Things scenarios, where the service requests are usually initiated by the end-user devices located at network edge and have ri...Cloud-as-the-center computing paradigms face multiple challenges in the 5G and Internet of Things scenarios, where the service requests are usually initiated by the end-user devices located at network edge and have rigid time constraints. Therefore, Fog computing, or mobile edge computing, is introduced as a promising solution to the service provision in the tiered IoT infrastructure to compensate the shortage of traditional cloud-only architecture. In this cloud-to-things continuum, several cloudlet or mobile edge server entities are placed at the access network to handle the task offloading and processing problems at the network edge. This raises the resource scheduling problem in this tiered system, which is vital for the promotion of the system efficiency. Therefore, in this paper, a scheduling mechanism for the cloudlets or fog nodes are presented, which takes the mobile tasks’ deadline and resources requirements at the same time while promoting the overall profit of the system. First, the problem at the cloudlet, to which IoT devices offload their tasks, is formulated as a multi-dimensional 0-1 knapsack problem. Second, based on ant colony optimization, a scheduling algorithm is presented which treat this problem as a subset selection problem. Third, to promote the performance of the system in the dynamic environments,a churn-refined algorithm is further put forward. A series of simulation experiments have shown that out proposal outperforms many state-of-the-art algorithms in both profit and guarantee ratio.展开更多
How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we ca...How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
基金the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567).
文摘Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.
文摘The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.
基金supported by the National Natural Science Foundation of China (52275480)the Guizhou Provincial Science and Technology Program of Qiankehe Zhongdi Guiding ([2023]02)+1 种基金the Guizhou Provincial Science and Technology Program of Qiankehe Platform Talent Project (GCC[2023]001)the Guizhou Provincial Science and Technology Project of Qiankehe Platform Project (KXJZ[2024]002).
文摘Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
文摘In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often overlooked.It is frequently assumed that vehicles can be accurately modeled during actual motion processes.However,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios.Taking into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading process.The optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming problem.Due to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission.Finally,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization problem.Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading.Meanwhile,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).
基金in part by the Hubei Natural Science and Research Project under Grant 2020418in part by the 2021 Light of Taihu Science and Technology Projectin part by the 2022 Wuxi Science and Technology Innovation and Entrepreneurship Program.
文摘More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.
基金This work was supported by the National Key Research and Development Program of China(2021YFB2900603)the National Natural Science Foundation of China(61831008).
文摘A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.
基金supported by ZTE‑University‑Institute Fund Project under Grant No.IA20230629009.
文摘This paper reviews task scheduling frameworks,methods,and evaluation metrics of central processing unit-graphics processing unit(CPU-GPU)heterogeneous clusters.Task scheduling of CPU-GPU heterogeneous clusters can be carried out on the system level,nodelevel,and device level.Most task-scheduling technologies are heuristic based on the experts’experience,while some technologies are based on statistic methods using machine learning,deep learning,or reinforcement learning.Many metrics have been adopted to evaluate and compare different task scheduling technologies that try to optimize different goals of task scheduling.Although statistic task scheduling has reached fewer research achievements than heuristic task scheduling,the statistic task scheduling still has significant research potential.
文摘Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and resource management becomes paramount.At the core of this efficiency lies task scheduling,a complex process that determines how tasks are allocated and executed across cloud resources.While extensive research has been conducted in the area of task scheduling,optimizing multiple objectives simultaneously remains a significant challenge due to the NP(Non-deterministic Polynomial)Complete nature of the problem.This study aims to address these challenges by providing a comprehensive review and experimental analysis of task scheduling approaches,with a particular focus on hybrid techniques that offer promising solutions.Utilizing the CloudSim simulation toolkit,we evaluated the performance of three hybrid algorithms:Estimation of Distribution Algorithm-Genetic Algorithm(EDA-GA),Hybrid Genetic Algorithm-Ant Colony Optimization(HGA-ACO),and Improved Discrete Particle Swarm Optimization(IDPSO).Our experimental results demonstrate that these hybrid methods significantly outperform traditional standalone algorithms in reducing Makespan,which is a critical measure of task completion time.Notably,the IDPSO algorithm exhibited superior performance,achieving a Makespan of just 0.64 milliseconds for a set of 150 tasks.These findings underscore the potential of hybrid algorithms to enhance task scheduling efficiency in cloud computing environments.This paper concludes with a discussion of the implications of our findings and offers recommendations for future research aimed at further improving task scheduling strategies,particularly in the context of increasingly complex and dynamic cloud environments.
文摘To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.
基金supported by the National Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
基金Supported by the National Basic ResearchProgramof China (973 Program2003CB314804)
文摘In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.
文摘Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.
基金supported by the National Youth Foundation(61503408)
文摘A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval(SI) time, which is the update interval of the radar invoking the scheduling algorithm, to be a fixed value,it is modeled as a fuzzy set to improve the scheduling flexibility.The scheduling algorithm exploits the fuzzy set model in order to intelligently adjust the SI time. The idle time in other SIs is provided for SIs which will be overload. Thereby more request tasks can be accommodated. The simulation results show that the proposed algorithm improves the successful scheduling ratio by 16%,the threat ratio of execution by 16% and the time utilization ratio by 15% compared with the highest task mode priority first(HPF)algorithm.
基金supported in part by the National Natural Science Foundation of China(61673123,61603100)in part by the Natural Science Foundation of Guangdong Province,China(2020A151501482)。
文摘The rise of multi-cloud systems has been spurred.For safety-critical missions,it is important to guarantee their security and reliability.To address trust constraints in a heterogeneous multi-cloud environment,this work proposes a novel scheduling method called matching and multi-round allocation(MMA)to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints.The method is divided into two phases for task scheduling.The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance,security,and reliability in a multi-cloud environment;the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time.The proposed algorithm,the modified cuckoo search(MCS),hybrid chaotic particle search(HCPS),modified artificial bee colony(MABC),max-min,and min-min algorithms are implemented in CloudSim to create simulations.The simulations and experimental results show that our proposed method achieves shorter makespan,lower cost,higher resource utilization,and better trade-off between time and economic cost.It is more stable and efficient.
基金supported by the National Natural Science Foundation of China(6120200461272084)+9 种基金the National Key Basic Research Program of China(973 Program)(2011CB302903)the Specialized Research Fund for the Doctoral Program of Higher Education(2009322312000120113223110003)the China Postdoctoral Science Foundation Funded Project(2011M5000952012T50514)the Natural Science Foundation of Jiangsu Province(BK2011754BK2009426)the Jiangsu Postdoctoral Science Foundation Funded Project(1102103C)the Natural Science Fund of Higher Education of Jiangsu Province(12KJB520007)the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(yx002001)
文摘How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.
文摘Cloud-as-the-center computing paradigms face multiple challenges in the 5G and Internet of Things scenarios, where the service requests are usually initiated by the end-user devices located at network edge and have rigid time constraints. Therefore, Fog computing, or mobile edge computing, is introduced as a promising solution to the service provision in the tiered IoT infrastructure to compensate the shortage of traditional cloud-only architecture. In this cloud-to-things continuum, several cloudlet or mobile edge server entities are placed at the access network to handle the task offloading and processing problems at the network edge. This raises the resource scheduling problem in this tiered system, which is vital for the promotion of the system efficiency. Therefore, in this paper, a scheduling mechanism for the cloudlets or fog nodes are presented, which takes the mobile tasks’ deadline and resources requirements at the same time while promoting the overall profit of the system. First, the problem at the cloudlet, to which IoT devices offload their tasks, is formulated as a multi-dimensional 0-1 knapsack problem. Second, based on ant colony optimization, a scheduling algorithm is presented which treat this problem as a subset selection problem. Third, to promote the performance of the system in the dynamic environments,a churn-refined algorithm is further put forward. A series of simulation experiments have shown that out proposal outperforms many state-of-the-art algorithms in both profit and guarantee ratio.
基金supported by the National Natural Science Foundation of China(71401131)the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(13XJC630011)the Ministry of Education Research Fund for the Doctoral Program of Higher Education(20120184120040)
文摘How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.
