Real-time task scheduling is of primary significance in multiprocessor systems.Meeting deadlines and achieving high system utilization are the two main objectives of task scheduling in such systems.In this paper,we re...Real-time task scheduling is of primary significance in multiprocessor systems.Meeting deadlines and achieving high system utilization are the two main objectives of task scheduling in such systems.In this paper,we represent those two goals as the minimization of the average response time and the average task laxity.To achieve this,we propose a genetic-based algorithm with problem-specific and efficient genetic operators.Adaptive control parameters are also employed in our work to improve the genetic algorithms' efficiency.The simulation results show that our proposed algorithm outperforms its counterpart considerably by up to 36% and 35% in terms of the average response time and the average task laxity,respectively.展开更多
The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent t...The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.展开更多
Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to t...Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.展开更多
μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is propos...μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is proposed. The basic idea is adding round-robin scheduling strategy in its original scheduler in order to schedule homology priority tasks through time slice roundrobin. Implementation approach is given in detail. Firstly, the Task Control Block （TCB） is extended. And then, a new priority index table is created, in which each index pointer points to a set of homology priority tasks. Eventually, on the basis of reconstructing μC/OS-Ⅱ real-time kernel, task scheduling module is rewritten. Otherwise, schedulability of homology task supported by modified kernel had been analyzed, and deadline formula of created homology tasks is given. By theoretical analysis and experiment verification, the modified kernel can support homology priority tasks scheduling, meanwhile, it also remains preemptive property of original μC/OS-Ⅱ.展开更多
To fulfill the requirements for hybrid real-time system scheduling, a long-release-interval-first （LRIF） real-time scheduling algorithm is proposed. The algorithm adopts both the fixed priority and the dynamic prior...To fulfill the requirements for hybrid real-time system scheduling, a long-release-interval-first （LRIF） real-time scheduling algorithm is proposed. The algorithm adopts both the fixed priority and the dynamic priority to assign priorities for tasks. By assigning higher priorities to the aperiodic soft real-time jobs with longer release intervals, it guarantees the executions for periodic hard real-time tasks and further probabilistically guarantees the executions for aperiodic soft real-time tasks. The schedulability test approach for the LRIF algorithm is presented. The implementation issues of the LRIF algorithm are also discussed. Simulation result shows that LRIF obtains better schedulable performance than the maximum urgency first （MUF） algorithm, the earliest deadline first （EDF） algorithm and EDF for hybrid tasks. LRIF has great capability to schedule both periodic hard real-time and aperiodic soft real-time tasks.展开更多
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
With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the ...With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.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
With the widespread adoption of unmanned aerial vehicle(UAV)technology,task scheduling for UAV swarms has become a crucial approach to improve operational efficiency.Most existing studies oversimplify the operational ...With the widespread adoption of unmanned aerial vehicle(UAV)technology,task scheduling for UAV swarms has become a crucial approach to improve operational efficiency.Most existing studies oversimplify the operational process rules of UAVs,making it difficult to accurately characterize the adaptability differences of UAVs to various tasks under practical operational constraints.To address this limitation,this paper proposes a UAV swarm task scheduling problem with limited communication range(UAVS-LCR)and establishes an integer programming model for its formal description.For solving this problem,a multi-neighborhood iterative local search(MNILS)algorithm is designed,which adopts a doubly linked list solution representation method to reduce the computational complexity of basic neighborhood operations.This algorithm generates high-quality initial solutions via a greedy construction strategy,combines insertion search,multi-swap search and the two-opt operator to enable alternating exploration across multiple neighborhoods,and incorporates a simulated annealing mechanism to balance search efficiency and solution diversity.This method can provide an effective solution for various application scenarios including wide-area UAV inspection and heterogeneous UAV collaborative operations.Experimental results on 12 power grid maintenance test instances demonstrate that the MNILS algorithm significantly outperforms the genetic algorithm,the artificial bee colony algorithm,the ant colony optimization algorithm and the variable neighborhood search algorithm in terms of both solution quality and scalability for large-scale problems.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
In the context of real-time fault-tolerant scheduling in multiprocessor systems, Primary-backup scheme plays an important role. A backup copy is always preferred to be executed as passive backup copy whenever possible...In the context of real-time fault-tolerant scheduling in multiprocessor systems, Primary-backup scheme plays an important role. A backup copy is always preferred to be executed as passive backup copy whenever possible because it can take the advantages of backup copy de-allocation technique and overloading technique to improve schedulability. In this paper, we propose a novel efficient fault-tolerant ratemonotonic best-fit algorithm efficient fault-tolerant rate-monotonic best-fit （ERMBF） based on multiprocessors systems to enhance the schedulability. Unlike existing scheduling algorithms that start scheduling tasks with only one processor. ERMBF pre-allocates a certain amount of processors before starting scheduling tasks, which enlarge the searching spaces for tasks. Besides, when a new processor is allocated, we reassign the task copies that have already been assigned to the existing processors in order to find a superior tasks assignment configuration. These two strategies are all aiming at making as many backup copies as possible to be executed as passive status. As a result, ERMBF can use fewer processors to schedule a set of tasks without losing real-time and fault-tolerant capabilities of the system. Simulation results reveal that ERMBF significantly improves the schedulability over existing, comparable algorithms in literature.展开更多
Real-time scheduling as an on-line optimization process must output dispatch results in real time. However, the calculation time required and the economy have a trade-off relationship. In response to a real-time sched...Real-time scheduling as an on-line optimization process must output dispatch results in real time. However, the calculation time required and the economy have a trade-off relationship. In response to a real-time scheduling problem, this paper proposes a real-time scheduling strategy considering the operation interval division of distributed generators(DGs) and batteries in the microgrid. Rolling scheduling models, including day-ahead scheduling and hours-ahead scheduling, are established, where the latter considers the future state-of-charge deviations. For the real-time scheduling, the output powers of the DGs are divided into two intervals based on the ability to track the day-ahead and hours-ahead schedules. The day-ahead and hours-ahead scheduling ensure the economy, whereas the real-time scheduling overcomes the timeconsumption problem. Finally, a grid-connected microgrid example is studied, and the simulation results demonstrate the effectiveness of the proposed strategy in terms of economic and real-time requirements.展开更多
Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling ...Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling for semiconductor wafer fab is proposed.The relevant algorithm, pheromone-based dynamic real-time scheduling algorithm (PBDR), is given.MIMAC test bed data set mini-fab is used to compare PBDR with FIFO (first in first out),SRPT(shortest remaining processing time) and CR(critical ratio) under three different release rules,i.e. deterministic rule, Poisson rule and CONWIP (constant WIP). It is shown that PBDR is prior toFIFO, SRPT and CR with better performance of cycle time, throughput, and on-time delivery,especially for on-time delivery performance.展开更多
Abstract-The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Fir...Abstract-The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Firstly, an objective function is designed based on the constraint, and a base load forecasting model is established when aggregating renewable generation and non-deferrable load into a power system, which aims to transform the problem of deferrable loads scheduling into a distributed optimal control problem. Then, to optimize the objective function, a real-time scheduling algorithm is presented to solve the proposed control problem. At every time step, the purpose is to minimize the variance of differences between power supply and aggregate load, which can thus ensure the effective utilization of power resources. Finally, simulation examples are provided to illustrate the effectiveness of the proposed algorithm.展开更多
Considering the disadvantage of first-fit strategy in fault-tolerant rate-monotonic first-fit （FTRMFF） algorithm, we analyze the slack time of processors and the schedulability of periodic tasks in rate-monotonic ...Considering the disadvantage of first-fit strategy in fault-tolerant rate-monotonic first-fit （FTRMFF） algorithm, we analyze the slack time of processors and the schedulability of periodic tasks in rate-monotonic （RM） algorithm. Then, the RM-based idleness factor and compact factor are presented to quantify the compact degree of tasks assigned to the same processor. In this paper, the novel fault-tolerant rate-monotonic compact-factor-driven （FTRMCFD） algorithm, which follows the principle of compact factor maximal when allocating the processors for tasks, is proposed. FTRMCFD algorithm makes every processor contain more tasks and get higher utilization to increase the schedulability performance of distributed systems. The simulation experiments reveal that FTRMCFD can reduce the number of required processors by up to 11.5% （with an average of 5.3%）.展开更多
The existing scheduling algorithms cannot adequately support modern embedded real-time applications. An important challenge for future research is how to model and introduce control mechanisms to real-time systems to ...The existing scheduling algorithms cannot adequately support modern embedded real-time applications. An important challenge for future research is how to model and introduce control mechanisms to real-time systems to improve real-time performance, and to allow the system to adapt to changes in the environment, the workload, or to changes in the system architecture due to failures. In this paper, we pursue this goal by formulating and simulating new real-time scheduling models that enable us to easily analyse feedback scheduling with various constraints, overload and disturbance, and by designing a robust, adaptive scheduler that responds gracefully to overload with robust H∞ and feedback error learning control.展开更多
By combining fault-tolerance with power management, this paper developed a new method for aperiodic task set for the problem of task scheduling and voltage allocation in embedded real-time systems. The scbedulability ...By combining fault-tolerance with power management, this paper developed a new method for aperiodic task set for the problem of task scheduling and voltage allocation in embedded real-time systems. The scbedulability of the system was analyzed through checkpointing and the energy saving was considered via dynamic voltage and frequency scaling. Simulation results showed that the proposed algorithm had better performance compared with the existing voltage allocation techniques. The proposed technique saves 51.5% energy over FT-Only and 19.9% over FT ＋ EC on average. Therefore, the proposed method was more appropriate for aperiodic tasks in embedded real-time systems.展开更多
文摘Real-time task scheduling is of primary significance in multiprocessor systems.Meeting deadlines and achieving high system utilization are the two main objectives of task scheduling in such systems.In this paper,we represent those two goals as the minimization of the average response time and the average task laxity.To achieve this,we propose a genetic-based algorithm with problem-specific and efficient genetic operators.Adaptive control parameters are also employed in our work to improve the genetic algorithms' efficiency.The simulation results show that our proposed algorithm outperforms its counterpart considerably by up to 36% and 35% in terms of the average response time and the average task laxity,respectively.
基金supported by the National Natural Science Foundation of China(72001212,71701204,71801218)the China Hunan Postgraduate Research Innovating Project(CX2018B020)。
文摘The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.
基金Project (60505018) supported by the National Natural Science Foundation of China
文摘Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.
基金Supported by the "Chunhui" Plan of Ministry of Education of China (Z2005-2-11013)
文摘μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is proposed. The basic idea is adding round-robin scheduling strategy in its original scheduler in order to schedule homology priority tasks through time slice roundrobin. Implementation approach is given in detail. Firstly, the Task Control Block （TCB） is extended. And then, a new priority index table is created, in which each index pointer points to a set of homology priority tasks. Eventually, on the basis of reconstructing μC/OS-Ⅱ real-time kernel, task scheduling module is rewritten. Otherwise, schedulability of homology task supported by modified kernel had been analyzed, and deadline formula of created homology tasks is given. By theoretical analysis and experiment verification, the modified kernel can support homology priority tasks scheduling, meanwhile, it also remains preemptive property of original μC/OS-Ⅱ.
基金The Natural Science Foundation of Jiangsu Province(NoBK2005408)
文摘To fulfill the requirements for hybrid real-time system scheduling, a long-release-interval-first （LRIF） real-time scheduling algorithm is proposed. The algorithm adopts both the fixed priority and the dynamic priority to assign priorities for tasks. By assigning higher priorities to the aperiodic soft real-time jobs with longer release intervals, it guarantees the executions for periodic hard real-time tasks and further probabilistically guarantees the executions for aperiodic soft real-time tasks. The schedulability test approach for the LRIF algorithm is presented. The implementation issues of the LRIF algorithm are also discussed. Simulation result shows that LRIF obtains better schedulable performance than the maximum urgency first （MUF） algorithm, the earliest deadline first （EDF） algorithm and EDF for hybrid tasks. LRIF has great capability to schedule both periodic hard real-time and aperiodic soft real-time tasks.
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.
基金supported by the National Key Research and Development Program of China(2020YFB1807700)Innovation Capability Support Program of Shaanxi(2024RS-CXTD-01).
文摘With the rapid advancement of satellite communication technologies,space information networks(SINs)have become essential infrastructure for complex service delivery and cross-domain task coordination,facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space,ground,and user segments.This study presents a novel intent-driven task-oriented network(IDTN)framework to address task scheduling and resource allocation challenges in SINs.The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments.To manage the variability of random task arrivals and dynamic resources,a context-aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty.Simulation results demonstrate the effectiveness of the proposed IDTN framework.Compared with conventional baseline methods,the framework achieves significant performance improvements,including a 4.4%-28.9%increase in average system reward,a 6.2%-34.5%improvement in resource utilization,and a 5.6%-35.7%enhancement in user satisfaction.The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.
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 Project Social Science Foundation Jiangsu Province(No.22GLB026)2025 National Major Project for Logistics Education Reform and Research in Higher Education and Vocational Colleges(No.JZW2025002)。
文摘With the widespread adoption of unmanned aerial vehicle(UAV)technology,task scheduling for UAV swarms has become a crucial approach to improve operational efficiency.Most existing studies oversimplify the operational process rules of UAVs,making it difficult to accurately characterize the adaptability differences of UAVs to various tasks under practical operational constraints.To address this limitation,this paper proposes a UAV swarm task scheduling problem with limited communication range(UAVS-LCR)and establishes an integer programming model for its formal description.For solving this problem,a multi-neighborhood iterative local search(MNILS)algorithm is designed,which adopts a doubly linked list solution representation method to reduce the computational complexity of basic neighborhood operations.This algorithm generates high-quality initial solutions via a greedy construction strategy,combines insertion search,multi-swap search and the two-opt operator to enable alternating exploration across multiple neighborhoods,and incorporates a simulated annealing mechanism to balance search efficiency and solution diversity.This method can provide an effective solution for various application scenarios including wide-area UAV inspection and heterogeneous UAV collaborative operations.Experimental results on 12 power grid maintenance test instances demonstrate that the MNILS algorithm significantly outperforms the genetic algorithm,the artificial bee colony algorithm,the ant colony optimization algorithm and the variable neighborhood search algorithm in terms of both solution quality and scalability for large-scale problems.
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 the National Basic Reseach Program of China (973 Program 2004 CB318200)
文摘In the context of real-time fault-tolerant scheduling in multiprocessor systems, Primary-backup scheme plays an important role. A backup copy is always preferred to be executed as passive backup copy whenever possible because it can take the advantages of backup copy de-allocation technique and overloading technique to improve schedulability. In this paper, we propose a novel efficient fault-tolerant ratemonotonic best-fit algorithm efficient fault-tolerant rate-monotonic best-fit （ERMBF） based on multiprocessors systems to enhance the schedulability. Unlike existing scheduling algorithms that start scheduling tasks with only one processor. ERMBF pre-allocates a certain amount of processors before starting scheduling tasks, which enlarge the searching spaces for tasks. Besides, when a new processor is allocated, we reassign the task copies that have already been assigned to the existing processors in order to find a superior tasks assignment configuration. These two strategies are all aiming at making as many backup copies as possible to be executed as passive status. As a result, ERMBF can use fewer processors to schedule a set of tasks without losing real-time and fault-tolerant capabilities of the system. Simulation results reveal that ERMBF significantly improves the schedulability over existing, comparable algorithms in literature.
基金supported by the National Key R&D Program of China (2018YFA0702200)the Fundamental Research Funds of Shandong University。
文摘Real-time scheduling as an on-line optimization process must output dispatch results in real time. However, the calculation time required and the economy have a trade-off relationship. In response to a real-time scheduling problem, this paper proposes a real-time scheduling strategy considering the operation interval division of distributed generators(DGs) and batteries in the microgrid. Rolling scheduling models, including day-ahead scheduling and hours-ahead scheduling, are established, where the latter considers the future state-of-charge deviations. For the real-time scheduling, the output powers of the DGs are divided into two intervals based on the ability to track the day-ahead and hours-ahead schedules. The day-ahead and hours-ahead scheduling ensure the economy, whereas the real-time scheduling overcomes the timeconsumption problem. Finally, a grid-connected microgrid example is studied, and the simulation results demonstrate the effectiveness of the proposed strategy in terms of economic and real-time requirements.
基金This project is supported by National 973 Project of China (No.2002-CB312202) National Natural Science Foundation of China (No.60374005, No.60104004) Chinese Postdoctoral Fellowship Foundation.
文摘Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling for semiconductor wafer fab is proposed.The relevant algorithm, pheromone-based dynamic real-time scheduling algorithm (PBDR), is given.MIMAC test bed data set mini-fab is used to compare PBDR with FIFO (first in first out),SRPT(shortest remaining processing time) and CR(critical ratio) under three different release rules,i.e. deterministic rule, Poisson rule and CONWIP (constant WIP). It is shown that PBDR is prior toFIFO, SRPT and CR with better performance of cycle time, throughput, and on-time delivery,especially for on-time delivery performance.
文摘Abstract-The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Firstly, an objective function is designed based on the constraint, and a base load forecasting model is established when aggregating renewable generation and non-deferrable load into a power system, which aims to transform the problem of deferrable loads scheduling into a distributed optimal control problem. Then, to optimize the objective function, a real-time scheduling algorithm is presented to solve the proposed control problem. At every time step, the purpose is to minimize the variance of differences between power supply and aggregate load, which can thus ensure the effective utilization of power resources. Finally, simulation examples are provided to illustrate the effectiveness of the proposed algorithm.
基金Supported by the National Natural Science Foundation of China (60603032)
文摘Considering the disadvantage of first-fit strategy in fault-tolerant rate-monotonic first-fit （FTRMFF） algorithm, we analyze the slack time of processors and the schedulability of periodic tasks in rate-monotonic （RM） algorithm. Then, the RM-based idleness factor and compact factor are presented to quantify the compact degree of tasks assigned to the same processor. In this paper, the novel fault-tolerant rate-monotonic compact-factor-driven （FTRMCFD） algorithm, which follows the principle of compact factor maximal when allocating the processors for tasks, is proposed. FTRMCFD algorithm makes every processor contain more tasks and get higher utilization to increase the schedulability performance of distributed systems. The simulation experiments reveal that FTRMCFD can reduce the number of required processors by up to 11.5% （with an average of 5.3%）.
文摘The existing scheduling algorithms cannot adequately support modern embedded real-time applications. An important challenge for future research is how to model and introduce control mechanisms to real-time systems to improve real-time performance, and to allow the system to adapt to changes in the environment, the workload, or to changes in the system architecture due to failures. In this paper, we pursue this goal by formulating and simulating new real-time scheduling models that enable us to easily analyse feedback scheduling with various constraints, overload and disturbance, and by designing a robust, adaptive scheduler that responds gracefully to overload with robust H∞ and feedback error learning control.
基金The National Natural Science Foundationof China(No.60873030 )the National High-Tech Research and Development Plan of China(863 Program)(No.2007AA01Z309)
文摘By combining fault-tolerance with power management, this paper developed a new method for aperiodic task set for the problem of task scheduling and voltage allocation in embedded real-time systems. The scbedulability of the system was analyzed through checkpointing and the energy saving was considered via dynamic voltage and frequency scaling. Simulation results showed that the proposed algorithm had better performance compared with the existing voltage allocation techniques. The proposed technique saves 51.5% energy over FT-Only and 19.9% over FT ＋ EC on average. Therefore, the proposed method was more appropriate for aperiodic tasks in embedded real-time systems.
