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Providing Robust and Low-Cost Edge Computing in Smart Grid:An Energy Harvesting Based Task Scheduling and Resource Management Framework 认领 引用 被引量：2
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作者 Xie Zhigang Song Xin +1 位作者 Xu Siyang Cao Jing 《China Communications》 SCIE EI CSCD 2025年第2期226-240,共15页
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
关键词 edge computing energy harvesting energy storage unit renewable energy sampling average approximation task scheduling
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An Improved Task Scheduling Algorithm in Grid Computing Environment 认领 引用
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作者 Liang Yu Gang Zhou Yifei Pu 《International Journal of Communications, Network and System Sciences》 2011年第4期227-231,共5页
Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational schedu... Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational scheduling (GS) and communication inclusion generational scheduling (CIGS) algorithms. Finally, an improved CIGS algorithm is proposed to use in grid computing environment, and it has been proved effectively. 展开更多
关键词 Grid Computing Model of Task Scheduling Heuristics Algorithm Dependent Task Scheduling Algorithm
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Multi-Objective Enhanced Cheetah Optimizer for Joint Optimization of Computation Offloading and Task Scheduling in Fog Computing 认领 引用
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作者 Ahmad Zia Nazia Azim +5 位作者 Bekarystankyzy Akbayan Khalid J.Alzahrani Ateeq Ur Rehman Faheem Ullah Khan Nouf Al-Kahtani Hend Khalid Alkahtani 《Computers, Materials & Continua》 SCIE EI 2026年第3期1559-1588,共30页
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c... The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods. 展开更多
关键词 Computation offloading task scheduling cheetah optimizer fog computing optimization resource allocation internet of things
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A Real-Time Task Scheduling Algorithm Based on Bilateral Matching Games in a Distributed Computing Environment 认领 引用
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作者 LI Shuo FANG Zuying +1 位作者 ZHOU Guoqiang DAI Guilan 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2026年第1期69-78,共10页
In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task exec... In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node. 展开更多
关键词 dispersed computing real-time task task scheduling bilateral matching game
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Energy-Efficient Delay-Constrained Task Scheduling in Heterogeneous Computing Power Networks 认领 引用
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作者 Zhang Wenkai Zhang Shicong +3 位作者 Gu Chenhui Yang Xinglin Hu Bing Wang Wei 《China Communications》 SCIE EI CSCD 2026年第5期317-330,共14页
With the dramatic increase in the demand for computing power across various services,the emergence of the computing power network(CPN)becomes inevitable.This paper studies the task scheduling to minimize energy consum... With the dramatic increase in the demand for computing power across various services,the emergence of the computing power network(CPN)becomes inevitable.This paper studies the task scheduling to minimize energy consumption under delay constraints considering the heterogeneity of computing resources.Specifically,we decompose the original problem and alternatively optimize the scheduling strategy and server parameters until convergence.Dynamic Voltage and frequency scaling(DVFS)technology is leveraged to allocate the optimal voltage and frequency for each server based on their task loads.An enhanced projection gradient descent method is utilized to update the scheduling strategy under the given server parameters.Simulation results show that our algorithm achieves significant performance gains compared to the baselines across various CPN scenarios. 展开更多
关键词 computing power network energy efficiency heterogeneous computing resource task scheduling
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Physical-layer secure hybrid task scheduling and resource management for fog computing IoT networks 认领 引用 被引量：1
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作者 ZHANG Shibo GAO Hongyuan +1 位作者 SU Yumeng SUN Rongchen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1146-1160,共15页
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
关键词 fog computing Internet-of-Things(IoT) physical layer security hybrid task scheduling and resource management quantum galaxy-based search algorithm(QGSA)
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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm 认领 引用 被引量：2
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作者 Jeng-Shyang Pan Na Yu +3 位作者 Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada 《Computers, Materials & Continua》 SCIE EI 2025年第2期2495-2520,共26页
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
关键词 Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy
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MDMOSA:Multi-Objective-Oriented Dwarf Mongoose Optimization for Cloud Task Scheduling 认领 引用
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作者 Olanrewaju Lawrence Abraham Md Asri Ngadi +1 位作者 Johan Bin Mohamad Sharif Mohd Kufaisal Mohd Sidik 《Computers, Materials & Continua》 SCIE EI 2026年第3期2062-2096,共35页
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev... Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures. 展开更多
关键词 Cloud computing multi-objective task scheduling dwarf mongoose optimization metaheuristic
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AMTS:Adaptive Multi-Objective Task Scheduling Strategy in Cloud Computing 认领 引用 被引量：2
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作者 HE Hua XU Guangquan +1 位作者 PANG Shanchen ZHAO Zenghua 《China Communications》 SCIE CSCD 2016年第4期162-171,共10页
Task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consump... Task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consumption and Quality of Service(QoS) requirements under the changing environment and diverse tasks. Considering both processing time and transmission time, a PSO-based Adaptive Multi-objective Task Scheduling(AMTS) Strategy is proposed in this paper. First, the task scheduling problem is formulated. Then, a task scheduling policy is advanced to get the optimal resource utilization, task completion time, average cost and average energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem. 展开更多
关键词 quality of service cloud computing multi-objective task scheduling particle swarm optimization(PSO) small position value(SPV)
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A Bioinspired Method for Optimal Task Scheduling in Fog-Cloud Environment 认领 引用
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作者 Ferzat Anka Ghanshyam G.Tejani +1 位作者 Sunil Kumar Sharma Mohammed Baljon 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2691-2724,共34页
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
关键词 Improved ARO fog computing task scheduling GoCJ_Dataset chaotic map levy flight
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Efficient Task Scheduling for Many Task Computing with Resource Attribute Selection 认领 引用 被引量：3
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作者 ZHAO Yong CHEN Liang +1 位作者 LI Youfu TIAN Wenhong 《China Communications》 SCIE CSCD 2014年第12期125-140,共16页
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,... Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications. 展开更多
关键词 task scheduling resource attribute selection many task computing resource utilization work-stealing
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Flexible Task Scheduling Based on Edge Computing and Cloud Collaboration 认领 引用 被引量：4
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作者 Suzhen Wang Wenli Wang +1 位作者 Zhiting Jia Chaoyi Pang 《Computer Systems Science & Engineering》 SCIE EI 2022年第9期1241-1255,共15页
With the rapid development and popularization of 5G and the Internetof Things, a number of new applications have emerged, such as driverless cars.Most of these applications are time-delay sensitive, and some deficienc... With the rapid development and popularization of 5G and the Internetof Things, a number of new applications have emerged, such as driverless cars.Most of these applications are time-delay sensitive, and some deficiencies werefound during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved atpresent. In 5 g environments, edge computing can better meet the needs of lowdelay and wide connection applications, and support the fast request of terminalusers. However, edge computing only has the edge layer computing advantage,and it is difficult to achieve global resource scheduling and configuration, whichmay lead to the problems of low resource utilization rate, long task processingdelay and unbalanced system load, so as to lead to affect the service quality ofusers. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes agenetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve taskscheduling and resource allocation, and designs a series of experiments to verifythe effectiveness of the GSA-EDGE algorithm. The experimental results showthat the proposed method can reduce the time delay of task processing comparedwith the local task processing method and the task average allocation method. 展开更多
关键词 Edge computing “cloud-edge-terminal”framework task scheduling and resource allocation
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Dynamic access task scheduling of LEO constellation based on space-based distributed computing 认领 引用 被引量：3
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作者 LIU Wei JIN Yifeng +2 位作者 ZHANG Lei GAO Zihe TAO Ying 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第4期842-854,共13页
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
关键词 beam resource allocation distributed computing low Earth obbit(LEO)constellation spacecraft access task scheduling
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Particle Swarm Optimization Embedded in Variable Neighborhood Search for Task Scheduling in Cloud Computing 认领 引用 被引量：2
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作者 郭力争 王永皎 +2 位作者 赵曙光 沈士根 姜长元 《Journal of Donghua University(English Edition)》 EI CAS 2013年第2期145-152,共8页
In cloud computing system,it is a hot and hard issue to find the optimal task scheduling method that makes the processing cost and the running time minimum. In order to deal with the task assignment,a task interaction... In cloud computing system,it is a hot and hard issue to find the optimal task scheduling method that makes the processing cost and the running time minimum. In order to deal with the task assignment,a task interaction graph was used to analyze the task scheduling; a modeling for task assignment was formulated and a particle swarm optimization (PSO)algorithm embedded in the variable neighborhood search (VNS) to optimize the task scheduling was proposed. The experimental results show that the method is more effective than the PSO in processing cost,transferring cost, and running time. When the task is more complex,the effect is much better. So,the algorithm can resolve the task scheduling in cloud computing and it is feasible,valid,and efficient. 展开更多
关键词 cloud computing particle swarm optimization （ PSO） task scheduling variable neighborhood search （ VNS）
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Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet 认领 引用 被引量：1
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作者 Yanping Chen Xuyang Bai +3 位作者 Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling 《Computers, Materials & Continua》 SCIE EI 2023年第4期2101-2117,共17页
Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskde... Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost. 展开更多
关键词 Edge computing task scheduling blockchain task caching industrial security
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Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing 认领 引用 被引量：1
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作者 Lei Yin Chang Sun +3 位作者 Ming Gao Yadong Fang Ming Li Fengyu Zhou 《Intelligent Automation & Soft Computing》 SCIE 2023年第8期1587-1608,共22页
The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the diff... The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity. 展开更多
关键词 Task scheduling cloud computing hyper-heuristic algorithm makespan optimization
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ACS-based resource assignment and task scheduling in grid 认领 引用 被引量：1
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作者 祁超 张璟 李军怀 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期451-454,共4页
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
关键词 grid resource assignment task scheduling ant colony system （ACS） task resource assignment graph （TRAG） semaphore
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An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing 认领 引用 被引量：1
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作者 Mohit Agarwal Shikha Gupta 《Computers, Materials & Continua》 SCIE EI 2022年第12期6103-6119,共17页
Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of t... Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration. 展开更多
关键词 Cloud computing genetic algorithm(GA) load balancing makespan resource utilization task scheduling
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A new heuristic for task scheduling in heterogeneous computing environment 认领 引用
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作者 Ehsan Ullah MUNIR Jian-zhong LI +2 位作者 Sheng-fei SHI Zhao-nian ZOU Qaisar RASOOL 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第12期1715-1723,共9页
Heterogeneous computing (HC) environment utilizes diverse resources with different computational capabilities to solve computing-intensive applications having diverse computational requirements and constraints. The ta... Heterogeneous computing (HC) environment utilizes diverse resources with different computational capabilities to solve computing-intensive applications having diverse computational requirements and constraints. The task assignment problem in HC environment can be formally defined as for a given set of tasks and machines, assigning tasks to machines to achieve the minimum makespan. In this paper we propose a new task scheduling heuristic, high standard deviation first (HSTDF), which considers the standard deviation of the expected execution time of a task as a selection criterion. Standard deviation of the ex- pected execution time of a task represents the amount of variation in task execution time on different machines. Our conclusion is that tasks having high standard deviation must be assigned first for scheduling. A large number of experiments were carried out to check the effectiveness of the proposed heuristic in different scenarios, and the comparison with the existing heuristics (Max-min, Sufferage, Segmented Min-average, Segmented Min-min, and Segmented Max-min) clearly reveals that the proposed heuristic outperforms all existing heuristics in terms of average makespan. 展开更多
关键词 Heterogeneous computing Task scheduling Greedy heuristics High standard deviation first （HSTDF） heuristic
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A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling 认领 引用 被引量：12
20
作者 SHU Wanneng ZHENG Shijue 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第5期1378-1382,共5页
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
关键词 grid computing, task scheduling genetic algorithm simulated annealing PGSAHA algorithm
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