As increase of disk access speed has far lagged the speed of processors and main memory, disk-scheduling performance, although less significant for personal users with dedicated storage, is crucial for internet-based ...As increase of disk access speed has far lagged the speed of processors and main memory, disk-scheduling performance, although less significant for personal users with dedicated storage, is crucial for internet-based intensive data processing. For modern disks, increase of disk rotation rate makes overhead of disk access to data transfer heavier. Therefore, it seems more important to improve both parallel processing capability of disk I/O and disk-scheduling performance at the same time. For disk-scheduling algorithms based on both disk arm and rotational positions, their time-resolving powers are more precise in comparison with those for disk-scheduling algorithms based only on disk arm position. Algorithms of this sort are studied in this paper. Several improved algorithms based on rotational position are proposed, and simulation results of their performances demonstrate.展开更多
The high-performance computing paradigm needs high-speed switching fabrics to meet the heavy traffic generated by their applications.These switching fabrics are efficiently driven by the deployed scheduling algorithms...The high-performance computing paradigm needs high-speed switching fabrics to meet the heavy traffic generated by their applications.These switching fabrics are efficiently driven by the deployed scheduling algorithms.In this paper,we proposed two scheduling algorithms for input queued switches whose operations are based on ranking procedures.At first,we proposed a Simple 2-Bit(S2B)scheme which uses binary ranking procedure and queue size for scheduling the packets.Here,the Virtual Output Queue(VOQ)set with maximum number of empty queues receives higher rank than other VOQ’s.Through simulation,we showed S2B has better throughput performance than Highest Ranking First(HRF)arbitration under uniform,and non-uniform traffic patterns.To further improve the throughput-delay performance,an Enhanced 2-Bit(E2B)approach is proposed.This approach adopts an integer representation for rank,which is the number of empty queues in a VOQ set.The simulation result shows E2B outperforms S2B and HRF scheduling algorithms with maximum throughput-delay performance.Furthermore,the algorithms are simulated under hotspot traffic and E2B proves to be more efficient.展开更多
In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the q...In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the quality-of-service(QoS)requirements of different network services.Concurrently,network calculus has found widespread application in network modeling and QoS analysis.Network calculus abstracts the details of how nodes or networks process data packets using the concept of service curves.This paper summarizes the service curves for typical scheduling algorithms,including Strict Priority(SP),Round Robin(RR),Cycling Queuing and Forwarding(CQF),Time Aware Shaper(TAS),Credit Based Shaper(CBS),and Asynchronous Traffic Shaper(ATS).It introduces the theory of network calculus and then provides an overview of various scheduling algorithms and their associated service curves.The delay bound analysis for different scheduling algorithms in specific scenarios is also conducted for more insights.展开更多
The exponential growth of Internet ofThings(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet ...The exponential growth of Internet ofThings(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems,while pure edge computing faces resource constraints that limit processing capabilities.This paper addresses these challenges by proposing a novel Deep Reinforcement Learning(DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments.Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency.The framework introduces three key innovations:(1)a DRL-based dynamic priority assignmentmechanism that learns fromsystem behavior,(2)a hybrid concurrency control protocol combining local edge validation with global cloud coordination,and(3)an integrated mathematical model that formalizes sensor-driven transactions across edge-cloud architectures.Extensive simulations across diverse workload scenarios demonstrate significant quantitative improvements:40%latency reduction,25%throughput increase,85%resource utilization(compared to 60%for heuristicmethods),40%reduction in energy consumption(300 vs.500 J per task),and 50%improvement in scalability factor(1.8 vs.1.2 for EDF)compared to state-of-the-art heuristic and meta-heuristic approaches.These results establish the framework as a robust solution for large-scale IoT and autonomous applications requiring real-time processing with consistency guarantees.展开更多
The downlink zero-forcing beamforming strategy in the case of random packet arrivals is investigated. Under this setting, the relevant fairness criterion is the stabilization of all buffer queues which guarantees a bo...The downlink zero-forcing beamforming strategy in the case of random packet arrivals is investigated. Under this setting, the relevant fairness criterion is the stabilization of all buffer queues which guarantees a bounded average delay for all users. It has been shown that allocating resources to maximize a queue-length-weighted sum of the rates is a stabilizing policy. However, the high complexity of user selection and the feasible rates determination for optimal scheme may prevent the real-time scheduling operation. Two low complexity algorithms are provided taking the channel state, queue state and orthogonality into account. In particular, the authors pick the first user with the largest product between channel gain and queuing length, and select the remaining users to construct candidate user set based on the greedy user selection method or channel orthogonal user selection method. Then, the power and rate allocation for the selected users are implemented based on the modified water-filling method. The complexity of the proposed algorithms is analyzed. The average delay and average throughput are studied in homogeneous scenarios and heterogeneous scenarios, respectively. Simulation results show that the proposed algorithms can take full advantage of the multi-user diversity gain and provide average delay （or throughput） and fairness improvement compared with channel-aware-only schemes.展开更多
Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and th...Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and then the problem is simplified asthe asymmetrical travelingsalesman problem with time windows. The rolling horizon scheduling algorithm (RHSA) to solve thisdynamic problem is proposed. By the rolling of time horizon, the RHSA can adapt to the problem'sdynamic change and reduce the computation time by dealing with only part of the customers in eachrolling time horizon. Then, its three factors, the current customer window, the scheduling of thecurrent customer window and the rolling strategy, are analyzed. The test results demonstrate theeffectiveness of the RHSA to solve the dynamic vehicle scheduling problem.展开更多
SATech-01 is an experimental satellite for space science exploration and on-orbit demonstration of advanced technologies.The satellite is equipped with 16 experimental payloads and supports multiple working modes to m...SATech-01 is an experimental satellite for space science exploration and on-orbit demonstration of advanced technologies.The satellite is equipped with 16 experimental payloads and supports multiple working modes to meet the observation requirements of various payloads.Due to the limitation of platform power supply and data storage systems,proposing reasonable mission planning schemes to improve scientific revenue of the payloads becomes a critical issue.In this article,we formulate the integrated task scheduling of SATech-01 as a multi-objective optimization problem and propose a novel Fair Integrated Scheduling with Proximal Policy Optimization(FIS-PPO)algorithm to solve it.We use multiple decision heads to generate decisions for each task and design the action mask to ensure the schedule meeting the platform constraints.Experimental results show that FIS-PPO could push the capability of the platform to the limit and improve the overall observation efficiency by 31.5%compared to rule-based plans currently used.Moreover,fairness is considered in the reward design and our method achieves much better performance in terms of equal task opportunities.Because of its low computational complexity,our task scheduling algorithm has the potential to be directly deployed on board for real-time task scheduling in future space projects.展开更多
One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consider...One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.展开更多
This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gai...This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gain are compared via simulations first.Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model.Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.展开更多
When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and ...When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.展开更多
In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its s...In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.展开更多
Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide th...Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.展开更多
We put forward an optimal disk schedule with n disk requests and prove its optimality mathematically.Generalizing the idea of an optimal disk schedule, we remove the limit of n requests and, at the same time, consider...We put forward an optimal disk schedule with n disk requests and prove its optimality mathematically.Generalizing the idea of an optimal disk schedule, we remove the limit of n requests and, at the same time, consider the dynamically arrival model of disk requests to obtain an algorithm, shortest path first-fit first （SPFF）. This algorithm is based on the shortest path of disk head motion constructed by all the pendent requests. From view of the head moving distance, it has the stronger glohality than SSTF. From view of the head-moving direction, it has the better flexibility than SCAN. Therefore, SPFF keeps the advantage of SCAN and, at the same time, absorbs the strength of SSTF. The algorithm SPFF not only shows the more superiority than other scheduling polices, but also have higher adjustability to meet the computer system＇s different demands.展开更多
It is important to evaluate function behaviors and performance features of task scheduling algorithm in the multi-processor system.A novel dynamic measurement method(DMM)was proposed to measure the task scheduling alg...It is important to evaluate function behaviors and performance features of task scheduling algorithm in the multi-processor system.A novel dynamic measurement method(DMM)was proposed to measure the task scheduling algorithm’s correctness and dependability.In a multi-processor system,task scheduling problem is represented by a combinatorial evaluation model,interactive Markov chain(IMC),and solution space of the algorithm with time and probability metrics is described by action-based continuous stochastic logic(aCSL).DMM derives a path by logging runtime scheduling actions and corresponding times.Through judging whether the derived path can be received by task scheduling IMC model,DMM analyses the correctness of algorithm.Through judging whether the actual values satisfy label function of the initial state,DMM analyses the dependability of algorithm.The simulation shows that DMM can effectively characterize the function behaviors and performance features of task scheduling algorithm.展开更多
Requests distribution is an key technology for Web cluster server. This paper presents a throughput-driven scheduling algorithm （TDSA）. The algorithm adopts the throughput of cluster back-ends to evaluate their load...Requests distribution is an key technology for Web cluster server. This paper presents a throughput-driven scheduling algorithm （TDSA）. The algorithm adopts the throughput of cluster back-ends to evaluate their load and employs the neural network model to predict the future load so that the scheduling system features a self-learning capability and good adaptability to the change of load. Moreover, it separates static requests from dynamic requests to make full use of the CPU resources and takes the locality of requests into account to improve the cache hit ratio. Experimental re suits from the testing tool of WebBench^TM show better per formance for Web cluster server with TDSA than that with traditional scheduling algorithms.展开更多
In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop pro...In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.展开更多
In the dynamic, complex and unbounded Grid systems, failures of Grid resources caused by malicious attacks and hardware failures are inevitable and have an adverse effect on the execution of tasks. To mitigate this pr...In the dynamic, complex and unbounded Grid systems, failures of Grid resources caused by malicious attacks and hardware failures are inevitable and have an adverse effect on the execution of tasks. To mitigate this problem, a makespan and reliability driven （MRD） sufferage scheduling algorithm is designed and implemented. Different from the traditional Grid scheduling algorithms, the algorithm addresses the makespan as well as reliability of tasks. The simulation experimental results show that the MRD sufferage scheduling algorithm can increase reliability of tasks and can trade off reliability against makespan of tasks by adjusting the weighting parameter in its cost function. So it can be applied to the complex Grid computing environment well.展开更多
Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multip...Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.展开更多
A dynamic advanced planning and scheduling （DAPS） problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A gene...A dynamic advanced planning and scheduling （DAPS） problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule at each rescheduling point for both original orders and new orders that both production idle time and penalties on tardiness and earliness of orders are minimized. The proposed methodology is tested on a small example to illustrate the effect of the frozen interval. The results indicate that the suggested approach can improve the schedule stability while retaining efficiency.展开更多
In this paper, we study utilitybased resource allocation for users supporting multiple services in a LTEA system with coordinated multipoint transmission for singleuser multiinput multioutput （CoMPSUMIMO）. We design...In this paper, we study utilitybased resource allocation for users supporting multiple services in a LTEA system with coordinated multipoint transmission for singleuser multiinput multioutput （CoMPSUMIMO）. We designed Joint Transmission Power Control （JTPC） for the selected clusters for minimizing power consumption in LTEA systems. The objective of JTPC is to calculate the optimal transmission power for each scheduled user and subcarrier. Moreover, based on the convex optimization theory, we propose the dynamic sector selection method in which the average sector throughput and celledge users （UEs） rates are performed to achieve the optimal solution. Simulation results show that the system performance achieved by using the proposed suboptimal algorithm is close to that achieved by the dual decomposition method.展开更多
基金Project supported by National Natural Science Foundation of Chi-na( Grant No . 60373088) , and Defense Pre-research Project ofChina(Grant No .413160502)
文摘As increase of disk access speed has far lagged the speed of processors and main memory, disk-scheduling performance, although less significant for personal users with dedicated storage, is crucial for internet-based intensive data processing. For modern disks, increase of disk rotation rate makes overhead of disk access to data transfer heavier. Therefore, it seems more important to improve both parallel processing capability of disk I/O and disk-scheduling performance at the same time. For disk-scheduling algorithms based on both disk arm and rotational positions, their time-resolving powers are more precise in comparison with those for disk-scheduling algorithms based only on disk arm position. Algorithms of this sort are studied in this paper. Several improved algorithms based on rotational position are proposed, and simulation results of their performances demonstrate.
文摘The high-performance computing paradigm needs high-speed switching fabrics to meet the heavy traffic generated by their applications.These switching fabrics are efficiently driven by the deployed scheduling algorithms.In this paper,we proposed two scheduling algorithms for input queued switches whose operations are based on ranking procedures.At first,we proposed a Simple 2-Bit(S2B)scheme which uses binary ranking procedure and queue size for scheduling the packets.Here,the Virtual Output Queue(VOQ)set with maximum number of empty queues receives higher rank than other VOQ’s.Through simulation,we showed S2B has better throughput performance than Highest Ranking First(HRF)arbitration under uniform,and non-uniform traffic patterns.To further improve the throughput-delay performance,an Enhanced 2-Bit(E2B)approach is proposed.This approach adopts an integer representation for rank,which is the number of empty queues in a VOQ set.The simulation result shows E2B outperforms S2B and HRF scheduling algorithms with maximum throughput-delay performance.Furthermore,the algorithms are simulated under hotspot traffic and E2B proves to be more efficient.
基金supported by ZTE Industry-University-Institute Cooperation Funds。
文摘In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the quality-of-service(QoS)requirements of different network services.Concurrently,network calculus has found widespread application in network modeling and QoS analysis.Network calculus abstracts the details of how nodes or networks process data packets using the concept of service curves.This paper summarizes the service curves for typical scheduling algorithms,including Strict Priority(SP),Round Robin(RR),Cycling Queuing and Forwarding(CQF),Time Aware Shaper(TAS),Credit Based Shaper(CBS),and Asynchronous Traffic Shaper(ATS).It introduces the theory of network calculus and then provides an overview of various scheduling algorithms and their associated service curves.The delay bound analysis for different scheduling algorithms in specific scenarios is also conducted for more insights.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R909),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of Internet ofThings(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems,while pure edge computing faces resource constraints that limit processing capabilities.This paper addresses these challenges by proposing a novel Deep Reinforcement Learning(DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments.Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency.The framework introduces three key innovations:(1)a DRL-based dynamic priority assignmentmechanism that learns fromsystem behavior,(2)a hybrid concurrency control protocol combining local edge validation with global cloud coordination,and(3)an integrated mathematical model that formalizes sensor-driven transactions across edge-cloud architectures.Extensive simulations across diverse workload scenarios demonstrate significant quantitative improvements:40%latency reduction,25%throughput increase,85%resource utilization(compared to 60%for heuristicmethods),40%reduction in energy consumption(300 vs.500 J per task),and 50%improvement in scalability factor(1.8 vs.1.2 for EDF)compared to state-of-the-art heuristic and meta-heuristic approaches.These results establish the framework as a robust solution for large-scale IoT and autonomous applications requiring real-time processing with consistency guarantees.
基金supported by the National Natural Science Foundation of China (6077212, 60772166)
文摘The downlink zero-forcing beamforming strategy in the case of random packet arrivals is investigated. Under this setting, the relevant fairness criterion is the stabilization of all buffer queues which guarantees a bounded average delay for all users. It has been shown that allocating resources to maximize a queue-length-weighted sum of the rates is a stabilizing policy. However, the high complexity of user selection and the feasible rates determination for optimal scheme may prevent the real-time scheduling operation. Two low complexity algorithms are provided taking the channel state, queue state and orthogonality into account. In particular, the authors pick the first user with the largest product between channel gain and queuing length, and select the remaining users to construct candidate user set based on the greedy user selection method or channel orthogonal user selection method. Then, the power and rate allocation for the selected users are implemented based on the modified water-filling method. The complexity of the proposed algorithms is analyzed. The average delay and average throughput are studied in homogeneous scenarios and heterogeneous scenarios, respectively. Simulation results show that the proposed algorithms can take full advantage of the multi-user diversity gain and provide average delay （or throughput） and fairness improvement compared with channel-aware-only schemes.
文摘Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and then the problem is simplified asthe asymmetrical travelingsalesman problem with time windows. The rolling horizon scheduling algorithm (RHSA) to solve thisdynamic problem is proposed. By the rolling of time horizon, the RHSA can adapt to the problem'sdynamic change and reduce the computation time by dealing with only part of the customers in eachrolling time horizon. Then, its three factors, the current customer window, the scheduling of thecurrent customer window and the rolling strategy, are analyzed. The test results demonstrate theeffectiveness of the RHSA to solve the dynamic vehicle scheduling problem.
基金supported by the Strategic Priority Program on Space Science,Chinese Academy of Sciences。
文摘SATech-01 is an experimental satellite for space science exploration and on-orbit demonstration of advanced technologies.The satellite is equipped with 16 experimental payloads and supports multiple working modes to meet the observation requirements of various payloads.Due to the limitation of platform power supply and data storage systems,proposing reasonable mission planning schemes to improve scientific revenue of the payloads becomes a critical issue.In this article,we formulate the integrated task scheduling of SATech-01 as a multi-objective optimization problem and propose a novel Fair Integrated Scheduling with Proximal Policy Optimization(FIS-PPO)algorithm to solve it.We use multiple decision heads to generate decisions for each task and design the action mask to ensure the schedule meeting the platform constraints.Experimental results show that FIS-PPO could push the capability of the platform to the limit and improve the overall observation efficiency by 31.5%compared to rule-based plans currently used.Moreover,fairness is considered in the reward design and our method achieves much better performance in terms of equal task opportunities.Because of its low computational complexity,our task scheduling algorithm has the potential to be directly deployed on board for real-time task scheduling in future space projects.
基金supported by Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry under Grant No.2010-2011 and Chinese Post-doctoral Research Foundation
文摘One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
基金supported by the National Defense Pre-research Foundation (9140A21041110KG0148)
文摘This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gain are compared via simulations first.Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model.Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.
文摘When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.
基金This project is supported by Key Science-Technology Project of Shanghai City Tenth Five-Year-Plan, China (No.031111002)Specialized Research Fund for the Doctoral Program of Higher Education, China (No.20040247033)Municipal Key Basic Research Program of Shanghai, China (No.05JC14060)
文摘In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.
基金Project supported by the National Natural Science Foundation of China (No. 60225009), and the National Science Fund for Distin-guished Young Scholars, China
文摘Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.
基金Supported by the National Natural Science Founda-tion of China (60373088)
文摘We put forward an optimal disk schedule with n disk requests and prove its optimality mathematically.Generalizing the idea of an optimal disk schedule, we remove the limit of n requests and, at the same time, consider the dynamically arrival model of disk requests to obtain an algorithm, shortest path first-fit first （SPFF）. This algorithm is based on the shortest path of disk head motion constructed by all the pendent requests. From view of the head moving distance, it has the stronger glohality than SSTF. From view of the head-moving direction, it has the better flexibility than SCAN. Therefore, SPFF keeps the advantage of SCAN and, at the same time, absorbs the strength of SSTF. The algorithm SPFF not only shows the more superiority than other scheduling polices, but also have higher adjustability to meet the computer system＇s different demands.
基金the National Natural Science Foundation of China(Nos.11371003 and 11461006)the Special Fund for Scientific and Technological Bases and Talents of Guangxi(No.2016AD05050)+3 种基金the Special Fund for Bagui Scholars of Guangxithe Major Tendering Project of the National Social Science Foundation(No.17ZDA160)the Sichuan Science and Technology Project(No.19YYJC0038)the Fundamental Research Funds for the Central Universities,SWUN(No.2019NYB20)
文摘It is important to evaluate function behaviors and performance features of task scheduling algorithm in the multi-processor system.A novel dynamic measurement method(DMM)was proposed to measure the task scheduling algorithm’s correctness and dependability.In a multi-processor system,task scheduling problem is represented by a combinatorial evaluation model,interactive Markov chain(IMC),and solution space of the algorithm with time and probability metrics is described by action-based continuous stochastic logic(aCSL).DMM derives a path by logging runtime scheduling actions and corresponding times.Through judging whether the derived path can be received by task scheduling IMC model,DMM analyses the correctness of algorithm.Through judging whether the actual values satisfy label function of the initial state,DMM analyses the dependability of algorithm.The simulation shows that DMM can effectively characterize the function behaviors and performance features of task scheduling algorithm.
基金Supported by the National Natural Science Funda-tion of China (60175015)
文摘Requests distribution is an key technology for Web cluster server. This paper presents a throughput-driven scheduling algorithm （TDSA）. The algorithm adopts the throughput of cluster back-ends to evaluate their load and employs the neural network model to predict the future load so that the scheduling system features a self-learning capability and good adaptability to the change of load. Moreover, it separates static requests from dynamic requests to make full use of the CPU resources and takes the locality of requests into account to improve the cache hit ratio. Experimental re suits from the testing tool of WebBench^TM show better per formance for Web cluster server with TDSA than that with traditional scheduling algorithms.
基金supported by the National Key R&D Plan(2020YFB1712902)the National Natural Science Foundation of China(52075036).
文摘In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.
文摘In the dynamic, complex and unbounded Grid systems, failures of Grid resources caused by malicious attacks and hardware failures are inevitable and have an adverse effect on the execution of tasks. To mitigate this problem, a makespan and reliability driven （MRD） sufferage scheduling algorithm is designed and implemented. Different from the traditional Grid scheduling algorithms, the algorithm addresses the makespan as well as reliability of tasks. The simulation experimental results show that the MRD sufferage scheduling algorithm can increase reliability of tasks and can trade off reliability against makespan of tasks by adjusting the weighting parameter in its cost function. So it can be applied to the complex Grid computing environment well.
基金Supported by the National Natural Science Foundation of China(21276078)"Shu Guang"project of Shanghai Municipal Education Commission,973 Program of China(2012CB720500)the Shanghai Science and Technology Program(13QH1401200)
文摘Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.
基金This project is supported by the Hong Kong Polytechnic University,China(No,G-RGF9).
文摘A dynamic advanced planning and scheduling （DAPS） problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule at each rescheduling point for both original orders and new orders that both production idle time and penalties on tardiness and earliness of orders are minimized. The proposed methodology is tested on a small example to illustrate the effect of the frozen interval. The results indicate that the suggested approach can improve the schedule stability while retaining efficiency.
基金partially supported by the Fundamental Research Funds for the Central Universities under Grant No.2012RC0401
文摘In this paper, we study utilitybased resource allocation for users supporting multiple services in a LTEA system with coordinated multipoint transmission for singleuser multiinput multioutput （CoMPSUMIMO）. We designed Joint Transmission Power Control （JTPC） for the selected clusters for minimizing power consumption in LTEA systems. The objective of JTPC is to calculate the optimal transmission power for each scheduled user and subcarrier. Moreover, based on the convex optimization theory, we propose the dynamic sector selection method in which the average sector throughput and celledge users （UEs） rates are performed to achieve the optimal solution. Simulation results show that the system performance achieved by using the proposed suboptimal algorithm is close to that achieved by the dual decomposition method.
