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 energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various operations.Owing to the large ...The energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various operations.Owing to the large number of platform users,complex businesses,and large amounts of data-mining tasks,it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of users.This study examines the two core technologies of platform task scheduling and multiuser concurrent processing,proposing a distributed task-scheduling method and a technical implementation scheme based on the particle swarm optimization algorithm,and presents a systematic solution in concurrent processing for massive user numbers.Based on the results of this study,the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems.展开更多
With the development of self-interference(SI) cancelation technology, full-duplex(FD) communication becomes possible. FD communication can theoretically double the spectral efficiency. When the time slot(TS) resources...With the development of self-interference(SI) cancelation technology, full-duplex(FD) communication becomes possible. FD communication can theoretically double the spectral efficiency. When the time slot(TS) resources are limited and the number of flows is large, the scheduling mechanism of the flows becomes more important. Therefore, the effectiveness of FD scheduling mechanism for the flows is studied in millimeter wave wireless backhaul network with the limited TS resources. We proposed a full duplex concurrent scheduling algorithm based on coalition game(FDCG) to maximize the number of flows with their QoS requirements satisfied. We transformed the problem of maximizing the number of flows with their QoS requirements satisfied into the problem of maximizing sum rate of concurrently scheduled flows in each slot. We obtained the scheduled flows with maximum sum rate in first slot by using coalition game.And then with certain restrictions, the maximum sum rate of concurrently scheduled flows can also be achieved in subsequent time slots. The simulation results show that the proposed FDCG algorithm canachieve superior performance in terms of the number of flows that meet their QoS requirements and system throughput compared with other three algorithms.展开更多
Scarce resources,precedence and non-determined time-lag are three constraints commonly found in small and medium manufacturing enterprises(SMEs),which are deemed to block the application of workflow management system(...Scarce resources,precedence and non-determined time-lag are three constraints commonly found in small and medium manufacturing enterprises(SMEs),which are deemed to block the application of workflow management system(WfMS). To tackle this problem,a workflow scheduling approach is proposed based on timing workflow net(TWF-net) and genetic algorithm(GA). The workflow is modelled in a form of TWF-net in favour of process simulation and resource conflict checking. After simplifying and reconstructing the set of workflow instance,the conflict resolution problem is transformed into a resource-constrained project scheduling problem(RCPSP),which could be efficiently solved by a heuristic method,such as GA. Finally,problems of various sizes are utilized to test the performance of the proposed algorithm and to compare it with first-come-firstserved(FCFS) strategy. The evaluation demonstrates that the proposed method is an overwhelming and effective approach for scheduling the concurrent processes with precedence and resource constraints.展开更多
This paper introduces a multi-granularity locking model (MGL) for concurrency control in object-oriented database system briefiy, and presents a MGL model formally. Four lockingscheduling algorithms for MGL are propos...This paper introduces a multi-granularity locking model (MGL) for concurrency control in object-oriented database system briefiy, and presents a MGL model formally. Four lockingscheduling algorithms for MGL are proposed in the paper. The ideas of single queue scheduling(SQS) and dual queue scheduling (DQS) are proposed and the algorithm and the performance evaluation for these two scheduling are presented in some paper. This paper describes a new idea of thescheduling for MGL, compatible requests first (CRF). Combining the new idea with SQS and DQS,we propose two new scheduling algorithms called CRFS and CRFD. After describing the simulationmodel, this paper illustrates the comparisons of the performance among these four algorithms. Asshown in the experiments, DQS has better performance than SQS, CRFD is better than DQS, CRFSperforms better than SQS, and CRFS is the best one of these four scheduling algorithms.展开更多
This paper studies the scheduling of design projects in concurrent development process with uncertain number of feedback revisions. An optimization based methodology that combines heuristic algorithm, artificial intel...This paper studies the scheduling of design projects in concurrent development process with uncertain number of feedback revisions. An optimization based methodology that combines heuristic algorithm, artificial intelligence (fuzzy, genetic algorithm) and scheduling theory is developed. In the algorithm, weighting values of some key factors are defined by genetic algorithm. In addition, fuzzy logic is used to decide the ability coefficients of different roles taken by designers. Moreover, the process of pre-release and feedback revision is realized. Simulation example is a fraction of practical development project, and satisfied results are obtained.展开更多
随着区块链技术应用的普及,联盟链Hyperledger Fabric(简称Fabric)已成为知名区块链开源平台,并得到广泛关注.然而Fabric仍受困于并发事务间冲突问题,冲突发生时会引发大量无效交易上链,导致吞吐量下降,阻碍其发展.对于该问题,现有面向...随着区块链技术应用的普及,联盟链Hyperledger Fabric(简称Fabric)已成为知名区块链开源平台,并得到广泛关注.然而Fabric仍受困于并发事务间冲突问题,冲突发生时会引发大量无效交易上链,导致吞吐量下降,阻碍其发展.对于该问题,现有面向块内冲突的方案缺乏高效的冲突检测和避免方法,同时现有研究往往忽略区块间冲突对吞吐量的不利影响.提出了一种Fabric的优化方案Fabric-HT(fabric with high throughput),从区块内和区块间2方面入手,有效降低事务间并发冲突和提高系统吞吐量.针对区块内事务冲突,提出了一种事务调度机制,根据块内冲突事务集定义了一种高效数据结构——依赖关系链,识别具有“危险结构”的事务并提前中止,合理调度事务和消除冲突;针对区块间事务冲突,将冲突事务检测提前至排序节点完成,建立以“推送-匹配”为核心的冲突事务早期避免机制.在多场景下开展大量实验,结果表明Fabric-HT在吞吐量、事务中止率、事务平均执行时间、无效事务空间占用率等方面均优于对比方案.Fabric-HT吞吐量最高可达Fabric的9.51倍,是最新优化方案FabricSharp的1.18倍;空间利用率上相比FabricSharp提升了14%.此外,Fabric-HT也表现出较好的鲁棒性和抗攻击能力.展开更多
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 Science and Technology Project of State Grid Corporation“Research and Application of Internet Operation Platform for Ubiquitous Power Internet of Things”(5700-201955462A-0-0-00).
文摘The energy Internet operation platform provides market entities such as energy users,energy enterprises,suppliers,and governments with the ability to interact,transact,and manage various operations.Owing to the large number of platform users,complex businesses,and large amounts of data-mining tasks,it is necessary to solve the problems afflicting platform task scheduling and the provision of simultaneous access to a large number of users.This study examines the two core technologies of platform task scheduling and multiuser concurrent processing,proposing a distributed task-scheduling method and a technical implementation scheme based on the particle swarm optimization algorithm,and presents a systematic solution in concurrent processing for massive user numbers.Based on the results of this study,the energy internet operation platform can effectively deal with the concurrent access of tens of millions of users and complex task-scheduling problems.
基金supported by the National Natural Science Foundation of China Grants 61725101 and 61801016the China Postdoctoral Science Foundation under Grant 2017M610040 and 2018T110041+2 种基金National key research and development program under Grant 2016YFE0200900the Beijing Natural Fund under Grant L172020Major projects of Beijing Municipal Science and Technology Commission under Grant No. Z181100003218010
文摘With the development of self-interference(SI) cancelation technology, full-duplex(FD) communication becomes possible. FD communication can theoretically double the spectral efficiency. When the time slot(TS) resources are limited and the number of flows is large, the scheduling mechanism of the flows becomes more important. Therefore, the effectiveness of FD scheduling mechanism for the flows is studied in millimeter wave wireless backhaul network with the limited TS resources. We proposed a full duplex concurrent scheduling algorithm based on coalition game(FDCG) to maximize the number of flows with their QoS requirements satisfied. We transformed the problem of maximizing the number of flows with their QoS requirements satisfied into the problem of maximizing sum rate of concurrently scheduled flows in each slot. We obtained the scheduled flows with maximum sum rate in first slot by using coalition game.And then with certain restrictions, the maximum sum rate of concurrently scheduled flows can also be achieved in subsequent time slots. The simulation results show that the proposed FDCG algorithm canachieve superior performance in terms of the number of flows that meet their QoS requirements and system throughput compared with other three algorithms.
基金Supported by the Postdoctoral Science Foundation of China(No.2015M572022)the National Natural Science Foundation of China(No.51175304)
文摘Scarce resources,precedence and non-determined time-lag are three constraints commonly found in small and medium manufacturing enterprises(SMEs),which are deemed to block the application of workflow management system(WfMS). To tackle this problem,a workflow scheduling approach is proposed based on timing workflow net(TWF-net) and genetic algorithm(GA). The workflow is modelled in a form of TWF-net in favour of process simulation and resource conflict checking. After simplifying and reconstructing the set of workflow instance,the conflict resolution problem is transformed into a resource-constrained project scheduling problem(RCPSP),which could be efficiently solved by a heuristic method,such as GA. Finally,problems of various sizes are utilized to test the performance of the proposed algorithm and to compare it with first-come-firstserved(FCFS) strategy. The evaluation demonstrates that the proposed method is an overwhelming and effective approach for scheduling the concurrent processes with precedence and resource constraints.
文摘This paper introduces a multi-granularity locking model (MGL) for concurrency control in object-oriented database system briefiy, and presents a MGL model formally. Four lockingscheduling algorithms for MGL are proposed in the paper. The ideas of single queue scheduling(SQS) and dual queue scheduling (DQS) are proposed and the algorithm and the performance evaluation for these two scheduling are presented in some paper. This paper describes a new idea of thescheduling for MGL, compatible requests first (CRF). Combining the new idea with SQS and DQS,we propose two new scheduling algorithms called CRFS and CRFD. After describing the simulationmodel, this paper illustrates the comparisons of the performance among these four algorithms. Asshown in the experiments, DQS has better performance than SQS, CRFD is better than DQS, CRFSperforms better than SQS, and CRFS is the best one of these four scheduling algorithms.
文摘This paper studies the scheduling of design projects in concurrent development process with uncertain number of feedback revisions. An optimization based methodology that combines heuristic algorithm, artificial intelligence (fuzzy, genetic algorithm) and scheduling theory is developed. In the algorithm, weighting values of some key factors are defined by genetic algorithm. In addition, fuzzy logic is used to decide the ability coefficients of different roles taken by designers. Moreover, the process of pre-release and feedback revision is realized. Simulation example is a fraction of practical development project, and satisfied results are obtained.
文摘随着区块链技术应用的普及,联盟链Hyperledger Fabric(简称Fabric)已成为知名区块链开源平台,并得到广泛关注.然而Fabric仍受困于并发事务间冲突问题,冲突发生时会引发大量无效交易上链,导致吞吐量下降,阻碍其发展.对于该问题,现有面向块内冲突的方案缺乏高效的冲突检测和避免方法,同时现有研究往往忽略区块间冲突对吞吐量的不利影响.提出了一种Fabric的优化方案Fabric-HT(fabric with high throughput),从区块内和区块间2方面入手,有效降低事务间并发冲突和提高系统吞吐量.针对区块内事务冲突,提出了一种事务调度机制,根据块内冲突事务集定义了一种高效数据结构——依赖关系链,识别具有“危险结构”的事务并提前中止,合理调度事务和消除冲突;针对区块间事务冲突,将冲突事务检测提前至排序节点完成,建立以“推送-匹配”为核心的冲突事务早期避免机制.在多场景下开展大量实验,结果表明Fabric-HT在吞吐量、事务中止率、事务平均执行时间、无效事务空间占用率等方面均优于对比方案.Fabric-HT吞吐量最高可达Fabric的9.51倍,是最新优化方案FabricSharp的1.18倍;空间利用率上相比FabricSharp提升了14%.此外,Fabric-HT也表现出较好的鲁棒性和抗攻击能力.
