Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay ...Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).展开更多
Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase sch...Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception.First,a full-cycle probabilistic trend sequence is constructed based on the source-load historical data,and in the day-ahead scheduling phase,the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend,with the probability of the security boundary as the security constraint,and with the economy as the objective.Then in the intraday phase,the core security and economic operation boundary of theADNis screened in real time.Fromthere,it quantitatively senses the degree of threat to the core security and economic operation boundary under the current source-load prediction information,and identifies the strictly secure and low/high-risk time periods.Flexibility resources within the response interval are dynamically adjusted in real-time by focusing on high-risk periods to cope with future core risks of the distribution grid.Finally,the improved IEEE 33-node distribution system is simulated to obtain the flexibility resource scheduling scheme on the load and storage side.Thescheduling results are evaluated from the perspectives of risk probability and flexible resource utilization efficiency,and the analysis shows that the scheduling model in this paper can promote the consumption of low-carbon energy from wind and photovoltaic sourceswhile reducing the operational risk of the distribution network.展开更多
California is one of the major alfalfa (Medicago sativa L) forage-producing states in the U.S, but its production area has decreased significantly in the last couple of decades. Selection of cultivars with high yield ...California is one of the major alfalfa (Medicago sativa L) forage-producing states in the U.S, but its production area has decreased significantly in the last couple of decades. Selection of cultivars with high yield and nutritive value under late-cutting schedule strategy may help identify cultivars that growers can use to maximize yield while maintaining area for sustainable alfalfa production, but there is little information on this strategy. A field study was conducted to determine cumulative dry matter (DM) and nutritive values of 20 semi- and non-fall dormant (FD) ratings (FD 7 and FD 8 - 10, respectively) cultivars under 35-day cut in California’s Central Valley in 2020-2022. Seasonal cumulative DM yields ranged from 6.8 in 2020 to 37.0 Mg·ha−1 in 2021. Four FD 8 - 9 cultivars were the highest yielding with 3-yrs avg. DM greater than the lowest yielding lines by 46%. FD 7 cultivar “715RR” produced the highest crude protein (CP: 240 g·Kg−1) while FD 8 cultivar “HVX840RR” resulted in the highest neutral detergent fiber digestibility (NDFD: 484 g·Kg−1, 7% greater than the top yielding cultivars) but with DM yield intermediate. Yields and NDFD correlated positively but weakly indicating some semi- and non-FD cultivars performing similarly. These results suggest that selecting high yielding cultivars under 35-day cutting schedule strategy can be used as a tool to help growers to maximize yield while achieving good quality forages for sustainable alfalfa production in California’s Central Valley.展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
Performance and energy consumption of a solid state disk(SSD) highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we us...Performance and energy consumption of a solid state disk(SSD) highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator(AI), which is the ratio of SSD performance value(e.g., data transfer rate in MB/s or throughput in IOPS) to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD(SSD-I) when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads(i.e., varmail, fileserver, and webserver), and explore optimal combinations of file systems and I/O schedulers(i.e., optimal values of AI) for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.展开更多
Emerging non-volatile memory technologies,especially flash-based solid state drives(SSDs),have increasingly been adopted in the storage stack.They provide numerous advantages over traditional mechanically rotating har...Emerging non-volatile memory technologies,especially flash-based solid state drives(SSDs),have increasingly been adopted in the storage stack.They provide numerous advantages over traditional mechanically rotating hard disk drives(HDDs)and have a tendency to replace HDDs.Due to the long existence of HDDs as primary building blocks for storage systems,however,much of the system software has been specially designed for HDD and may not be optimal for non-volatile memory media.Therefore,in order to realistically leverage its superior raw performance to the maximum,the existing upper layer software has to be re-evaluated or re-designed.To this end,in this paper,we propose PASS,an optimized I/O scheduler at the Linux block layer to accommodate the changing trend of underlying storage devices toward flash-based SSDs.PASS takes the rich internal parallelism in SSDs into account when dispatching requests to the device driver in order to achieve high performance.Specifically,it partitions the logical storage space into fixed-size regions(preferably the component package sizes)as scheduling units.These scheduling units are serviced in a round-robin manner and for every chance that the chosen dispatching unit issues only a batch of either read or write requests to suppress the excessive mutual interference.Additionally,the requests are sorted according to their visiting addresses while waiting in the dispatching queues to exploit high sequential performance of SSD.The experimental results with a variety of workloads have shown that PASS outperforms the four Linux off-the-shelf I/O schedulers by a degree of 3%up to41%,while at the same time it improves the lifetime significantly,due to reducing the internal write amplification.展开更多
在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随...在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。展开更多
Active schedule is one of the most basic and popular concepts in production scheduling research. For identical parallel machine scheduling with jobs＇ dynamic arrivals, the tight performance bounds of active schedules...Active schedule is one of the most basic and popular concepts in production scheduling research. For identical parallel machine scheduling with jobs＇ dynamic arrivals, the tight performance bounds of active schedules under the measurement of four popular objectives are respectively given in this paper. Similar analysis method and conclusions can be generalized to static identical parallel machine and single machine scheduling problem.展开更多
From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule, a simulation program using a software packag...From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule, a simulation program using a software package of Matlab/ Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.展开更多
开发Java Web项目中发现服务之间的调用存在超时情况,由于涉及的处理逻辑全部是异步,引入定时重试的机制,重试工具选择了JDK自带的Scheduled Thread Pool Executor。当A服务依赖B服务,B服务由于在业务高峰期处理能力降低,导致大量A服务...开发Java Web项目中发现服务之间的调用存在超时情况,由于涉及的处理逻辑全部是异步,引入定时重试的机制,重试工具选择了JDK自带的Scheduled Thread Pool Executor。当A服务依赖B服务,B服务由于在业务高峰期处理能力降低,导致大量A服务过来的请求超时,A加入了超时重试机制,间隔时间根据重试次数的多少来决定,次数越多,两次重试之间间隔的时间越多,此时的业务高峰也会给A带来大量请求,大量的超时会导致重试队列迅速堆积,直到内存溢出。该文从线程池工作机制、Scheduled Thread Pool Executor实例的创建,获取重试任务的过程以及提交任务的过程角度分析,并通过源代码的剖析和测试工具My Eclipse进行演示测试内存泄露的情况,得出避免内存泄露的解决方案。展开更多
文摘Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
基金supported by Key Technology Research and Application of Online Control Simulation and Intelligent Decision Making for Active Distribution Network(5108-202218280A-2-377-XG).
文摘Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception.First,a full-cycle probabilistic trend sequence is constructed based on the source-load historical data,and in the day-ahead scheduling phase,the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend,with the probability of the security boundary as the security constraint,and with the economy as the objective.Then in the intraday phase,the core security and economic operation boundary of theADNis screened in real time.Fromthere,it quantitatively senses the degree of threat to the core security and economic operation boundary under the current source-load prediction information,and identifies the strictly secure and low/high-risk time periods.Flexibility resources within the response interval are dynamically adjusted in real-time by focusing on high-risk periods to cope with future core risks of the distribution grid.Finally,the improved IEEE 33-node distribution system is simulated to obtain the flexibility resource scheduling scheme on the load and storage side.Thescheduling results are evaluated from the perspectives of risk probability and flexible resource utilization efficiency,and the analysis shows that the scheduling model in this paper can promote the consumption of low-carbon energy from wind and photovoltaic sourceswhile reducing the operational risk of the distribution network.
文摘California is one of the major alfalfa (Medicago sativa L) forage-producing states in the U.S, but its production area has decreased significantly in the last couple of decades. Selection of cultivars with high yield and nutritive value under late-cutting schedule strategy may help identify cultivars that growers can use to maximize yield while maintaining area for sustainable alfalfa production, but there is little information on this strategy. A field study was conducted to determine cumulative dry matter (DM) and nutritive values of 20 semi- and non-fall dormant (FD) ratings (FD 7 and FD 8 - 10, respectively) cultivars under 35-day cut in California’s Central Valley in 2020-2022. Seasonal cumulative DM yields ranged from 6.8 in 2020 to 37.0 Mg·ha−1 in 2021. Four FD 8 - 9 cultivars were the highest yielding with 3-yrs avg. DM greater than the lowest yielding lines by 46%. FD 7 cultivar “715RR” produced the highest crude protein (CP: 240 g·Kg−1) while FD 8 cultivar “HVX840RR” resulted in the highest neutral detergent fiber digestibility (NDFD: 484 g·Kg−1, 7% greater than the top yielding cultivars) but with DM yield intermediate. Yields and NDFD correlated positively but weakly indicating some semi- and non-FD cultivars performing similarly. These results suggest that selecting high yielding cultivars under 35-day cutting schedule strategy can be used as a tool to help growers to maximize yield while achieving good quality forages for sustainable alfalfa production in California’s Central Valley.
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
基金supported by the National Basic Research Program(973)of China(No.2011CB302303)the National Natural Science Foundation of China(No.60933002)+1 种基金the National High-Tech R&D Program(863)of China(No.2013AA013203)the U.S. National Science Foundation under Grants CCF0845257(CAREER),CNS-0917137(CSR),CNS-0757778(CSR),CCF-0742187(CPA),CNS-0831502(CyberTrust),CNS-0855251(CRI),OCI-0753305(CI-TEAM),DUE-0837341(CCLI),and DUE-0830831(SFS)
文摘Performance and energy consumption of a solid state disk(SSD) highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator(AI), which is the ratio of SSD performance value(e.g., data transfer rate in MB/s or throughput in IOPS) to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD(SSD-I) when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads(i.e., varmail, fileserver, and webserver), and explore optimal combinations of file systems and I/O schedulers(i.e., optimal values of AI) for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.
基金supported by the National Basic Research Program(973)of China(No.2004CB318203) the National High-Tech R&D Program(863)of China(No.2009AA01A402)+1 种基金the Natural Science Foundation of Hubei Province,China(No.2013CFB035)the Key Science Research Project of Hubei Education Office in China(No.D20141301)
文摘Emerging non-volatile memory technologies,especially flash-based solid state drives(SSDs),have increasingly been adopted in the storage stack.They provide numerous advantages over traditional mechanically rotating hard disk drives(HDDs)and have a tendency to replace HDDs.Due to the long existence of HDDs as primary building blocks for storage systems,however,much of the system software has been specially designed for HDD and may not be optimal for non-volatile memory media.Therefore,in order to realistically leverage its superior raw performance to the maximum,the existing upper layer software has to be re-evaluated or re-designed.To this end,in this paper,we propose PASS,an optimized I/O scheduler at the Linux block layer to accommodate the changing trend of underlying storage devices toward flash-based SSDs.PASS takes the rich internal parallelism in SSDs into account when dispatching requests to the device driver in order to achieve high performance.Specifically,it partitions the logical storage space into fixed-size regions(preferably the component package sizes)as scheduling units.These scheduling units are serviced in a round-robin manner and for every chance that the chosen dispatching unit issues only a batch of either read or write requests to suppress the excessive mutual interference.Additionally,the requests are sorted according to their visiting addresses while waiting in the dispatching queues to exploit high sequential performance of SSD.The experimental results with a variety of workloads have shown that PASS outperforms the four Linux off-the-shelf I/O schedulers by a degree of 3%up to41%,while at the same time it improves the lifetime significantly,due to reducing the internal write amplification.
文摘在室内可见光通信中符号间干扰和噪声会严重影响系统性能,K均值(K-means)均衡方法可以抑制光无线信道的影响,但其复杂度较高,且在聚类边界处易出现误判。提出了改进聚类中心点的K-means(Improved Center K-means,IC-Kmeans)算法,通过随机生成足够长的训练序列,然后将训练序列每一簇的均值作为K-means聚类中心,避免了传统K-means反复迭代寻找聚类中心。进一步,提出了基于神经网络的IC-Kmeans(Neural Network Based IC-Kmeans,NNIC-Kmeans)算法,使用反向传播神经网络将接收端二维数据映射至三维空间,以增加不同簇之间混合数据的距离,提高了分类准确性。蒙特卡罗误码率仿真表明,IC-Kmeans均衡和传统K-means算法的误码率性能相当,但可以显著降低复杂度,特别是在信噪比较小时。同时,在室内多径信道模型下,与IC-Kmeans和传统Kmeans均衡相比,NNIC-Kmeans均衡的光正交频分复用系统误码率性能最好。
基金This work was supported by the National Natural Science Foundation of China (No. 60474002, 60504026)Shanghai Development Foundation forScience and Technology (No. 04DZ11008)
文摘Active schedule is one of the most basic and popular concepts in production scheduling research. For identical parallel machine scheduling with jobs＇ dynamic arrivals, the tight performance bounds of active schedules under the measurement of four popular objectives are respectively given in this paper. Similar analysis method and conclusions can be generalized to static identical parallel machine and single machine scheduling problem.
基金This project is supported by National Natural Science Foundation of China( No.59705005) and Backbone Teacher Foundation of Minis
文摘From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule, a simulation program using a software package of Matlab/ Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.
文摘开发Java Web项目中发现服务之间的调用存在超时情况,由于涉及的处理逻辑全部是异步,引入定时重试的机制,重试工具选择了JDK自带的Scheduled Thread Pool Executor。当A服务依赖B服务,B服务由于在业务高峰期处理能力降低,导致大量A服务过来的请求超时,A加入了超时重试机制,间隔时间根据重试次数的多少来决定,次数越多,两次重试之间间隔的时间越多,此时的业务高峰也会给A带来大量请求,大量的超时会导致重试队列迅速堆积,直到内存溢出。该文从线程池工作机制、Scheduled Thread Pool Executor实例的创建,获取重试任务的过程以及提交任务的过程角度分析,并通过源代码的剖析和测试工具My Eclipse进行演示测试内存泄露的情况,得出避免内存泄露的解决方案。
