Scalability is one of the utmost nonfunctional requirement of server applications,because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload.In order to achiev...Scalability is one of the utmost nonfunctional requirement of server applications,because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload.In order to achieve scalability,thread pool system(TPS)has been used extensively as a middleware service in server applications.The size of thread pool is the most significant factor,that affects the overall performance of servers.Determining the optimal size of thread pool dynamically on runtime is a challenging problem.The most widely used and simple method to tackle this problem is to keep the size of thread pool equal to the request rate,i.e.,the frequencyoriented thread pool(FOTP).The FOTPs are the most widely used TPSs in the industry,because of the implementation simplicity,the negligible overhead and the capability to use in any system.However,the frequency-based schemes only focused on one aspect of changes in the load,and that is the fluctuations in request rate.The request rate alone is an imperfect knob to scale thread pool.Thus,this paper presents a workload profiling based FOTP,that focuses on request size(service time of request)besides the request rate as a knob to scale thread pool on runtime,because we argue that the combination of both truly represents the load fluctuation in server-side applications.We evaluated the results of the proposed system against state of the art TPS of Oracle Corporation(by a client-server-based simulator)and concluded that our system outperformed in terms of both;the response times and throughput.展开更多
The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a gre...The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a greater role in actual production,these algorithm modules must be integrated into software systems and used more often in actual production projects.Deep learning frameworks,such as TensorFlow and PyTorch,basically take Python as the core architecture,while the application program mainly uses Java,C#,and other programming languages.During integration,the seismic data read by the Java and C#data interfaces must be transferred to the Python main program module.The data exchange methods between Java,C#,and Python include shared memory,shared directory,and so on.However,these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks.Considering the large volume of seismic data and the need for network support for deep learning,this paper proposes a method of transmitting seismic data based on Socket.By maximizing Socket’s cross-network and efficient longdistance transmission,this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system.Furthermore,the actual production application shows that this method effectively solves the shortage of data transmission in shared memory,shared directory,and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.展开更多
文摘Scalability is one of the utmost nonfunctional requirement of server applications,because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload.In order to achieve scalability,thread pool system(TPS)has been used extensively as a middleware service in server applications.The size of thread pool is the most significant factor,that affects the overall performance of servers.Determining the optimal size of thread pool dynamically on runtime is a challenging problem.The most widely used and simple method to tackle this problem is to keep the size of thread pool equal to the request rate,i.e.,the frequencyoriented thread pool(FOTP).The FOTPs are the most widely used TPSs in the industry,because of the implementation simplicity,the negligible overhead and the capability to use in any system.However,the frequency-based schemes only focused on one aspect of changes in the load,and that is the fluctuations in request rate.The request rate alone is an imperfect knob to scale thread pool.Thus,this paper presents a workload profiling based FOTP,that focuses on request size(service time of request)besides the request rate as a knob to scale thread pool on runtime,because we argue that the combination of both truly represents the load fluctuation in server-side applications.We evaluated the results of the proposed system against state of the art TPS of Oracle Corporation(by a client-server-based simulator)and concluded that our system outperformed in terms of both;the response times and throughput.
基金supported by the PetroChina Prospective,Basic,and Strategic Technology Research Project(No.2021ZG03-02 and No.2023DJ8402)。
文摘The deep learning algorithm,which has been increasingly applied in the field of petroleum geophysical prospecting,has achieved good results in improving efficiency and accuracy based on test applications.To play a greater role in actual production,these algorithm modules must be integrated into software systems and used more often in actual production projects.Deep learning frameworks,such as TensorFlow and PyTorch,basically take Python as the core architecture,while the application program mainly uses Java,C#,and other programming languages.During integration,the seismic data read by the Java and C#data interfaces must be transferred to the Python main program module.The data exchange methods between Java,C#,and Python include shared memory,shared directory,and so on.However,these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks.Considering the large volume of seismic data and the need for network support for deep learning,this paper proposes a method of transmitting seismic data based on Socket.By maximizing Socket’s cross-network and efficient longdistance transmission,this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system.Furthermore,the actual production application shows that this method effectively solves the shortage of data transmission in shared memory,shared directory,and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.
