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
Scalability is one of the most important quality attribute of softwareintensive systems,because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload.In order to ...Scalability is one of the most important quality attribute of softwareintensive systems,because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload.In order to achieve scalability,thread pool system(TPS)(which is also known as executor service)has been used extensively as a middleware service in software-intensive systems.TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime.In case of distributed-TPS(DTPS),another issue is the load balancing b/w available set of TPSs running at backend servers.Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload.Consequently,the performance of software-intensive system is suffered.Thus,in this paper,we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword.On the one hand,it effectively performs the load balancing(in case of overload situation)among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs.And on the other hand,its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size,because the pool size in each TPS is kept equal to the request rate on it,hence dynamically optimizes TPS.We evaluated the results of the proposed system against state of the art DTPSs by a clientserver based simulator and found that our system outperformed by sustaining smaller response times.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.
文摘Scalability is one of the most important quality attribute of softwareintensive systems,because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload.In order to achieve scalability,thread pool system(TPS)(which is also known as executor service)has been used extensively as a middleware service in software-intensive systems.TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime.In case of distributed-TPS(DTPS),another issue is the load balancing b/w available set of TPSs running at backend servers.Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload.Consequently,the performance of software-intensive system is suffered.Thus,in this paper,we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword.On the one hand,it effectively performs the load balancing(in case of overload situation)among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs.And on the other hand,its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size,because the pool size in each TPS is kept equal to the request rate on it,hence dynamically optimizes TPS.We evaluated the results of the proposed system against state of the art DTPSs by a clientserver based simulator and found that our system outperformed by sustaining smaller response times.
