The paper presents the embedded real-time software-oriented requirements engineering environment—SREZ. It involves the whole process of software requirements engineering, including the definition, analysis and checki...The paper presents the embedded real-time software-oriented requirements engineering environment—SREZ. It involves the whole process of software requirements engineering, including the definition, analysis and checking of requirements ,specifications. We first explain the principles of the executable specification language RTRSM. Subsequently, we introduce the main functions of SREE, illustrate the methods and techniques of checking requirements specifications, especially how to perform simulation execution, combining prototyping method with RTRSM and animated representations. At last, we compare the SREE with other requirements specifications methods and make a summary for SREE＇s advantages.展开更多
Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center.These applications are submitted to the cloud in the form of simulation jobs.Meanwhile,the man...Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center.These applications are submitted to the cloud in the form of simulation jobs.Meanwhile,the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service.In this paper,we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center,named simulation execution as a service(SimEaaS).It aims at releasing users from complex simulation running settings,while guaranteeing the QoS requirements adaptively.Furthermore,a novel job scheduling algorithm named adaptive deadline-aware job size adjustment(ADaSA)algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS.ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively,while guaranteeing that jobs’deadline requirements are not violated.Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA.The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time(up to 90%)and bounded slow down(up to 95%),while obtains approximately equivalent deadline-missed rate.ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate(up to 60%).展开更多
基金Supported by the National Natural Science Foun-dation of China(69873035) the K.C. Wong Education Foundation,Hong Kong,China
文摘The paper presents the embedded real-time software-oriented requirements engineering environment—SREZ. It involves the whole process of software requirements engineering, including the definition, analysis and checking of requirements ,specifications. We first explain the principles of the executable specification language RTRSM. Subsequently, we introduce the main functions of SREE, illustrate the methods and techniques of checking requirements specifications, especially how to perform simulation execution, combining prototyping method with RTRSM and animated representations. At last, we compare the SREE with other requirements specifications methods and make a summary for SREE＇s advantages.
基金supported by Scientific Research Plan of National University of Defense Technology under Grant No.ZK-20-38National Key Research&Development(R&D)Plan under Grant No.2017YFC0803300+2 种基金the National Natural Science Foundation of China under Grant Nos.71673292,71673294,61503402 and 61673388National Social Science Foundation of China under Grant No.17CGL047Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion.
文摘Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center.These applications are submitted to the cloud in the form of simulation jobs.Meanwhile,the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service.In this paper,we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center,named simulation execution as a service(SimEaaS).It aims at releasing users from complex simulation running settings,while guaranteeing the QoS requirements adaptively.Furthermore,a novel job scheduling algorithm named adaptive deadline-aware job size adjustment(ADaSA)algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS.ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively,while guaranteeing that jobs’deadline requirements are not violated.Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA.The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time(up to 90%)and bounded slow down(up to 95%),while obtains approximately equivalent deadline-missed rate.ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate(up to 60%).
