Using remote method invocation (RMI) and a distributed object-oriented technique, this paper presents a systematic approach to developing a manufacturing execution system (MES) framework, which is open, modularized, d...Using remote method invocation (RMI) and a distributed object-oriented technique, this paper presents a systematic approach to developing a manufacturing execution system (MES) framework, which is open, modularized, distributed, configurable, interoperable and maintainable. Moreover, the design patterns for the framework .are developed and a variety of functional components are designed by inheriting appropriate patterns. And then an application is constructed by invoking corresponding methods of related components. An MES system implementing the framework and design patterns can be facilely integrated with other manufacturing applications, such as enterprise resource planning (ERP) and floor control system (FCS) .展开更多
A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs crea...A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs create a trusted application(TA)in the TEE to process sensitive information,such as payment or message encryption,which is transparent to the APPs running in the rich execution environments(REEs).In detail,the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs.In this paper,we first present a comprehensive analysis of the performance of open-source TEE encrypted text.We then propose a high energy-efficient task scheduling strategy(ETS-TEE).By leveraging the deep learning algorithm,our policy considers the complexity of TA tasks,which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server.The results show that compared with the default scheduling strategy on the local device,our approach achieves an average of 38.0%energy reduction and 1.6×speedup.This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications,so that the trusted execution environment has both security and high performance.展开更多
基金The National Natural Science Foundation of China (59990470).
文摘Using remote method invocation (RMI) and a distributed object-oriented technique, this paper presents a systematic approach to developing a manufacturing execution system (MES) framework, which is open, modularized, distributed, configurable, interoperable and maintainable. Moreover, the design patterns for the framework .are developed and a variety of functional components are designed by inheriting appropriate patterns. And then an application is constructed by invoking corresponding methods of related components. An MES system implementing the framework and design patterns can be facilely integrated with other manufacturing applications, such as enterprise resource planning (ERP) and floor control system (FCS) .
基金supported by the National Natural Science Foundation of China (No.61902229)Fundamental Research Funds for the Central Universities (No.GK202103084).
文摘A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs create a trusted application(TA)in the TEE to process sensitive information,such as payment or message encryption,which is transparent to the APPs running in the rich execution environments(REEs).In detail,the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs.In this paper,we first present a comprehensive analysis of the performance of open-source TEE encrypted text.We then propose a high energy-efficient task scheduling strategy(ETS-TEE).By leveraging the deep learning algorithm,our policy considers the complexity of TA tasks,which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server.The results show that compared with the default scheduling strategy on the local device,our approach achieves an average of 38.0%energy reduction and 1.6×speedup.This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications,so that the trusted execution environment has both security and high performance.
