CReMeS: a CORBA compliant reflective memory based real-time communication service

Authors:
Sun-Tae Chung;Oscar González;Krithi Ramamritham;Chia Shen
Affiliations:
School of Electronic Engineering, Soongsil University, Seoul, Korea;Computer Science Dept., Univ. of Massachusetts, Amherst, Mass.;Computer Science Dept., Univ. of Massachusetts, Amherst, Mass.;Mitsubishi Electric Research Labs, Cambridge Research Lab, Cambridge, Mass.
Venue:
RTSS'10 Proceedings of the 21st IEEE conference on Real-time systems symposium
Year:
2000

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Abstract

We present CReMeS, a CORBA-compliant design and implementation of a new real-time communication service. The CReMeS architecture is based on MidART's Real-Time Channel-based Reflective Memory (RT-CRM) which (a) supports the separation of QoS specification between producer and consumer of data and (b) employs a user-level scheduling scheme for communicatinn real-time tasks. Although RT-CRM provides for predictable and scalable asynchronous commumcation infrastructure for applications, it is not a standard middleware. The desire to make its service widely available motivated us to develop CReMeS by adapting RTCRM for CORBA environments. As a result, CReMeS provides for efficient, predictable, and scalable communication between information producers and consumers. Since CReMeS provides a CORBA interface to applications and demands no changes to the ORB layer and the language mapping layer, it can run on non real-time Off-The-Shelf ORBs and enables applications on these ORBs to have scalable and end-to-end predictable asynchronous communication facility. In addition, an application designer can select whether to use an out-of-band channel or the ORB GlOP/IIOP for data communication. This permits a trade-off between performance, predictability and reliability. Experimental results demonstrate that our architecture can achieve better performance and predictability than a real-time implementation of the CORBA Event Service when the out-of-band channel is employed for data communication; it delivers better predictability with comparable performance when the ORB GlOP/IIOP is used.