Scheduling real-time applications in an open environment
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
A Fixed-Priority-Driven Open Environment for Real-Time Applications
RTSS '99 Proceedings of the 20th IEEE Real-Time Systems Symposium
Resource Partition for Real-Time Systems
RTAS '01 Proceedings of the Seventh Real-Time Technology and Applications Symposium (RTAS '01)
Periodic Resource Model for Compositional Real-Time Guarantees
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Compositional Analysis Framework Using EDP Resource Models
RTSS '07 Proceedings of the 28th IEEE International Real-Time Systems Symposium
A Hierarchical Multiprocessor Bandwidth Reservation Scheme with Timing Guarantees
ECRTS '08 Proceedings of the 2008 Euromicro Conference on Real-Time Systems
Optimal virtual cluster-based multiprocessor scheduling
Real-Time Systems
The Multi Supply Function Abstraction for Multiprocessors
RTCSA '09 Proceedings of the 2009 15th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Virtual Multiprocessor Platforms: Specification and Use
RTSS '09 Proceedings of the 2009 30th IEEE Real-Time Systems Symposium
RTSS '10 Proceedings of the 2010 31st IEEE Real-Time Systems Symposium
Response Time Analysis of Hierarchical Scheduling: The Synchronized Deferrable Servers Approach
RTSS '11 Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium
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The Multiprocessor Periodic Resource (MPR) model has been proposed for modeling compositional real-time guarantees of real-time systems which run on a shared multiprocessor hardware. In this paper we extend the MPR model such that the execution of virtual processors (servers) is not assumed to be synchronized i.e., the servers can have different phases. We believe that relaxing the server synchronization requirement provides greater deal of compatibility for implementing such a compositional method on various hardware platforms. We derive the resource supply bound function of the extended MPR model using an algorithm. Furthermore, we suggest an approach to calculate an approximate supply bound function with lower computational complexity for systems where calculating their supply bound function is computationally expensive.