A software synthesis tool for distributed embedded system design
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Stochastic Analysis of a Reseveration Based System
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Real-Time Digital Signal Processing of Phased Array Radars
IEEE Transactions on Parallel and Distributed Systems
Resource Reservation in Dynamic Real-Time Systems
Real-Time Systems
Real-Time Dwell Scheduling of Component-Oriented Phased Array Radars
IEEE Transactions on Computers
Performance guarantees by simulation of process
SCOPES '05 Proceedings of the 2005 workshop on Software and compilers for embedded systems
Real-time digital signal processing of component-oriented phased array radars
RTSS'10 Proceedings of the 21st IEEE conference on Real-time systems symposium
Real-time control system analysis: an integrated approach
RTSS'10 Proceedings of the 21st IEEE conference on Real-time systems symposium
Efficient and robust probabilistic guarantees for real-time tasks
Journal of Systems and Software
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This paper presents a design method for distributed systems with statistical, end-to-end real-time constraints, and with underlying stochastic resource requirements. A system is modeled as a set of chains, where each chain is a distributed pipeline of tasks, and a task can represent any activity requiring nonzero load from some CPU or network resource. Every chain has two end-to-end performance requirements: Its delay constraint denotes the maximum amount time a computation can take to flow through the pipeline, from input to output. A chain's quality constraint mandates a minimum allowable success rate for outputs that meet their delay constraints. Our design method solves this problem by deriving (1) a fixed proportion of resource load to give each task; and (2) a deterministic processing rate for every chain, in which the objective is to optimize the output success rate (as determined by an analytical approximation). We demonstrate our technique on an example system, and compare the estimated success rates with those derived via simulated on-line behavior.