Response Time in Mixed-Critical Pervasive Systems
Abstract
Pervasive computing systems at large scale rely on real-time scheduling on the top of distributed and networked computing environments. From an user experience perspective, while the requirements on the response time for specific applications might be different, the mixed-criticality in real-time scheduling, which provide diverse response time guarantee for applications, is often required. In this paper, we study the real-time scheduling problem in mixed-critical pervasive computing systems. We first analyze the response time requirements for common networked pervasive computing systems, and model the mixed-criticality using the minimum response time Quality-of-Service (QoS) that should be guaranteed even in the worst-case. Then, we propose to leverage Fixed-Priority Rate-Monotonic (FPRM) Scheduler for real-time scheduling. We evaluate FPRM using synthetic workloads generated according to the real-world pervasive computing systems. Both simulation experiments and worst-case analytical results show that, when sufficient resources are given, all pervasive computing tasks can be completed subject to the response time requirements strictly with mixed-criticality guarantees ensured.
Recommended Citation
S. Vaidhun et al., "Response Time in Mixed-Critical Pervasive Systems," Proceedings of the 14th IEEE International Conference on Ubiquitous Intelligence and Computing (2017, San Francisco, CA), Institute of Electrical and Electronics Engineers (IEEE), Aug 2017.
Meeting Name
14th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC (2017: Aug. 4-8, San Francisco, CA)
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Aug 2017