End-to-end utilization control in distributed real-time systems

Lu, Chenyang; Wang, X.; Koutsoukos, Xenofon

doi:10.1109/icdcs.2004.1281612

Cited by 25 publications

(16 citation statements)

References 11 publications

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“…The simulation set-up makes it possible to integrate and test various adaptive management algorithms (see e.g. [3,18,43,50,72]) including our own algorithms for DQMS and ACM as illustrated in Section 6.…”

Section: Simulationmentioning

confidence: 99%

Autonomic Middleware for Automotive Embedded Systems

Anthony

Chen

Törngren

et al. 2009

Autonomic Communication

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This chapter describes DySCAS: an advanced autonomic platform-independent middleware framework for automotive embedded systems. The concepts and architecture are motivated and described in detail, focusing on the need for, and achievement of, high flexibility and automatic run-time reconfiguration. The design of the middleware is positioned with respect to the way it overcomes the specific technical, environmental, and performance challenges of the automotive domain. Self-management is achieved in terms of automatic configuration for context-aware behavior, resource-use efficiency, and self-healing to handle run-time detected faults. The self-management is governed by the use of policies distributed throughout the middleware components. The simulation techniques that have been used for extensive validation are described and some key results presented. A reference implementation is presented, illustrating the way in which the various concepts and mechanisms can be realized and orchestrated.

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Section: Simulationmentioning

confidence: 99%

Autonomic Middleware for Automotive Embedded Systems

Anthony

Chen

Törngren

et al. 2009

Autonomic Communication

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“…The web server then adapts its resource allocation based on the predicted delay in the closed-loop. Model predictive control technique is applied to manage the CPU utilization in multiprocessor environment [20]. However, these approaches do not consider database-specific issues such as fine-grained data access delay prediction and integrated data service backlog adaptation.…”

Section: Related Workmentioning

confidence: 99%

Integrating Proactive and Reactive Approaches for Robust Real-Time Data Services

Zhou

Kang

2009

2009 30th IEEE Real-Time Systems Symposium

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Abstract-Real-time data services are needed in data-intensive real-time applications such as e-commerce or traffic control. However, it is challenging to support real-time data services, if workloads dynamically change based on the market or traffic status. To enhance the quality of real-time data services even in the presence of dynamic workloads, feedback control theory has been applied. However, a major drawback of feedback control is that it only reacts to performance errors. To improve the robustness of realtime data services, we develop a statistical feed-forward approach that proactively adapts the incoming load, if necessary, to support the desired real-time data service delay. Further, we integrate it with a feedback controller to compensate potential prediction errors and adjust the system behavior in a reactive manner for timely data services. Performance evaluation results acquired in our real-time data service testbed show that our integrated approach considerably reduces the average delay and transient delay fluctuations, while improving throughput compared to the tested baselines including the feed-forward-only and feedbackonly approaches.

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“…Feedback control has been applied to real-time scheduling [15], a real-time middleware [16], and a web server [14]. However, they do not consider databasespecific issues such as data service backlogs.…”

Section: Related Workmentioning

confidence: 99%

“…However, they do not consider databasespecific issues such as data service backlogs. In addition to feedback, feed-forward has been applied to support the desired performance in a web server [7] and real-time middleware [16]. Analogously, we predict the estimated database backlog before the database actually processes the submitted transactions using meta data, while adapting the burstiness of workloads, if necessary, to reduce the rejection rate before the next feedback control signal becomes available.…”

Section: Related Workmentioning

confidence: 99%

Backlog Estimation and Management for Real-Time Data Services

Kang

Zhou

2008

2008 Euromicro Conference on Real-Time Systems

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Real-time data services can benefit data-intensive real-time applications, e.g., e-commerce, via timely transaction processing using fresh data, e.g., the current stock prices. To enhance the real-time data service quality, we present several novel techniques for (1) database backlog estimation, (2) fine-grained closedloop admission control based on the backlog model, and (3) hint-based incoming load smoothing. Our backlog estimation and feedback control aim to support the desired service delay bound without degrading the data freshness critical for real-time data services. Workload smoothing, under overload, help the database admit and process more transactions in a timely manner by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service delay and throughput, our feedback-based admission control and probabilistic load smoothing considerably outperform the baselines, which represent the current state of the art, in the experiments performed in a stock trading database testbed.

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End-to-end utilization control in distributed real-time systems

Cited by 25 publications

References 11 publications

Autonomic Middleware for Automotive Embedded Systems

Autonomic Middleware for Automotive Embedded Systems

Integrating Proactive and Reactive Approaches for Robust Real-Time Data Services

Backlog Estimation and Management for Real-Time Data Services

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