A control-theoretic approach to flow control
SIGCOMM '91 Proceedings of the conference on Communications architecture & protocols
QoS Negotiation in Real-Time Systems and Its Application to Automated Flight Control
IEEE Transactions on Computers
Feedback–Feedforward Scheduling of Control Tasks
Real-Time Systems
A Dynamic Quality of Service Middleware Agent for Mediating Application Resource Usage
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Design and Evaluation of a Feedback Control EDF Scheduling Algorithm
RTSS '99 Proceedings of the 20th IEEE Real-Time Systems Symposium
Analysis of a Reservation-Based Feedback Scheduler
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
Feedback Control Scheduling in Distributed Real-Time Systems
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Design, Implementation, and Evaluation of Differentiated Caching Services
IEEE Transactions on Parallel and Distributed Systems
Decentralized Utilization Control in Distributed Real-Time Systems
RTSS '05 Proceedings of the 26th IEEE International Real-Time Systems Symposium
Enhancing the Robustness of Distributed Real-Time Middleware via End-to-End Utilization Control
RTSS '05 Proceedings of the 26th IEEE International Real-Time Systems Symposium
Performance specifications and metrics for adaptive real-time systems
RTSS'10 Proceedings of the 21st IEEE conference on Real-time systems symposium
Feedback scheduling of priority-driven control networks
Computer Standards & Interfaces
A feedback-based decentralised coordination model for distributed open real-time systems
Journal of Systems and Software
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The resource management in distributed real-time systems becomes increasingly unpredictable with the proliferation of data-driven applications. Therefore, it is inefficient to allocate the resources statically to handle a set of highly dynamic tasks whose resource requirements (e.g., execution time) are unknown a prior. In this paper, we build a distributed real-time system based on the control theory, focusing on the computational resource management. Specifically, this work makes three important contributions. First, it allows the designer to specify the desired temporal behavior of system adaptation, such as the speed of convergence. This is in contrast to previous literature, specifying only steady-state metrics, e.g. the deadline miss ratio. Second, unlike QoS optimization approaches, our solution meets performance guarantees with no accurate knowledge of task execution parameters - a key advantage in a poorly modeled environment. Last, in contrast to ad hoc algorithms based on intuition and testing, we rigorously prove that our approach not only has excellent steady state behavior, but also meets stability, overshoot, and settling time requirements.