TY - JOUR
T1 - Feedback control-based dynamic resource management in distributed real-time systems
AU - He, Tian
AU - Stankovic, John A.
AU - Marley, Michael
AU - Lu, Chenyang
AU - Lu, Ying
AU - Abdelzaher, Tarek
AU - Son, Sang
AU - Tao, Gang
PY - 2007/7
Y1 - 2007/7
N2 - 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.
AB - 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.
KW - Feedback control
KW - Quality of service
KW - Real-time
KW - Scheduling
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U2 - 10.1016/j.jss.2006.09.029
DO - 10.1016/j.jss.2006.09.029
M3 - Article
AN - SCOPUS:34248597366
SN - 0164-1212
VL - 80
SP - 997
EP - 1004
JO - Journal of Systems and Software
JF - Journal of Systems and Software
IS - 7
ER -