133 research outputs found
Neural Feedback Scheduling of Real-Time Control Tasks
Many embedded real-time control systems suffer from resource constraints and
dynamic workload variations. Although optimal feedback scheduling schemes are
in principle capable of maximizing the overall control performance of
multitasking control systems, most of them induce excessively large
computational overheads associated with the mathematical optimization routines
involved and hence are not directly applicable to practical systems. To
optimize the overall control performance while minimizing the overhead of
feedback scheduling, this paper proposes an efficient feedback scheduling
scheme based on feedforward neural networks. Using the optimal solutions
obtained offline by mathematical optimization methods, a back-propagation (BP)
neural network is designed to adapt online the sampling periods of concurrent
control tasks with respect to changes in computing resource availability.
Numerical simulation results show that the proposed scheme can reduce the
computational overhead significantly while delivering almost the same overall
control performance as compared to optimal feedback scheduling.Comment: To appear in International Journal of Innovative Computing,
Information and Contro
Game among Interdependent Networks: The Impact of Rationality on System Robustness
Many real-world systems are composed of interdependent networks that rely on
one another. Such networks are typically designed and operated by different
entities, who aim at maximizing their own payoffs. There exists a game among
these entities when designing their own networks. In this paper, we study the
game investigating how the rational behaviors of entities impact the system
robustness. We first introduce a mathematical model to quantify the interacting
payoffs among varying entities. Then we study the Nash equilibrium of the game
and compare it with the optimal social welfare. We reveal that the cooperation
among different entities can be reached to maximize the social welfare in
continuous game only when the average degree of each network is constant.
Therefore, the huge gap between Nash equilibrium and optimal social welfare
generally exists. The rationality of entities makes the system inherently
deficient and even renders it extremely vulnerable in some cases. We analyze
our model for two concrete systems with continuous strategy space and discrete
strategy space, respectively. Furthermore, we uncover some factors (such as
weakening coupled strength of interdependent networks, designing suitable
topology dependency of the system) that help reduce the gap and the system
vulnerability
Fuzzy Feedback Scheduling of Resource-Constrained Embedded Control Systems
The quality of control (QoC) of a resource-constrained embedded control
system may be jeopardized in dynamic environments with variable workload. This
gives rise to the increasing demand of co-design of control and scheduling. To
deal with uncertainties in resource availability, a fuzzy feedback scheduling
(FFS) scheme is proposed in this paper. Within the framework of feedback
scheduling, the sampling periods of control loops are dynamically adjusted
using the fuzzy control technique. The feedback scheduler provides QoC
guarantees in dynamic environments through maintaining the CPU utilization at a
desired level. The framework and design methodology of the proposed FFS scheme
are described in detail. A simplified mobile robot target tracking system is
investigated as a case study to demonstrate the effectiveness of the proposed
FFS scheme. The scheme is independent of task execution times, robust to
measurement noises, and easy to implement, while incurring only a small
overhead.Comment: To appear in International Journal of Innovative Computing,
Information and Contro
Control-theoretic dynamic voltage scaling for embedded controllers
For microprocessors used in real-time embedded systems, minimizing power
consumption is difficult due to the timing constraints. Dynamic voltage scaling
(DVS) has been incorporated into modern microprocessors as a promising
technique for exploring the trade-off between energy consumption and system
performance. However, it remains a challenge to realize the potential of DVS in
unpredictable environments where the system workload cannot be accurately
known. Addressing system-level power-aware design for DVS-enabled embedded
controllers, this paper establishes an analytical model for the DVS system that
encompasses multiple real-time control tasks. From this model, a feedback
control based approach to power management is developed to reduce dynamic power
consumption while achieving good application performance. With this approach,
the unpredictability and variability of task execution times can be attacked.
Thanks to the use of feedback control theory, predictable performance of the
DVS system is achieved, which is favorable to real-time applications. Extensive
simulations are conducted to evaluate the performance of the proposed approach.Comment: Accepted for publication in IET Computers and Digital Techniques.
doi:10.1049/iet-cdt:2007011
A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks
Centrality is widely recognized as one of the most critical measures to
provide insight in the structure and function of complex networks. While
various centrality measures have been proposed for single-layer networks, a
general framework for studying centrality in multilayer networks (i.e.,
multicentrality) is still lacking. In this study, a tensor-based framework is
introduced to study eigenvector multicentrality, which enables the
quantification of the impact of interlayer influence on multicentrality,
providing a systematic way to describe how multicentrality propagates across
different layers. This framework can leverage prior knowledge about the
interplay among layers to better characterize multicentrality for varying
scenarios. Two interesting cases are presented to illustrate how to model
multilayer influence by choosing appropriate functions of interlayer influence
and design algorithms to calculate eigenvector multicentrality. This framework
is applied to analyze several empirical multilayer networks, and the results
corroborate that it can quantify the influence among layers and multicentrality
of nodes effectively.Comment: 57 pages, 10 figure
- …