4,176 research outputs found
Self-optimization of optical confinement in ultraviolet photonic crystal slab laser
We studied numerically and experimentally the effects of structural disorder
on the performance of ultraviolet photonic crystal slab lasers. Optical gain
selectively amplifies the high-quality modes of the passive system. For these
modes, the in-plane and out-of-plane leakage rates may be automatically
balanced in the presence of disorder. The spontaneous optimization of in-plane
and out-of-plane confinement of light in a photonic crystal slab may lead to a
reduction of the lasing threshold.Comment: 5 pages, 5 figure
mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization
Self-adaptive software systems are often structured into an adaptation engine
that manages an adaptable software by operating on a runtime model that
represents the architecture of the software (model-based architectural
self-adaptation). Despite the popularity of such approaches, existing exemplars
provide application programming interfaces but no runtime model to develop
adaptation engines. Consequently, there does not exist any exemplar that
supports developing, evaluating, and comparing model-based self-adaptation off
the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based
architectural self-healing and self-optimization. mRUBiS simulates the
adaptable software and therefore provides and maintains an architectural
runtime model of the software, which can be directly used by adaptation engines
to realize and perform self-adaptation. Particularly, mRUBiS supports injecting
issues into the model, which should be handled by self-adaptation, and
validating the model to assess the self-adaptation. Finally, mRUBiS allows
developers to explore variants of adaptation engines (e.g., event-driven
self-adaptation) and to evaluate the effectiveness, efficiency, and scalability
of the engines
Self-Optimization of Internet Services with Dynamic Resource Provisioning
Self-optimization through dynamic resource provisioning is an appealing approach to tackle load variation in Internet services. It allows to assign or release resources to/from Internet services according to the varying load. However, dynamic resource provisioning raises several challenges among which: (i) How to plan a good capacity of an Internet service, i.e.~a necessary and sufficient amount of resource to handle the Internet service workload, (ii) How to manage both gradual load variation and load peaks in Internet services, (iii) How to prevent system oscillations in presence of potentially concurrent dynamic resource provisioning, and (iv) How to provide generic self-optimization that applies to different Internet services such as e-mail services, streaming servers or e-commerce web systems. This paper precisely answers these questions. It presents the design principles and implementation details of a self-optimization autonomic manager. It describes the results of an experimental evaluation of the self-optimization manager with a realistic e-commerce multi-tier web application running in a Linux cluster of computers. The experimental results show the usefulness of self-optimization in terms of end-user's perceived performance and system's operational costs, with a negligible overhead
Uncertainty handling in goal-driven self-optimization â limiting the negative effect on adaptation
Goal-driven self-optimization through feedback loops has shown effectiveness in reducing oscillating utilities due to a large number of uncertain factors in the runtime environments. However, such self-optimization is less satisfactory when there contains uncertainty in the predefined requirements goal models, such as imprecise contributions and unknown quality preferences, or during the switches of goal solutions, such as lack of understanding about the time for the adaptation actions to take effect. In this paper, we propose to handle such uncertainty in goal-driven self-optimization without interrupting the services. Taking the monitored quality values as the feedback, and the estimated earned value as the global indicator of self-optimization, our approach dynamically updates the quantitative contributions from alternative functionalities to quality requirements, tunes the preferences of relevant quality requirements, and determines a proper timing delay for the last adaptation action to take effect. After applying these runtime measures to limit the negative effect of the uncertainty in goal models and their suggested switches, an experimental study on a real-life online shopping system shows the improvements over goal-driven self-optimization approaches without uncertainty handling
Self-optimization, community stability, and fluctuations in two individual-based models of biological coevolution
We compare and contrast the long-time dynamical properties of two
individual-based models of biological coevolution. Selection occurs via
multispecies, stochastic population dynamics with reproduction probabilities
that depend nonlinearly on the population densities of all species resident in
the community. New species are introduced through mutation. Both models are
amenable to exact linear stability analysis, and we compare the analytic
results with large-scale kinetic Monte Carlo simulations, obtaining the
population size as a function of an average interspecies interaction strength.
Over time, the models self-optimize through mutation and selection to
approximately maximize a community fitness function, subject only to
constraints internal to the particular model. If the interspecies interactions
are randomly distributed on an interval including positive values, the system
evolves toward self-sustaining, mutualistic communities. In contrast, for the
predator-prey case the matrix of interactions is antisymmetric, and a nonzero
population size must be sustained by an external resource. Time series of the
diversity and population size for both models show approximate 1/f noise and
power-law distributions for the lifetimes of communities and species. For the
mutualistic model, these two lifetime distributions have the same exponent,
while their exponents are different for the predator-prey model. The difference
is probably due to greater resilience toward mass extinctions in the food-web
like communities produced by the predator-prey model.Comment: 26 pages, 12 figures. Discussion of early-time dynamics added. J.
Math. Biol., in pres
Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current
It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neuronĆ firing dynamics. This principle is shown to work in an analog VLSI-designed silicon neuron
Wireless Interference Identification with Convolutional Neural Networks
The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural networks (CNNs). The CNN naively learns its features through
self-optimization during an extensive data-driven GPU-based training process.
We propose a CNN example which is based upon sensing snapshots with a limited
duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
approaches and has a classification accuracy greater than 95% for
signal-to-noise ratio of at least -5 dB
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