6,775 research outputs found
Software Aging Analysis of Web Server Using Neural Networks
Software aging is a phenomenon that refers to progressive performance
degradation or transient failures or even crashes in long running software
systems such as web servers. It mainly occurs due to the deterioration of
operating system resource, fragmentation and numerical error accumulation. A
primitive method to fight against software aging is software rejuvenation.
Software rejuvenation is a proactive fault management technique aimed at
cleaning up the system internal state to prevent the occurrence of more severe
crash failures in the future. It involves occasionally stopping the running
software, cleaning its internal state and restarting it. An optimized schedule
for performing the software rejuvenation has to be derived in advance because a
long running application could not be put down now and then as it may lead to
waste of cost. This paper proposes a method to derive an accurate and optimized
schedule for rejuvenation of a web server (Apache) by using Radial Basis
Function (RBF) based Feed Forward Neural Network, a variant of Artificial
Neural Networks (ANN). Aging indicators are obtained through experimental setup
involving Apache web server and clients, which acts as input to the neural
network model. This method is better than existing ones because usage of RBF
leads to better accuracy and speed in convergence.Comment: 11 pages, 8 figures, 1 table; International Journal of Artificial
Intelligence & Applications (IJAIA), Vol.3, No.3, May 201
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Optimized multi-objective design of herringbone micromixers
This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.A design method which systematically integrates Computational Fluids Dynamics (CFD) with an optimization scheme based on the use of the techniques Design of Experiments (DOE), Function Approximation technique (FA) and Multi-Objective Genetic Algorithm (MOGA), has been applied to the shape optimization of the staggered herringbone micromixer (SHM) at different Reynolds numbers. To quantify the mixing intensity in the mixer a Mixing index is defined on the basis of the intensity of segregation of the mass concentration on the outlet section. Four geometric parameters, i.e., aspect ratio of the mixing channel, ratio of groove depth to channel height, ratio of groove width to groove pitch and the asymmetry factor (offset) of groove, are the design variables selected for optimization. The mixing index at the outlet section and the pressure drop in the mixing channel are the performance criteria used as objective functions. The Pareto front with the optimum trade-offs, maximum mixing index with minimum pressure drop, is obtained. Experiments for qualitative and quantitative validation have been implemented.This study is supported by the Dorothy Hodgkin Postgraduate Award (DHPA) of the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom and Ebara Research Co. Ltd. of Japan
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensive black-box optimization tasks, the constraint
optimization algorithm COBRA was proposed, making use of RBF surrogate modeling
for both the objective and the constraint functions. COBRA has shown remarkable
success in solving reliably complex benchmark problems in less than 500
function evaluations. Unfortunately, COBRA requires careful adjustment of
parameters in order to do so.
In this work we present a new self-adjusting algorithm SACOBRA, which is
based on COBRA and capable to achieve high-quality results with very few
function evaluations and no parameter tuning. It is shown with the help of
performance profiles on a set of benchmark problems (G-problems, MOPTA08) that
SACOBRA consistently outperforms any COBRA algorithm with fixed parameter
setting. We analyze the importance of the several new elements in SACOBRA and
find that each element of SACOBRA plays a role to boost up the overall
optimization performance. We discuss the reasons behind and get in this way a
better understanding of high-quality RBF surrogate modeling
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