Combining Response Surface Methodology with Numerical Methods for Optimization of Markovian Models

Kemper, Peter; Müller, Dennis; Thümmler, Axel

doi:10.1109/tdsc.2006.28

Cited by 14 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach is model free, well established and fairly straightforward to apply, but it is not implemented in any of the commercial packages. The primary drawback is the excessive use of simulation points in one area before exploring other parts of the search space [49,50]. This can be especially exacerbated when the number of input variables is large.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Zheng

Tan

Zhang

et al. 2010

Neural Comput & Applic

View full text Add to dashboard Cite

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization.

show abstract

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Zheng

Tan

Zhang

et al. 2010

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…However, both works do neither consider hardware resource assignment parameters nor multitenancy aspects and limit their evaluation to small database scale factors. Experiment design methods, such as fractional factorial and response surface designs are successfully used in [22] to approximate and optimize Markovian models, but the focus is put on optimization of a theoretical model rather than a real system.…”

Section: Related Workmentioning

confidence: 99%

Experiments or simulation? A characterization of evaluation methods for in-memory databases

Molka

Casale

2015

2015 11th International Conference on Network and Service Management (CNSM)

View full text Add to dashboard Cite

Abstract-The recent growth of interest for in-memory databases poses the question on whether established prediction methods such as response surfaces and simulation are effective to describe the performance of these systems. In particular, the limited dependence of in-memory technologies on the disk makes methods such as simulation more appealing than in the past, since disks are difficult to simulate. To answer this question, we study an in-memory commercial solution, SAP HANA, deployed on a high-end server with 120 physical cores. First, we apply experimental design methods to generate response surfaces that describe database performance as a function of workload and hardware parameters. Next, we develop a class-switching queueing network model to predict in-memory database performance under similar scenarios. By comparing the applicability of the two approaches to modeling multi-tenancy, we find that both queueing and response surface models yield mean prediction errors in the range 5%-22% with respect to mean memory occupancy and response times, but the accuracy for the latter deteriorates in response surfaces as the number of experiments are reduced, whereas simulation is effective in all cases. This suggests that simulation can be very effective in performance prediction for in-memory database management.

show abstract

“…Transient states are called phases in general. Continuous PH distribution can be categorized into several subclasses according to the structure of T [29]. When phase transition is acyclic, the corresponding PH distribution is called acyclic PH distribution (APH).…”

Section: Continuous Ph Distributionmentioning

confidence: 99%

Availability Analysis of Software Systems with Rejuvenation and Checkpointing

Zheng

Okamura

2021

Mathematics

View full text Add to dashboard Cite

In software reliability engineering, software-rejuvenation and -checkpointing techniques are widely used for enhancing system reliability and strengthening data protection. In this paper, a stochastic framework composed of a composite stochastic Petri reward net and its resulting non-Markovian availability model is presented to capture the dynamic behavior of an operational software system in which time-based software rejuvenation and checkpointing are both aperiodically conducted. In particular, apart from the software-aging problem that may cause the system to fail, human-error factors (i.e., a system operator’s misoperations) during checkpointing are also considered. To solve the stationary solution of the non-Markovian availability model, which is derived on the basis of the reachability graph of stochastic Petri reward nets and is actually not one of the trivial stochastic models such as the semi-Markov process and the Markov regenerative process, the phase-expansion approach is considered. In numerical experiments, we illustrate steady-state system availability and find optimal software-rejuvenation policies that maximize steady-state system availability. The effects of human-error factors on both steady-state system availability and the optimal software-rejuvenation trigger timing are also evaluated. Numerical results showed that human errors during checkpointing both decreased system availability and brought a significant effect on the optimal rejuvenation-trigger timing, so that it should not be overlooked during system modeling.

show abstract

Combining Response Surface Methodology with Numerical Methods for Optimization of Markovian Models

Cited by 14 publications

References 18 publications

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Experiments or simulation? A characterization of evaluation methods for in-memory databases

Availability Analysis of Software Systems with Rejuvenation and Checkpointing

Contact Info

Product

Resources

About