1,099 research outputs found
Modelling multi-tier enterprise applications behaviour with design of experiments technique
Queueing network models are commonly used for performance modelling. However, through application development stage analytical models might not be able to continuously reflect performance, for example due to performance bugs or minor changes in the application code that cannot be readily reflected in the queueing model. To cope with this problem, a measurement-based approach adopting Design of Experiments (DoE) technique is proposed. The applicability of the proposed method is demonstrated on a complex 3-tier e-commerce application that is difficult to model with queueing networks
Microservices Architecture Enables DevOps: an Experience Report on Migration to a Cloud-Native Architecture
This article reports on experiences and lessons learned during incremental migration and architectural refactoring of a commercial mobile back end as a service to microservices architecture. It explains how the researchers adopted DevOps and how this facilitated a smooth migration
Migrating to Cloud-Native Architectures Using Microservices: An Experience Report
Migration to the cloud has been a popular topic in industry and academia in
recent years. Despite many benefits that the cloud presents, such as high
availability and scalability, most of the on-premise application architectures
are not ready to fully exploit the benefits of this environment, and adapting
them to this environment is a non-trivial task. Microservices have appeared
recently as novel architectural styles that are native to the cloud. These
cloud-native architectures can facilitate migrating on-premise architectures to
fully benefit from the cloud environments because non-functional attributes,
like scalability, are inherent in this style. The existing approaches on cloud
migration does not mostly consider cloud-native architectures as their
first-class citizens. As a result, the final product may not meet its primary
drivers for migration. In this paper, we intend to report our experience and
lessons learned in an ongoing project on migrating a monolithic on-premise
software architecture to microservices. We concluded that microservices is not
a one-fit-all solution as it introduces new complexities to the system, and
many factors, such as distribution complexities, should be considered before
adopting this style. However, if adopted in a context that needs high
flexibility in terms of scalability and availability, it can deliver its
promised benefits
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures
Cloud controllers support the operation and quality management of dynamic cloud architectures by automatically scaling the compute resources to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of architecture adaptation rules. However, for a cloud provider, deployed application architectures are black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. We propose the dynamic learning of adaptation rules for deployed application architectures in the cloud. We introduce FQL4KE, a self-learning fuzzy controller that learns and modifies fuzzy rules at runtime. The benefit is that we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to configure cloud controllers by simply adjusting weights representing priorities for architecture quality instead of defining complex rules. FQL4KE has been experimentally validated using the cloud application framework ElasticBench in Azure and OpenStack. The experimental results demonstrate that FQL4KE outperforms both a fuzzy controller without learning and the native Azure auto-scalin
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms
Using Bad Learners to find Good Configurations
Finding the optimally performing configuration of a software system for a
given setting is often challenging. Recent approaches address this challenge by
learning performance models based on a sample set of configurations. However,
building an accurate performance model can be very expensive (and is often
infeasible in practice). The central insight of this paper is that exact
performance values (e.g. the response time of a software system) are not
required to rank configurations and to identify the optimal one. As shown by
our experiments, models that are cheap to learn but inaccurate (with respect to
the difference between actual and predicted performance) can still be used rank
configurations and hence find the optimal configuration. This novel
\emph{rank-based approach} allows us to significantly reduce the cost (in terms
of number of measurements of sample configuration) as well as the time required
to build models. We evaluate our approach with 21 scenarios based on 9 software
systems and demonstrate that our approach is beneficial in 16 scenarios; for
the remaining 5 scenarios, an accurate model can be built by using very few
samples anyway, without the need for a rank-based approach.Comment: 11 pages, 11 figure
On the closure problem of the effective stress in the Eulerian-Eulerian and mixture modeling approaches for the simulation of liquid-particle suspensions
© 2019 Author(s). We address the closure problem of the phasic effective stress tensors in the Eulerian-Eulerian and mixture models, considering suspensions of identical particles dispersed in Newtonian liquids. First, after briefly describing the modeling approaches, we review the key mechanisms generating phasic stress and discuss the shortcomings of some constitutive expressions in reproducing important experimental observations. For dilute suspensions, these include the mixture viscosity rise with solid concentration whilst for dense suspensions, the occurrence of particle migration and the change of mixture rheology from Newtonian to non-Newtonian. We then use computational fluid dynamics simulations to compare results based on various stress tensor closures. In a first case study, the simulation results of a laminar flow in a horizontal pipe of a dilute suspension of particles dispersed in a Newtonian liquid are compared to experimental data obtained from the literature. We show that both the Eulerian-Eulerian and mixture models can predict pressure drops accurately but only if they are coupled with suitable experimental closures for the mixture rheology. In a second case study, we simulate the laminar flow of a dense suspension of identical particles dispersed in a Newtonian liquid through an abrupt expansion. We show that the particle concentration profile in the upstream tube, which develops owing to shear-induced particle migration, strongly affects the flow patterns downstream of the expansion. This migration must be modeled via an appropriate closure for the solid effective stress tensor; this allows capturing the sophisticated flow patterns in the expansion section
Pattern-based multi-cloud architecture migration
Many organizations migrate on-premise software applications to the cloud. However, current coarse-grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial-anderror. This paper presents Variability-based, Pattern-driven Architecture Migration .V-PAM), a migration method based on (i) a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability-based, Pattern-driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model
Dynamic Model of a Continuous Cold Rolling Mill
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryJoint Services Electronics Program / DAAB-07-67-C-0199U.S. Air Force / AFOSR 68-1579AAlcoa Foundatio
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