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

The case for cloud service trustmarks and assurance-as-a-service

Cloud computing represents a significant economic opportunity for Europe. However, this growth is threatened by adoption barriers largely related to trust. This position paper examines trust and confidence issues in cloud computing and advances a case for addressing them through the implementation of a novel trustmark scheme for cloud service providers. The proposed trustmark would be both active and dynamic featuring multi-modal information about the performance of the underlying cloud service. The trustmarks would be informed by live performance data from the cloud service provider, or ideally an independent third-party accountability and assurance service that would communicate up-to-date information relating to service performance and dependability. By combining assurance measures with a remediation scheme, cloud service providers could both signal dependability to customers and the wider marketplace and provide customers, auditors and regulators with a mechanism for determining accountability in the event of failure or non-compliance. As a result, the trustmarks would convey to consumers of cloud services and other stakeholders that strong assurance and accountability measures are in place for the service in question and thereby address trust and confidence issues in cloud computing

Patterns of Vertical Specialisation in Trade:Long-run Evidence for 91 Countries

We estimate the domestic value-added content in exports of manufacturing goods (VAX-D ratio) for 91 countries over the period from 1970 to 2013. We find a strong decline in the world VAX-D ratio since the mid-1980s mostly accounted for by the substitution of foreign for domestic intermediates. Using a breakpoint detection method, we identify three waves of vertical specialisation in the world economy: 1970-79, 1986-95 and 1996-08. We find that most countries (79) initiated a period of vertical specialisation at least once. We find strong evidence that the VAX-D ratio correlates negatively with GDP per capita, and that the negative slope is flattening out at higher levels of income

Do Global Value Chains Enhance Economic Upgrading? A Long View

Exporting through global value chains (GVCs) has recently been highlighted as a panacea for weak industrialisation trends in the South. We study the long-run effects of GVC participation for a large set of countries between 1970 and 2008. We find strong evidence for the positive effects on productivity growth in the formal manufacturing sector. This effect is stronger when the gap with the global productivity frontier is larger. However, we find no evidence for a positive effect on employment generation. These findings also hold in analyses of sub-sets of countries and industries and are robust to the inclusion of non-manufacturing employment

Study of moments of event shapes in e+e- annihilation using JADE data

Data from e+e- annihilation into hadrons collected by the JADE experiment at centre-of-mass energies between 14 GeV and 44 GeV were used to study moments of event shape distributions. The data were compared with Monte Carlo models and with predictions from QCD NLO order calculations. The strong coupling constant measured from the moments is alpha_S(M_Z) = 0.1286 +/- 0.0007 (stat) +/- 0.0011 (expt) +/- 0.0022 (had) +/- 0.0068 (theo), alpha_S(M_Z) = 0.1286 +/- 0.0072 (total error), consistent with the world average. However, systematic deficiencies in the QCD NLO order predictions are visible for some of the higher moments.Comment: JADE note 147 submitted as contributed paper to ICHEP 2004, corrected statistical error of 6 observable average and several typo

Measurement of the Strong Coupling Constant alpha_S from the Four-Jet Rate in e+e- Annihilation using JADE data

Data from e+e- annihilation into hadrons collected by the JADE experiment at centre-of-mass energies between 14 GeV and 44 GeV were used to study the four-jet rate as a function of the Durham algorithm's resolution parameter y_cut. The four-jet rate was compared to a QCD NLO order calculations including NLLA resummation of large logarithms. The strong coupling constant measured from the four-jet rate is alpha_S(M_Z) = 0.1169 +/- 0.0004 (stat) +/- 0.0012 (expt) +/- 0.0021 (had) +/- 0.0007 (theo), alpha_S(M_Z) = 0.1169 +/- 0.0026 (total error) in agreement with the world average.Comment: JADE note 146 submitted as contributed paper to ICHEP 200

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