A Knowledge Base Driven Solution
نویسندگان
چکیده
Cloud infrastructures needs to become more smarter and dynamic in managing resources, requiring new solutions and tools to cope with processes of configuration and reconfiguration, automatic scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution for automatically managing cloud infrastructures, and programming the monitoring aspects directly from the configuration and deploy phases. This means that the proposed solution can enforce smart cloud intelligence to almost all Cloud Configuration management systems and orchestrators by using REST calls and XML files. The solution proposed includes a Smart Cloud Engine, an advanced Supervisor and Monitor, and a Knowledge Base. The Knowledge Base is grounded on a cloud ontology modeling cloud resources and relationships, Service Level Agreements and their evolution, constraints, metrics for assessment, etc. The Knowledge Base enables the reasoning on cloud structures and permits to implement strategies of efficient smart cloud management and intelligence: (i) verify and validate the configurations and related changes according to the cloud model and available resources; (ii) easily generate the processing control for verifying cloud resource healthiness and service level agreement verification; (iii) take decision about dynamic reconfiguration on cloud, for example for scaling, balancing, migration, etc. The proposed solution has been validated in the context of ICARO project on complex cloud configurations with good performance and low operating workload. The validation has also assessed the reliability and the scalability.
منابع مشابه
Identifying and Ranking Development Drivers of Knowledge-based Technology-Driven Companies (Case study: Fars Province Science and Technology Park)
The purpose of this Study study is to identify and rank the development drivers of knowledge-based, technology-driven businesses. This work is conducted as a case study in Fars Province Science and Technology Park. It is a descriptive survey in terms of purpose since a part of its data is collected through questionnaires and is of surveying type because it describes the existing conditions. The...
متن کاملA comparison between knowledge-driven fuzzy and data-driven artificial neural network approaches for prospecting porphyry Cu mineralization; a case study of Shahr-e-Babak area, Kerman Province, SE Iran
The study area, located in the southern section of the Central Iranian volcano–sedimentary complex, contains a large number of mineral deposits and occurrences which is currently facing a shortage of resources. Therefore, the prospecting potential areas in the deeper and peripheral spaces has become a high priority in this region. Different direct and indirect methods try to predict promising a...
متن کاملExistence and Measurability of the Solution of the Stochastic Differential Equations Driven by Fractional Brownian Motion
متن کامل
Reconciling Data-derived Knowledge with Expert Rules Using Clustering
Successful construction of intelligent systems to automate human reasoning processes often requires the use of a variety of techniques, including symbolic rule-based representations of reasoning and data-driven reenement of the knowledge. Connicts may arise between knowledge derived from the data and expert rules, as evidenced by misdiagnoses which are not correctable using neural network reene...
متن کاملA Fuzzy Association Rule Mining Expert-Driven (FARME-D) Approach to Knowledge Acquisition
Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact ...
متن کامل