Based on SCR(Software Cost Reduction), this paper presents a formal mOdel analyzingSCR-style requirements- This model mainly apply state trans1ation rules, semantic computing rules and attributes to define formal seme...Based on SCR(Software Cost Reduction), this paper presents a formal mOdel analyzingSCR-style requirements- This model mainly apply state trans1ation rules, semantic computing rules and attributes to define formal sementics of a tabular notation in the SCR requirements method, and may automatically analyze requirements specifications to be specified by the SCR method. Combining with a simp1eexample, this paper introduces how to analyze consistency and completeness of requirements specifica-tlons.展开更多
Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pa...Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing（such as Hadoop） and stream processing（such as Spark） of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.展开更多
文摘Based on SCR(Software Cost Reduction), this paper presents a formal mOdel analyzingSCR-style requirements- This model mainly apply state trans1ation rules, semantic computing rules and attributes to define formal sementics of a tabular notation in the SCR requirements method, and may automatically analyze requirements specifications to be specified by the SCR method. Combining with a simp1eexample, this paper introduces how to analyze consistency and completeness of requirements specifica-tlons.
基金supported by the Discovery grant No.RGPIN 2014-05254 from Natural Science&Engineering Research Council(NSERC),Canada
文摘Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures,platforms, and applications. Analysis of monitoring data delivers insights of the system＇s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing（such as Hadoop） and stream processing（such as Spark） of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.
