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.

The reputation-based trust mechanism is a way to assess the trustworthiness of offered services, based on the feedback obtained from their users. In the absence of appropriate safeguards, service users can still manipulate this feedback. Auction mechanisms have already addressed the problem of manipulation by market-trading participants. When auction mechanisms are applied to trust systems, their interaction with the trust systems and associated overhead need to be quantitatively evaluated. This paper proposes two distributed architectures based on centralized and hybrid computing for integrating an auction mechanism with the trust systems. The empirical evaluation demonstrates how the architectures help to discourage users from giving untruthful feedback and reduce the overhead costs of the auction mechanisms.