Authors:
Federico Chesani
;
Anna Ciampolini
;
Daniela Loreti
and
Paola Mello
Affiliation:
University of Bologna, Italy
Keyword(s):
Business Process Management, Map Reduce, Monitoring, Cloud Computing, Autonomic System.
Related
Ontology
Subjects/Areas/Topics:
Big Data Cloud Services
;
Cloud Applications Performance and Monitoring
;
Cloud Computing
;
Cloud Interoperability
;
Fundamentals
;
Platforms and Applications
Abstract:
The adoption of mobile devices and sensors, and the Internet of Things trend, are making available a huge quantity of information that needs to be analyzed. Distributed architectures, such as Map Reduce, are indeed providing technical answers to the challenge of processing these big data. Due to the distributed nature of these solutions, it can be difficult to guarantee the Quality of Service: e.g., it might be not possible to ensure that processing tasks are performed within a temporal deadline, due to specificities of the infrastructure or processed data itself. However, relaying on cloud infrastructures, distributed applications for data processing can easily be provided with additional resources, such as the dynamic provisioning of computational nodes. In this paper, we focus on the step of monitoring Map Reduce applications, to detect situations where resources are needed to meet the deadlines. To this end, we exploit some techniques and tools developed in the research field of
Business Process Management: in particular, we focus on declarative languages and tools for monitoring the execution of business process. We introduce a distributed architecture where a logic-based monitor is able to detect possible delays, and trigger recovery actions such as the dynamic provisioning of further resources.
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