The Vision of Autonomic Computing
Computer
The utility business model and the future of computing services
IBM Systems Journal
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Towards an autonomic computing testbed
HotAC II Hot Topics in Autonomic Computing on Hot Topics in Autonomic Computing
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
A survey of autonomic computing—degrees, models, and applications
ACM Computing Surveys (CSUR)
Cloud Computing Principles and Paradigms
Cloud Computing Principles and Paradigms
Performance modeling: understanding the past and predicting the future
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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Cloud computing plays an increasingly important role in realizing scientific applications by offering virtualized compute and storage infrastructures that can scale on demand. In this paper we report on the design of a self-configuring adaptive framework for developing and optimizing scientific applications on top of Cloud technologies. Our framework relies on a MAPE-K loop, known from autonomic computing, for optimizing the configuration of scientific applications taking into account the three abstraction layers of the Cloud stack: the application layer, the execution environment layer, and the resource layer. By evaluating monitored resources, the framework configures the layers and allocates resources on a per job basis. The evaluation of configurations relies on historic data and a utility function that ranks different configurations regarding to the arising costs. The adaptive framework has been integrated into the Vienna Cloud Environment (VCE) and has been evaluated with a MapReduce application.