Architectural Requirements for Cloud Computing Systems: An Enterprise Cloud Approach
Journal of Grid Computing
The SHARC framework for data quality in Web archiving
The VLDB Journal — The International Journal on Very Large Data Bases
Predicting cost amortization for query services
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
How to price shared optimizations in the cloud
Proceedings of the VLDB Endowment
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Cost models for view materialization in the cloud
Proceedings of the 2012 Joint EDBT/ICDT Workshops
QoS-Aware cloud service composition based on economic models
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Toward practical query pricing with QueryMarket
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Optimized data management for e-learning in the clouds towards Cloodle
Proceedings of the Fourth Symposium on Information and Communication Technology
A survey of smart data pricing: Past proposals, current plans, and future trends
ACM Computing Surveys (CSUR)
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Cloud computing, the new trend for service infrastructures requires user multi-tenancy as well as minimal capital expenditure. In a cloud that services large amounts of data that are massively collected and queried, such as scientific data, users typically pay for query services. The cloud supports caching of data in order to provide quality query services. User payments cover query execution costs and maintenance of cloud infrastructure, and incur cloud profit. The challenge resides in providing efficient and resource-economic query services while maintaining a profitable cloud. In this work we propose an economic model for self-tuned cloud caching targeting the service of scientific data. The proposed economy is adapted to policies that encourage high-quality individual and overall query services but also brace the profit of the cloud. We propose a cost model that takes into account all possible query and infrastructure expenditure. The experimental study proves that the proposed solution is viable for a variety of workloads and data.