Abstract
Due to the large variety in computing resources, Cloud markets often suffer from a low probability of finding matches between consumers’ bids and providers’ asks, resulting in low market liquidity. The approach of service level agreement (SLA) templates (i.e., templates for electronic contracts) is a mean to reduce this variety as it channels the demand and supply. However, until now, the SLA templates used were static, not able to reflect changes in users’ requirements. To address this shortcoming, we introduce an adaptive approach for automatically deriving public SLA templates based on the requirements of market participants. To achieve this goal, we utilize clustering algorithms for grouping similar requirements and learning methods for adapting the public SLA templates to observed changes of market conditions. To assess the benefits of the approach, we conduct a simulation-based evaluation and formalize a utility and cost model. Our results show that the use of clustering algorithms and learning algorithms improves the performance of the adaptive SLA template approach.
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Acknowledgements
This work is supported by the Vienna Science and Technology Fund under the grant agreement ICT08-018 Foundations of Self-Governing ICT Infrastructures (FoSII), and by the National Research Foundation of the Ministry of Education, Science and Technology of Korea under the grant K21001001625-10B1300-03310.
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Breskovic, I., Maurer, M., Emeakaroha, V.C., Brandic, I., Altmann, J. (2012). Achieving Market Liquidity Through Autonomic Cloud Market Management. In: Ivanov, I., van Sinderen, M., Shishkov, B. (eds) Cloud Computing and Services Science. CLOSER 2011. Service Science: Research and Innovations in the Service Economy. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2326-3_5
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DOI: https://doi.org/10.1007/978-1-4614-2326-3_5
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