Achieving Market Liquidity through Autonomic Cloud Market Management
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 SLA templates (i.e., templates for electronic contracts) is a means 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.
|Date of creation:||Aug 2011|
|Date of revision:||Aug 2011|
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- Michael Maurer & Vincent C. Emeakaroha & Ivona Brandic & Jorn Altmann, 2010. "Cost and Benefit of the SLA Mapping Approach for Defining Standardized Goods in Cloud Computing Markets," TEMEP Discussion Papers 201069, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Dec 2010.
- Marcel Risch & Ivona Brandic & Jorn Altmann, 2009. "Using SLA Mapping to Increase Market Liquidity," TEMEP Discussion Papers 200937, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Oct 2009.
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