Abstract.Recently IT giants such as Google, Amazon, Microsoft, and IBM are gearing up to be a part of the Cloud and begin to sell their cloud services. However, the current market trading mechanism is inflexible, and the price is not reasonable enough in some situation. Therefore, we first propose a cloud market framework for people to build a uniform and fully competitive cloud market where users can buy resources from different companies and exchange their idle resources in a more flexible way. Then we define a double auction Bayesian Game-based pricing model (DABGPM) for the suggested cloud market and discuss how to develop an optimal pricing strategy for this model. Our work, we think, makes a good example of more flexible and more reasonable cloud resources trading.
Recently, the Storage Networking Industry Association (SNIA) has released the first standard for cloud interoperability. With more and more standards for interoperability emerging, it can be expected that a global cloud resource exchange market will form. In such a market, it is challenging to present a dynamic pricing scheme to meet different requirements. To cope with the challenge, in this paper we first present a framework for constructing global cloud resource markets and then propose a knowledge-based continuous double auction (CDA) model that determines the price of cloud resources using a learning algorithm based on historical trading information. Experimental result shows that our model can attain high market efficiency as well as stable trading price.
A face recognition model based on Convolutional Neural Network was proposed in this paper. This model mixed local and global image features at the largest extent. By acquiring the good local feature from mouth, nose and mouth, it is great improvement to enhance the accuracy for face recognition. From the experiment results, this model can better abstract the global feature and show a great improvement in face recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.