| dc.description.abstract | Due to the increasing amount of internet users and fast growing of internet technique in recent years, traditional internet structure cannot deal with such huge amount of users and change anymore. As a results, in order to reduce the huge cost of operating internet and more use hardware resources efficiently. Cloud computing comes into existence. Cloud computing is a new concept derived from distributed systems. It has contains advantages of high expansion, fast distribution, high reliability, and combining deciding payment by using amount of public computing. Thus, many cloud service providers has mushroomed in few years, such as Amazon EC2, Google App Engine, Microsoft Azure, etc.
Cloud computing is still in development. When users need to access resources from different cloud service providers, user has to query the query system from cloud service providers; however, there is still no consistent and efficient query mechanism. Hence, in order to solve this problem, this thesis propose two different mechanisms, The first mechanism is a multi-attribute range query mechanism based on Distribution Hash Table (DHT) in cloud computing. By this mechanism, users can fast query resources from different clouds. The proposed query mechanism in this thesis enhances Content Addressable Network (CAN) with locality in peer-to-peer network, and therefore contains characteristics of distribution, high expansion, and preventing prevention of single peer breakdown. Furthermore, the simulation resulted by PeerSim show that the average query hop number of DMMRQ is faster than CAN by 31.43%, and the load balance between peers enhances 24.71%.
The second proposed mechanism is Agent-based Cloud Query Mechanism (ACQM) implemented on embedded systems is the second proposed mechanism in this thesis. Cloud agent will search resource messages from different clouds in place of users. What users need to do is just install a application but doesn’t need to maintain the list and protocol of cloud information. Consequently, users can get resource messages from different clouds easily. By the experiment, show that when cloud agent is built on home gateway embedded board, it could sustain the online amount of about 300 clients at the same time. This represents the low overhand of ACQM.
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