Binbin Zhang

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I/O virtualization performance is an important problem in KVM. In this paper, we evaluate KVM I/O performance and propose several optimizations for improvement. First, we reduce VM Exits by merging successive I/O instructions and decreasing the frequency of timer interrupt. Second, we simplify the Guest OS by removing redundant operations when the guest OS(More)
In this paper, we describe a whole-system live migration scheme, which transfers the whole system run-time state, including CPU state, memory data, and local disk storage, of the virtual machine (VM). To minimize the downtime caused by migrating large disk storage data and keep data integrity and consistency, we propose a three-phase migration (TPM)(More)
Virtual Machine (VM) cloning is to create a replica of a source virtual machine (parent virtual machine); the replica, also called child virtual machine, owns exactly the same executing status as parent virtual machine. Fast live cloning guarantees that, during the period of cloning, the services running on the parent virtual machine observe no performance(More)
Traditional association-rule mining only concerns the occurrence frequencies of the items in a binary database. In real-world applications, customers may buy several copies of the purchased items. Other factors such as profit, quantity, or price should be concerned to measure the utilities of the purchased items. High-utility itemsets mining was thus(More)
This paper addresses the robust speech recognition problem as a domain adaptation task. Specifically, we introduce an unsupervised deep domain adaptation (DDA) approach to acoustic modeling in order to eliminate the training–testing mismatch that is common in real-world use of speech recognition. Under a multi-task learning framework, the approach jointly(More)
Data mining is used to mine meaningful and useful information or knowledge from a very large database. Some secure or private information can be discovered by data mining techniques, thus resulting in an inherent risk of threats to privacy. Privacy-preserving data mining (PPDM) has thus arisen in recent years to sanitize the original database for hiding(More)
Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various(More)
In virtual machine systems, with the increase in the number of VMs and the demands of the applications, the main memory is becoming a bottleneck for the application performance. To improve paging performance and alleviating thrashing behavior for memory-intensive or I/O-intensive virtual machine workloads, we proposed hypervisor based remote paging, which(More)
This paper introduces dynamic paravirtualization, which imitates paravirtualization and aims at reducing VM exits of full virtualization with hardware support. In dynamic paravirtualization, VMM (virtual machine monitor) dynamically monitors and replaces the hot instructions, which cause most VM exits. It is transparent to the guest OS such that the legacy(More)
Mining useful information or knowledge from large databases has become a critical issue in recent years. Sequential patterns can be applied in many domains to analyze the customer or user behaviors, such as basket analysis, biological data or web click streams. Conventional approaches may re-mine the updated database in batch mode while sequences are(More)