Live virtual machine (VM) migration (simply live migration) is a powerful tool for managing data center resources. Live migration moves a running VM between different physical machines without losing any states such as network conditions and CPU status. Live migration has attracted the attention of academic and industrial researchers since replacing running VMs inside data centers by live migration makes it easier to manage data center resources. This paper summarizes live migration basics and techniques for improving them. Specifically, this survey focuses on software mechanisms for realizing basic live migration, improving its performance, and expanding its applicability. Also, this paper shows research opportunities that the state-of-the-art live migration techniques have not covered yet.
We propose subtle foot-based gestures named foot plantar-based (FPB) gestures that are used with sock-style pressure sensors. In this system, the user can control a computing device by changing his or her foot plantar distributions, e.g., pressing the floor with his or her toe. Because such foot movement is subtle, it is suitable for use especially in a public space such as a crowded train. In this work, we focus on a user-defined gesture that is designed by the end-users, not developers of this system. We first conduct a guessability study that asks people what is the appropriate gesture for a specific command to control the computing device. Then, we implement a gesture recognizer with a machine learning technique. To avoid unexpected gesture activations, we also collect foot plantar pressure patterns made during daily activities such as walking, as negative training data. Finally, we conclude with several applications to further illustrate the utility of FPB gestures.
We introduced an interactive extension mechanism, which allows us to extend programs and systems verified with Coq. In the mechanism, only adding new constructors into existing inductive types are allowed, but adding parameters into functions or constructors are not. In this paper, we introduce a novel mechanism, which allows us to add new fields into record types. We also show the limitations of the mechanism arising from the type system of Coq. The method used in the mechanism is also applicable for adding parameters to existing constructors and functions. We explain problems occurring when we apply the method into them.
In this paper we propose a method to support Personal Software Process (PSP), which is a well known software process improvement framework for individual developers. The proposed method estimates developer's purposes (aims) from time-series data about developer's tasks, given by an execution history of software applications. We implemented the method by a machine learning algorithm, Random Forests. The experiment result shows the prediction with the time-series data is more accurate than the prediction without the time-series data. Especially, when using longer time-series data, accuracy of estimation became 97 %. It can be expected that the proposed method can help developers' process improvement as they become aware of how much time they spent on a specific aim such as implementation and testing.
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