Virtualization is one of the key enablers in cloud computing. At the same time, though, it is also widely considered as a double-edged sword that may cause information leakage between virtual machines (VM) co-residing on the same physical server via various cross-VM covert channels. In this paper, we first explore the impact of different bystander workloads on cross-VM covert channels. Then, we use a Continuous Time Markov Process to model the impact of bystanders on the cross-VM covert channel in terms of both the work scheduling of the virtualization platform and the intensity of the bystander workloads. Based on empirical study, we quantify the relationship between the influential factors and the transmission quality of the covert channel. A tailored and lightweight VM provisioning strategy, which aims to ensure that bystander workloads on each server can cause sufficiently high error rates to covert channels, is proposed to mitigate the threat of cross-VM covert channels while maintaining the resource efficiency of virtualization. The efficiency and efficacy of the proposed VM provisioning strategy is evaluated through trace-driven simulations.

Touch input is increasingly popular nowadays, especially for mobile devices such as smartphones and tablet computers. However, the human finger has considerably large fingertip size and finger input is imprecise. As such, acquiring small targets on a touch screen is still a challenging task. In this paper, we present the LinearDragger, a new and integrated one-finger target acquisition technique for small and clustered targets. The proposed method has three advantages. First, it allows users to select targets in dense clustered groups easily with a single touch-drag-release operation. Second, it maps the 2D selection problem into a more precise 1D selection problem, which is independent of the target distribution. Third, it avoids finger occlusion and does not create visual distraction. As a result, it is particularly suitable for applications with dense targets and rich visual elements. Results of our controlled experiments show that when selecting small targets, LinearDragger takes about 70% and 30% less selection time than target acquisition without using any techniques and with the state-of-the-art target acquisition technique that involves a single touch operation, respectively, while maintaining a reasonable error rate.

We present the Implicit Fan Cursor (IFC) - a novel target pointing technique using a cursor with a fan-shape activation area. The IFC couples the cursor's activation area with its velocity, i.e., the speed and direction of the mouse motion, behaving like a 2D spotlight cursor at low speed and a circular area cursor at high speed. Thus, it enables the user to precisely acquire distant targets at low speed and easily acquire nearest targets at high speed, without explicit mode switching. This technique minimizes cursor movement, while taking into consideration of the precision of cursor movement at different speeds. It also ensures that only one target is captured at any time. The results of our controlled experiments show that the IFC outperforms the point cursor and the area cursor techniques, particularly in terms of cursor moving distance, and that its performance can be accurately modeled using the Fitts' law.