Optimizing response time and synchronization in User Virtualization via adaptive memory compression

In this paper, we address the problem of prolonged latency periods for user log-in and log-off in User Virtualization using Virtual Pooled Desktops. These latency periods are caused due to large chunks of User Profile data transferred over the network in order to achieve User Virtualization. Thus we require efficient and quick `synchronization' of data from the client to the server. Data transfer time and file characteristics are factors that assist us to determine how synchronization can be improved. File type and File size are the key characteristics used to determine which compression algorithms will be applicable. Our contribution in this paper is the development of a new system for synchronization, using various compression algorithms and a `learning' based model. The learning model helps to determine whether or not to compress the file and plays an integral part in building a ranking system of compression algorithms, based on their performance on a specific type and size of a file.