Xuan-Qui Pham

An Index Scheme for Similarity Search on Cloud Computing using MapReduce over Docker Container

We consider the problem of similarity search over the large datasets in the distributed environment. The proposed framework employs the Vp-Tree algorithm that integrated on top of the MapReduce framework to achieve good performance as well as meet the scalability and fault tolerance requirements for the system while data scale up. Since VP-Tree algorithm was implemented initially for partition and searching data in the local disk access, we proposed a new approach to using it in the parallel environment. The key point of the Vp-Tree algorithm is that it distributed the similar data points into groups, thereby reducing number of data need to scan during the searching stage. Consequently, the response time of the entire system has been improved. Otherwise, we used an open source computer vision library OpenCV for detect the similarity among images in the dataset. We evaluate the performance of our proposed framework using a synthetic data to show the positive of our approach. The experiment shows that our proposed framework achieves 57% improvement in response time in comparison with running searching job in tradition Hadoop framework. We also compared our application running time on Docker container against VM-based environment. The result points out that deploy our system over Docker container provide higher performance than VM-based environment in term of response time.

Cloud-based real-time location tracking and messaging system: a child-care case study

Cloud Computing represents an attractive and cost-efficient of server-based computing and application service provider models. Together with Cloud Computing development, we have witnessed the rapid increasing of mobile devices industry. Smart-phone is now a well-functioning system of GPS navigation, 3G/4G mobile network, wifi technology and much more. By using Cloud Computing, traditional mobile applications often involve with many Cloud Services such as online data storage, collaboration, real-time monitoring, web, email, push messaging, database processing, compute processing and so on. As the extension of mobile applications, real-time location tracking and messaging system has become particularly important. In this paper, we present a cloud-based real-time location tracking and messaging system (CRLTMS), that consists a cloud-based push messaging service, namely Google Cloud Messaging (GCM), web server, database and GPS navigation data. An Android-based child-care application case study shows that the effectiveness of the system, which enables the server tracks and communicates synchronously to smart-phones in real-time manner.

A procedure to achieve cost and performance optimization for recovery in cloud computing

This research discusses a system architecture that comes up with potentially better resiliency and faster recovery from failures based on the renowned genetic algorithm. Additionally, we aim to achieve a globally optimized performance as well as a service solution that can remain financially and operationally balanced according to customer preferences. The proposed methodology has undergone numerous and severe evaluations to be proclaimed of their effectiveness and efficiency, even when put under tight comparison with other existing work.

Homography-Based Motion Detection in Screen Content

Screen content has some different characteristics from camera-captured content, such as large motion and repeating patterns which lead to low encoding speed and higher bit-rate. To cope with these problems, homography-based motion detection is proposed to better explore the temporal correlation in screen content. After detecting motion, the motion parameters are forwarded to JPEG encoder for motion compensated predictions. Therefore, it can improve the coding efficiency. Experimental results show the proposed algorithm achieves efficiency in terms of both encoding time and encoding complexity.