Huu-Quoc Nguyen

Low cost real-time system monitoring using Raspberry Pi

Nowadays, the Closed-Circuit Television (CCTV) surveillance system is being utilized in order to keep peace and provide security to people. There are several defects in the video surveillance system, such as: picture is indistinct, anomalies cannot be identified automatically, a lot of storage spaces are needed to save the surveillance information, and prices remain relatively high. This paper describes the design and implementation of a low-cost system monitoring based on Raspberry Pi, a single board computer which follows Motion Detection algorithm written in Python as a default programming environment. In addition, the system uses the motion detection algorithm to significantly decrease storage usage and save investment costs. The algorithm for motion detection is being implemented on Raspberry Pi, which enables live streaming camera along with detection of motion. The live video camera can be viewed from any web browser, even from mobile in real-time.

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.

Gaussian process for predicting CPU utilization and its application to energy efficiency

For the past ten years, Gaussian process has become increasingly popular for modeling numerous inferences and reasoning solutions due to the robustness and dynamic features. Particularly concerning regression and classification data, the combination of Gaussian process and Bayesian learning is considered to be one of the most appropriate supervised learning approaches in terms of accuracy and tractability. However, due to the high complexity in computation and data storage, Gaussian process performs poorly when processing large input dataset. Because of the limitation, this method is ill-equipped to deal with the large-scale system that requires reasonable precision and fast reaction rate. To improve the drawback, our research focuses on a comprehensive analysis of Gaussian process performance issues, highlighting ways to drastically reduce the complexity of hyper-parameter learning and training phases, which could be applicable in predicting the CPU utilization in the demonstrated application. In fact, the purpose of this application is to save the energy by distributively engaging the Gaussian process regression to monitor and predict the status of each computing node. Subsequently, a migration mechanism is applied to migrate the system-level processes between multi-core and turn off the idle one in order to reduce the power consumption while still maintaining the overall performance.

Cost efficient real-time applications scheduling in mobile cloud computing

Real-time applications require the system, which has enough processing capacity to respond the results, complete all the tasks in time. Cloud computing represents an attractive and cost-efficient of server-based computing and application service provider models. Virtualization technology enables Cloud Service Providers (CSP) to dynamically allocate their resources based on the workload fluctuations from Cloud Consumers. Cloud Service Providers have to tradeoff using different types of virtual machines in order to satisfy the Quality of Service (QoS) about response time also achieve cost efficient when hiring Virtual Machine (VM) from Cloud Hosting Providers (CHP). In this paper, we propose the Cost efficient Real-time Applications Scheduling (CERAS) algorithm in Cloud Computing to solve aforementioned issue. We first develop the scheduling algorithm to schedule tasks while finding the optimal number of VMs needed so that applications execution time is minimized. Based on that optimal number, we develop the cost efficient algorithm, that finds the minimum cost which CSP has to pay for CHP. The experiment results show that how efficient the CERAS algorithm can guarantee applications deadline while achieving the optimal resources needed and cost, compares to other traditional approaches.

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.

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.