Ha Manh Tran

Applying mapreduce framework to peer-to-peer computing applications

MapReduce is a programming framework for processing large amount of data in distribution. MapReduce implementations, such as Hadoop MapReduce, basically operate on dedicated clusters of workstations to achieve high performance. However, the dedicated clusters can be unrealistic for users who infrequently have a demand of solving large distributed problems. This paper presents an approach of applying the MapReduce framework on peer-to-peer (P2P) networks for distributed applications. This approach aims at exploiting leisure resources including storage, bandwidth and processing power on peers to perform MapReduce operations. The paper also introduces a prototyping implementation of a MapReduce P2P system, where the main functions of peers contain contributing computing resources, forming computing groups and executing the MapReduce operations. The performance evaluation of the system has been compared with the Hadoop cluster using the prevailing word count problem.

Evaluation of the Distributed Case-Based Reasoning System on a Distributed Computing Platform

DisCaRia is a distributed case-based reasoning system used for fault resolution. This system features peer-to-peer and case-based reasoning approaches that provide the capability of exploring various fault data sources and exploiting fault solving knowledge from these distributed sources. This paper focuses on the prototype implementation and the performance evaluation of DisCaRia. The prototype system has been deployed on a distributed computing and storage testbed called EmanicsLab. This paper describes the results obtained from experiments performed on various software bug datasets.

DisCaRia—Distributed Case-Based Reasoning System for Fault Management

Fault resolution in communication networks and distributed systems is a challenge that demands the expertise of system administrators and the support of multiple systems, such as monitoring and event correlation systems. Trouble ticket systems are frequently used to organize the workflow of the fault resolution process. In this context, we introduce DisCaRia, a distributed case-based reasoning system that assists system administrators and network operators in resolving faults. DisCaRia integrates various fault knowledge resources that are already available in the Internet, and it exploits them by applying a distributed case-based reasoning methodology, which is based on scalable peer-to-peer technology. We present the architecture of DisCaRia, the key algorithms used by DisCaRia, and provide an evaluation of a prototype implementation of the system.

Vision-based approach for urban vehicle detection & classification

Vehicles detection and classification are the most popular subjects in the computer vision researching field, and also are the most important parts in any traffic monitoring or surveillance system. Although there has been a considerable amount of ideas to accommodate this problem since the 90s, many problems are still unresolved due to the complexity of traffic systems and the variety of vehicles. This paper is a work-in-process that proposes a new approach to detect and classify vehicles based on the traffic system in Vietnam. The main goal of this method is to group vehicles into 2 main classes, which are 2-wheeled and 4-wheeled vehicles, based on low-level traffic parameters in urban areas.

Software Bug Ontology Supporting Semantic Bug Search on Peer-to-Peer Networks

This article presents a semantic bug search system that assists users in sharing and searching solutions for similar bug reports on peer-to-peer networks. This system features the capability of exploring bug knowledge resources at various sites in distributed environment and exploiting the classification and relationship information of bug reports. The system uses a unified bug schema that not only integrates bug reports from various bug knowledge resources into a single database but also contains several selective properties including package dependencies, bug relationships, bug symptoms and categories to foster a semantic search mechanism. We have implemented several components of the system including bug updater to maintain bug database, query handler to share and search bug reports, and peer controller to manage communication on an appropriate peer-to-peer network. We have experimented the system on a distributed computing testbed and measured its feasibility, scalability and efficiency.

Distributed Event Monitoring for Software Defined Networks

Software defined network separates data and control planes that facilitate network management functions, especially enabling programmable network control functions. Event monitoring is a fault management function involved in collecting and filtering event notification messages from network devices. This study presents an approach of distributed event monitoring for software defined network. Monitoring events usually deals with a large amount of event log data, log collecting and filtering processes thus require a high degree of automation and efficiency. This approach takes advantage of the OpenFlow and syslog protocols to collect and store log events obtained from network devices on a syslog server. It also uses the adaptive semantic filtering method to filter and present non-trivial events for system administrators to take further actions. We have evaluated this approach on a network simulation platform and provided some log collection and filtering results with analysis.

Fault Data Analytics Using Decision Tree for Fault Detection

Monitoring events on communication and computing systems becomes more and more challenging due to the increasing complexity and diversity of these systems. Several supporting tools have been created to assist system administrators in monitoring an enormous number of events daily. The main function of these tools is to filter as many as possible events and present non-trivial events to the administrators for fault analysis and detection. However, non-trivial events never decrease on large systems, such as cloud computing systems, while investigating events is time consuming. This paper proposes an approach for evaluating the severity level of an event using a classification and regression decision tree. The approach aims to build a decision tree based on the features of old events, then use this tree to decide the severity level of new events. The administrators take advantages of this decision to determine proper actions for the non-trivial events. We have implemented and experimented the approach for software bug datasets obtained from bug tracking systems. The experimental results reveal that the accuracy scores for different decision trees are above 70i¾?% and some detailed analyses are provided.

Probabilistic Model and Neural Network for Scene Classification in Traffic Surveillance System

Traffic surveillance system (TSS) has seen great progress in the last several years. Many algorithms have been developed to cope with a wide range of scenarios such as overcast, sunny weather that created shadows, rainy days that result in mirror reflection on the road, or nighttime when low lighting conditions limit the visual range. However, in real-world applications, one of the most challenging problems is the scene determination in a highly dynamic outdoor environment. As also pointed out in recent survey, there have been limited studies on a mechanism for scene recognition and adapting appropriate algorithms for that scene. Therefore, this research presents a scene recognition algorithm for all-day surveillance. The proposed method detects and classifies outdoor surveillance scenes into four common types: overcast, clear sky, rain, and nighttime. The major contributions are to help diminish hand-operated adjustment and increase the speed of responding to the change of alfresco environment in the practical system. To obtain high reliable results, we combine the histogram features on RGB color space with the probabilistic model on CIE-Lab color space and input them into a feedforward neural network. Early experiments have suggested promising results on real-world video data.

A Study of Uber-based Applications

The Uber-based applications have recently created a new business model: a taxi company without any car, a tutor company without any tutor, or a hotel without any room. These applications coordinate mobile computing and peer-to-peer technology to facilitate the peer-to-peer provision of services. This paper presents a study of Uber-based applications. The paper first explains the driving force of mobile computing and peer-to-peer technology that exploit direct communications between mobile applications for services. It then describes a common application framework with the system architecture and prevailing components. We use virtual healthcare and software outsourcing case studies to demonstrate the prototyping systems with functions and evaluate service availability and performance.

Scene recognition in traffic surveillance system using Neural Network and probabilistic model

In the traffic surveillance system (TSS), there are many factors affect the qualities of the result. Through practical application, it is difficult to determine which scene changing during the day period, from the daylight to nighttime, the conversion of the sunny and overcast, wet and dry scene. However, there have been no controlled studies which illustrate the method to distinguish environment scene, which is the one of six main challenges in TSS. Therefore, this paper presents the method to detect and recognize the change of scene during all-day surveillance; Thus, TSS adopt the recognition to determine the appropriate method for each scene, for increasing performance. Our recognition model is based on the combination of the CIE-Lab color space and the histogram of the region-of-interest (ROI) in each frame, which used for extracting the feature for the Feed Forward Neural Network to perform the detection. In the experiment section, our results show that the benefits of our proposed method in the real-world traffic surveillance system.