Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks

Web server clusters require a reliable network management for increasing the quality of service (QoS). A load balancer system installed in a software-defined network (SDN) is one method that can improve the performance and availability of web server services. SDN is a dynamic and a programmable network management approach, and one protocol that supports it is OpenFlow. This research aims to design and analyse a model of a load balancer on OpenFlow networks, implementing a Weighted Round Robin (WRR) algorithm. The analysis process is conducted by measuring the value of a QoS web server performance parameters, such as response time, throughput, HTTP success, and loss connection. The results showed the WRR algorithm can be implemented for balancing a network system with dynamic resource allocation. The weight workload of each service can be obtained from the needs and existing network resources. The performance of a load balancer on an OpenFlow network is 57% better than in a traditional one for testing of response time conducted in a high connection. However, the throughput and HTTP success connection decreased by 2% and 10%, respectively, while HTTP loss connection increased by 49%

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

The resources of information technology and the availability of services on non-cloud network systems are limited. This constitutes problems for companies, especially in the efficient management of information technology. The high investment in infrastructure procurement is an obstacle in building centralized systems, including the adoption of cloud computing through Infrastructure as a Service (IaaS), as an elective solution. This research aims to analyze the performance of cloud servers on IaaS services using the parameters of cloud service availability, resource utilization, and throughput transfer which were implemented in companies engaged in the toll road concession sector. Furthermore, the results are expected to be a reference in supporting company decisions/policies related to cloud system adoption. The methodology involved the Network Development Life Cycle (NDLC), a system constituted by 6 (six) stages of management, namely user, proxy server, database, web service, monitoring service, and Remote Desktop Protocol (RDP). The results of cloud service availability indicate that the cloud system provides service availability (system interface, broad network access, and resource pooling). Furthermore, cloud systems have a significant performance on resource utilization (CPU) and throughput transfer parameters, while non-cloud systems only excel in response time and resource utilization (Memory) parameters. The overall result analysis based on this research scenario showed that the cloud system provides services according to user needs and has a better speed in data transmission, but has shortcomings in response time

An investigation into the use of 3G mobile communications to provide telehealth services in rural KwaZulu-Natal

This article has been made available through the Brunel Open Access Publishing Fund.Abstract Background: We investigated the use of third-generation (3G) mobile communications to provide telehealth services in remote health clinics in rural KwaZulu-Natal, South Africa. Materials and Methods: We specified a minimal set of services as our use case that would be representative of typical activity and to provide a baseline for analysis of network performance. Services included database access to manage chronic disease, local support and management of patients (to reduce unnecessary travel to the hospital), emergency care (up to 8 h for an ambulance to arrive), e-mail, access to up-to-date information (Web), and teleclinics. We made site measurements at a representative set of health clinics to determine the type of coverage (general packet radio service [GPRS]/3G), its capabilities to support videoconferencing (H323 and Skype™ [Microsoft, Redmond, WA]) and audio (Skype), and throughput for transmission control protocol (TCP) to gain a measure of application performance. Results: We found that none of the remote health clinics had 3G service. The GPRS service provided typical upload speed of 44 kilobits per second (Kbps) and download speed of 64 Kbps. This was not sufficient to support any form of videoconferencing. We also observed that GPRS had significant round trip time (RTT), in some cases in excess of 750 ms, and this led to slow start-up for TCP applications. Conclusions: We found audio was always so broken as to be unusable and further observed that many applications such as Web access would fail under conditions of very high RTT. We found some health clinics were so remote that they had no mobile service. 3G, where available, had measured upload speed of 331 Kbps and download speed of 446 Kbps and supported videoconferencing and audio at all sites, but we frequently experienced 3G changing to GPRS. We conclude that mobile communications currently provide insufficient coverage and capability to provide reliable clinical services and would advocate dedicated wireless services where reliable communication is essential and use of store and forward for mobile applications.The Royal Society, United Kingdom

Proposal for Supplier Relationship Management at PT XL Axiata Tbk

The telecommunication industry in Indonesia is growing rapidly in the last fifteen years. The habit slowly changed from voice and short messaging service into data. Nowadays, the challenge for the telecommunication company is to provide good quality of network. To fulfill the challenge, XL has been developing a partnerships scheme with suppliers and has been creating a performance measurement to keep partnerships work in the right path. The last supplier performance measurement report shows that the result is not meet the target. The unsatisfying result show that something wrong in the partnership with supplier. To find the core problem, this paper uses a Current Reality Tree (CRT) method. Based on CRT analysis, the core problem that arises is poor coordination. To eliminate core problem, a strategy is created. Strategic Supply Management is used as a framework to create strategy in eliminating core problem. Four critical enablers in the strategic supply management are analyzed to find solution for the problem. Those four critical enablers are organizational design, supply measurements, information systems, and human resources. The suggested and recommended solution based on analysis are improvement in business process, developing responsibility assignment matrix (RAM), create web-based application, and developing skill and knowledge people in supply organization

Data mining and predictive analytics application on cellular networks to monitor and optimize quality of service and customer experience

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems.Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization.Electrical and Mining EngineeringM. Tech (Electrical Engineering

Passive observation-based architectures for management of web services

Web Services technologies are emerging as the standard paradigm for program-to-program interactions over the Internet. A Web Service is any application that offers its functionalities through the Internet by publishing a description of its interfaces. Web Services are gaining more and more momentum and their utilization is being spread and even standardized in many areas including e -Government, e -Telecomm, e -Health, and digital imaging. The management of Web Services will play an important role for the success of this emerging technology and its adoption by both providers and consumers. As the technology matures and spreads, consumers are likely to be very picky and restrictive with regards to the quality of the offered Web Services. Another challenging factor for the management of Web Services is related to the diversity of platforms on which Web Services are developed and deployed. In this thesis, the focus is on the management of Web Services using passive observation with the intent to have open and platform-independent management architectures capable of assessing both functional and non-functional aspects of Web Services. The bulk of the observation process is carried out by model-based entities known as observers. These observers make use of formal model such as Finite State Machines, Communicating Finite State Machines, and Extended Finite State Machines. The proposed architectures include observers developed and deployed as Web Services: mono-observer architecture and multi-observer architectures. A single observer is enough for observation of a non-composite Web Service while a network of observers is preferred when observing a composite Web Service. Passive observation requires traces' collection mechanisms which are thoroughly studied and their performance compared for all architectures. A new approach for online observation based on Extended Finite State Machine is proposed to accelerate misbehaviors' detection. This approach proposes backward and forward walks in the model to reduce possible sets of states and values of variables. I adopted a pragmatic evaluation approach to assess each of my contributions: analytical analysis and proof, implementation, and real case studies. All components of management architectures have been studied, their complexities determined, developed, and deployed. The use cases used for the evaluation of the effectiveness of the architecture, including simple and composite Web Services, are also fully implemented and deployed

The Clarens Web Service Framework for Distributed Scientific Analysis in Grid Projects

Large scientific collaborations are moving towards service oriented architecutres for implementation and deployment of globally distributed systems. Clarens is a high performance, easy to deploy Web Service framework that supports the construction of such globally distributed systems. This paper discusses some of the core functionality of Clarens that the authors believe is important for building distributed systems based on Web Services that support scientific analysis

An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

A QoS monitoring system in a heterogeneous multi-domain DVB-H platform

The MobileTV, IPTV, and DVB standards (DVB-H/T) have been defined to offer mobile users interactive multimedia services with quality of service (QoS) consistency analogous to TV services. However, the market has yet to provide effective and economical solutions for the real-time delivery of such services to the corresponding transmitters over multi-domain IP networks. The monitoring system proposed in this paper enables the QoS in the IP networks involved in the delivery of real-time multimedia content to the transmitters to be ascertained. The system utilizes the QoS parameters defined in MPEG-2 Transport Streams to detect problems occurring in the heterogeneous multi-domain IP networks. The ability to detect problems having an adverse effect on QoS allows appropriate control actions to be determined to recover the QoS across the composite IP network. The design and implementation of the proposed QoS-Monitoring system (QoS-MS) is presented, followed by analysis of experimental results that demonstrate the feasibility of the system