Telecommunication Engineering

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Traffic Modeling using Power Consumption of Base Station: The case of Ethio telecom
(AAU, 2018-10) Tesfu, Hagos; Dereje, Hailemariam (PhD)
Wireless telecommunication networks have become fundamental to daily activities. The mobile telecommunication market in Ethiopia is grown significantly in the past few years. Recently there are about 67.5 million mobile subscribers served by 7,353 base stations. Now a day’s optimizing the energy consumption of wireless telecommunications infrastructure has become a new challenge for the research community, governments and industries in order to reduce CO2 emission and operational energy cost. In view of the above problems, operators should have to have proper strategy to own energy efficient network. Studying the relationship between traffic load and power consumption at a Base Station (BS) could help to have proper strategy. The real time hourly data of the power consumption and traffic load have been obtained from servers where measurement is performed on a fully operated base stations. In this thesis, we analyze and model traffic load based on power consumption at a BS using MATLAB, R-studio and Excel. The analysis show a direct relationship is obtained between base station traffic load and power consumption. According to the relationship, we develop a piecewise linear model for base stations serving GUL (GSM, UMTS and LTE) and GU (GSM and UMTS) technologies. This result can be input for energy saving techniques. This thesis also analyzes the power consumption data has lognormal distribution and the traffic load data has Weibull distribution.
Comparison of Machine Learning Techniques For Intrusion Detection System
(2018-10) Kelem, Birhane; Henock, Mulugeta (PhD)
The rapid growth in the ubiquity and sophistication of Information Communication Technology (ICT) and the emergence of new networking paradigms such as Cloud Computing (CC), and Internet of Things (IoT) have made vital changes in the globe. Computer network security is one of the most critical issue as attackers are also evolving dynamically. There should be a mechanism that fill the security vulnerability. One of the promising technique to ensure computer network security is the use of hybrid machine learning (ML) techniques which automate the process of intrusion detection in computer networks. In this research, six hybrid ML models were developed based on the Knowledge Discovery in Database (KDD) process model. The dataset used in this study has been taken from University of New Brunswick Institute (Canada Institute of Cyber Security). After selecting the dataset, preprocessing techniques such as filling missing records, reduce dimension, selecting the most relevant features, and finally normalize the dataset input using features scaling are performed. The hybrid ML models for intrusion detection systems (IDS) are implemented using Python programming language. In this work, a total of 274208 dataset records are used for the ML models evaluation. Out of this, 191945 datasets are used for training and a separate 82263 records are used as a testing set. The decision tree (DT) and neural network (NN) algorithms as supervised and K-means algorithm as unsupervised algorithms are applied in both without feature selection and with feature selection. The principal component analysis and decision tree (PCA-DT) model showed the best results in all performance parameters. The model has a prediction accuracy of 99.89% and the lowest false positive rate of 0.027%. Results confirm the effectiveness of our proposed methods.
Quality of Experience Evaluation for Addis Ababa UMTS Enterprise Data Customers
(AAU, 2018-10) Abera, Reesom; Beneyam, Berehanu (PhD)
Penetration of high-speed mobile data services including social media and video services has been significantly increased across the globe due to fast development of mobile network and smartphone technologies. A similar trend has also been seen in Ethiopia mainly due to large mobile network expansion projects of the sole incumbent operator ethio telecom, particularly for Universal Mobile Telecommunications System (UMTS) network. To achieve a successful business, mobile operators need to continuously monitor satisfaction of their customers and the quality of their networks to take timely marketing, network optimization and other relevant decisions. While traditionally the focus has been towards network Quality of Service (QoS), operators recently also provide attention to the aggregate user quality perception – Quality of Experience (QoE). Yet, a combined quality analysis for mobile networks in Ethiopia, particularly for the popular UMTS data service, has not been performed. In this thesis work, the detailed QoS and QoE evaluation for Addis Ababa UMTS data service including both network quality perspectives and user side perception focused on selected enterprise customers are presented. Assessment of quality monitoring process in ethio telecom has been done before conducting the evaluation on QoS and QoE. The evaluation is made based on download throughput, upload throughput and latency quality metrics collected from network management system (NMS), indoor walk test, crowdsourcing test using RTR-NetTest tool and contextually formulated survey questionnaire. In general, achieved throughput and latency results show that both QoS and QoE are not good and there is dissatisfaction of customers. For instance, the average download throughput results are 0.57 Mbps from NMS, 0.86 Mbps from indoor walk test and 2.53 Mbps from RTR-NetTest. These quantitative results are reflected in the perception result of the participants where a Mean Opinion Score value of 2.65 for the satisfaction of downloading files or video/music is achieved
Techno-economic Analysis of LTE Deployment Scenarios for Emerging City: A Case of Adama, Ethiopia
(AAU, 2018-10) Dechasa, Negash; Beneyam, Berehanu (PhD)
The exponential growth of demand for mobile broadband access in emerging cities has been pushing deployment of long-term evolution (LTE) mobile technology. However, technological advancement towards LTE alone cannot show the performance, acceptance, and economic viability of an investment without detail technical and economic feasibility assessment of possible LTE deployment alternatives. In this thesis, first potential LTE deployment scenarios analyzed and formulated through scenario planning method for emerging city. Then, techno-economic analysis consists of marketing forecast, radio access network dimensioning, cost and revenue modeling, and economic feasibility analysis for seven years study period assuming a monopoly telecom market using modified TERA model performed. For techno-economic evaluation, modified TERA model is implemented in the MATLAB. Results show that market potential and operating frequency have a great impact on network capacity, coverage and number of sites in the area which in turn influences the rate of return on investment. Deployment of LTE in 1800Mhz band under high and low demand capacity, and deployment of LTE in 2100Mhz band under high demand capacity are feasible with the payback period of less than 3.5 years for emerging city. From specially formulated deployments scenarios techno-economic results, coverage favored LTE deployment scenario (in 1800Mhz under low demand capacity) is technically and economically feasible for Adama city of Ethiopia in a monopoly telecom operator market with payback period of 3.25 years.
Comparison and Fine Tuning Empirical Pathloss Models at 1800MHZ and 2100MHZ Bands for Addis Ababa, Ethiopia
(AAU, 2018-10) Esayas, Andarge; Dereje, Hailemariam (PhD)
Pathloss models play a very important role in wireless communications in coverage planning, interference estimations, frequency assignments, Location Based Services (LBS), etc. They are used to estimate the average pathloss a signal experience at a particular distance from a transmitter. Inaccurate propagation models may result in poor coverage, poor quality of service or high investment cost. Both second generation (2G) and third generation (3G) networks in the city of Addis Ababa (AA), Ethiopia, have problems like poor coverage, low data throughput, call drops and others. One of the root causes of these problems is the use of untuned pathloss model during network planning. So, it is mandatory for the operators to select the best fit pathloss model and tune it according to the specific situation the pathloss model is used. This thesis compares three pathloss models; namely, COST231, ECC-33 and SUI and tunes the one that performs best in the specific area type. At 1800MHZ band, COST231 was best in estimating the measured path loss in urban areas with a Root Mean Squared Error (RMSE) of 3.27dB before tuning and the RMSE could improve to 3.25dB after tuning. COST231 was also best in suburban areas with an RMSE of 5.27dB. Tuning the model could improve the RMSE to 4.18dB. SUI was best in open areas. It has an RMSE value of 6.0dB before tuning. Tuning has improved the RMSE value to 4.91dB. At 2100MHZ, 25 sites are used to collect path loss data. Similar analysis was done in the three path loss models. Based on the analysis, SUI is found to be best in predicting the path loss for all the three morphology types. Although ECC-33 was equally competent for urban area sites, SUI could predict the path loss better for the overall all average measured path loss with an RMSE of 4.27dB. Tuning the model could improve the RMSE to 2.23dB. The measured path loss for suburban areas could also be better predicted by SUI with an RMSE value of 5.75dB before tuning and 2.57dB after tuning. Path loss in open areas can also be better predicted by SUI. It has an RMSE value of 6.53dB before tuning. An improvement in RMSE to 3.38dB could be achieved after tuning.
Performance Evaluation of Server Cluster Load balancing Algorithms Using SDN Open Flow Model
(AAU, 2018-10) Wubshet, Abebe; Yalemzewud, Negash (PhD)
Due to the growth in the network usage, there is huge traffic pressure to server clusters in the datacenters. To manage incoming traffic, operators choose a load balancing technology to assign the incoming traffic to server clusters. However, this classical load balancing technology has complexity and challenge in scalability, flexibility and manageability. But, the emerging software defined networking having application, control and infrastructure layers offers an advantage of low cost, scalable, programmable and easy management to server clusters load balancing activity by abstracting the low level functionality of the network. This study evaluates the performance of round robin, weighted round robin and least load server cluster load balancing algorithms using software defined networking open Flow model by providing ethio telecom’s enterprise shop customers relation Management system users’ data as input in order to answer a research question of which load balancing algorithms is better in terms of network performance parameters such as response time, transaction per second, throughput in SDN platform. Simulation has been used as a methodology to evaluate server load balancing algorithms using Open Flow model by creating a virtual environment with oracle virtual box. In addition, mininet simulation tool has used to create the network topology and POX controller used in the control layer of the open Flow model to do the performance evaluation of the load balancing algorithms. The simulation result shows that round robin algorithm is better than weighted round robin and least load server load balancing algorithms interms of response time (sec), transaction rate (trans/sec), throughput (MB/sec).
Enhancing Mobile Banking Service Availability Using Machine Learning
(2018-10) Said, Ahmed Said; Murad, Ridwan (PhD)
One of the main obstacles for adoption of mobile banking is that of security concern. This concern is becoming a reality in the case of mobile core inter-node protocol, Signaling System number 7 (SS7). SS7 was developed with the assumption of trusted network within and among operators. With growing number of value-added service providers and roaming partners connecting to operators, the trusted network is no longer a closed network. Attackers continue to exploit vulnerabilities of SS7 network to conduct attacks that compromise confidentiality, integrity and availability of mobile banking users and mobile network operators. In Ethiopia, Short Message Service (SMS) and Unstructured Supplementary Service Data (USSD) are mainly used for mobile banking. These services are both vulnerable to availability attacks. This thesis is an effort to detect SMS availability attacks on Mobile Application Part (MAP) layer of SS7. To mitigate these attacks, machine learning techniques using real SMS traffic data from ethio telecom is used for adaptive detection of abnormal SMS. A novel approach of using aggregation of Message Origination (MO) error codes is proposed for class feature extraction. A combination of expert judgments, literature reviews and information gain are used for optimal feature selection. As a result, it is recommended to use origination, destination, and mobile switching center address and write time as optimal features. To solve the problem of attack message detection, PART, Random Forest and J48 algorithms are compared. It is found that J48 has a superior performance with an accuracy of 98.6465% and model build time of 3.71 seconds.
An Improved Technique for Enterprise Service Bus Data Transformation: The Case of ethio telecom
(AAU, 2018-10-19) Alemtsehay, Kebede; Mesfin, Kifle (PhD)
Service Oriented Architecture (SOA) is an integration architecture approach that is based on the concept of a service and addresses, the requirements of loosely coupled, standards-based, and protocol independent distributed computing. SOA used Enterprise Service Bus (ESB) to realize its principle. ESB is architecture to overcome the limitation of traditional architectures. One of its main functions is Data Transformation, which is the conversion process of data structure among heterogeneous systems during integration. Data transformation consists of four data transformation functions which are name and value transformation, attribute aggregation and splitting. Its performance depends on the data exchange format and transformation functions complexity. Most ESB platforms adopt XML-based format as their common data model, which is quite time consuming on data transformation. The performance of XML based format is affecting the real time communication performance, mainly as complexity increases. The objective of this research is to analyze the performance of ESB data transformation functions and propose an improved technique for ESB data transformation data exchange formats. We used various research methods including literature review, informal interview and focus group discussion for gathering and analyzing relevant data. An improved technique for ESB data exchange formats is proposed that uses both XML and JSON formats based on complexity matrix in Complexity Analyses Algorithm (CAA). CAA uses JSON format for high complexity and XML for low complexity level. From the experiment result we have observed that JSON data format requires less time than XML data format while data complexities increases. So by implemented this improved technique we can enhance the performance of ESB data transformation.
Hybrid SARIMA-ELM-based Data Traffic Forecasting: The Case of UMTS Network in Addis Ababa, Ethiopia
(AAU, 2018-10-25) Getinet, Tesfaye; Dereje, Hailemariam (PhD)
In Universal Mobile Telecommunications Service (UMTS) network planning, data traffic demand is one critical input in deciding dimension of network elements. Past data collected from deployed UMTS network can be used to forecast future demand. In the context of ethio telecom, the sole telecom service provider in Ethiopia, the future demand forecast is, however, based on number of subscribers growth forecast obtained from marketing section. This approach assumes uniform data demand per subscriber to obtain the total data demand. Understandably, it does not utilize the data growth information which is already available in the network. Forecasting the traffic demand based on historical data from network can enhance the marketing inputs and the traffic model accuracy. In this regard, taking data from ethio telecom’s UMTS network, a prior research has used Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast a one month data traffic demand. However, the research did not consider the non-linearity observed in the data traffic. This thesis handles this non-linearity via a hybrid model that accounts the linearity with SARIMA model and the non-linearity via Extreme Learning Machine (ELM) model; here after called the hybrid SARIMA-ELM model. A one and half year (i.e., from April 2015 – June 2016) data traffic collected from five Radio Network Controllers (RNCs) of the UMTS network in the city of Addis Ababa is used for the forecast. The forecasting performance metrics are: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Square Error (MASE). The results indicate that the hybrid SARIMA-ELM model with SARIMA order of (0,1,1) (1,0,1)7 is selected with 3.75% increase in forecast than SARIMA only model. The outperform SARIMA-MLP, which has the second lower error, with 24.8% percentage error reduction.
Context Based Cloud Service Architecture: From Carrier Perspective
(2018-11) Aschalew, Gashaw; Fekade, Getahun (PhD)
Cloud computing is one of the most discussed topics in recent years among distributed systems. Many Information System (IS) professionals now consider cloud technology as the best solution to improve the growth of business in every sector. Today, there are a plethora of cloud service architectures that are generated by prior studies. However, there is no de facto architecture that can satisfy context-based client requirements. The provider dependent architectures are designed in a way to suit the services and products of the respective cloud providers. While others are targeted for specific cloud service model and cloud deployment model. These days, many organizations show interest to adopt cloud environment that is apt to their local settings. To this end, organizations require cloud service architecture tailored to their current context. On the other hand, carriers which adopt new technologies mostly with the recommendation of providers are challenged to satisfy client requirements. The study proposed a conceptual carrier-based cloud service architecture taking into consideration client’s requirements and the capability of the carrier. The architecture was designed with three major components, namely Infrastructure as a Service (IaaS), Cloud Service Broker (CSB) and telecom capability services. The architecture has also incorporated the Service Oriented Architecture (SOA) based Enterprise Service Bus (ESB) architecture for dynamically configurable virtualised services. A deployment environment is also constructed for the proposed architecture using the analysis result of the collected data from clients and carrier cloud provider. Industry best practice and standards have also been considered while generating the deployment environment. Finally, an evaluation of the proposed architecture has been conducted using experts review and it achieves a high degree of agreement among the participants.
Telecom Engineering (Information Systems (TIS) Stream)
(AAU, 2018-11) Werku, Melesse; Mesfin, Kifle (PhD)
In the rapid development of information technology, many enterprises are challenged by seamless information exchange and resource sharing requirement in a heterogeneous enterprise environment. However, service-oriented middleware technologies can provide a better solution in facilitating seamless resource sharing and information exchange among integrated enterprise applications. It is important to carefully design flexible and scalable integration architecture to create and support a well-performing enterprise system. In this research, a Data Synchronization Solution Model (DSSM) is designed and implemented to overcome a data synchronization problem in a heterogeneous enterprise environment. The case study and the research idea are initialized based on existing real problems in ethiotelecom working environment. The company faces data synchronization problem between integrated applications. The data in the different application databases have mismatch; for example, for the same customer its service status could be active in one application and barring in another. As a solution, a data synchronization solution model is proposed based on middleware and knowledgebase. The middleware is a message processing part that receives the incoming message, processes and forwarded the message to a destination suitable data format. The knowledgebase is a database for the common data exchanged among integrated applications; which becomes a common data reference for integrated applications. To implement and validate the data synchronization solution model we simulate a heterogeneous enterprise environment using virtual machines, with different operating systems and databases. The implementation and validation is performed based on the company’s SIM card replacement business process. To simulate the message processing, we design Simple Object Access Protocol (SOAP) message with similar message structure in the company. The message processor is designed and implemented using open source WSO2 enterprise integrator tool. The validation result indicates, the practicality of DSSM hypothesis to creating consistency and synchronize data among integrated applications in a heterogeneous environment. The result also shows the features and components of DSSM are functioning per the design specified in the research document. Moreover, unlike the existing system in which the case study is based upon, the new solution is flexible for maintenance and redesign.
Analysing Impact of Seamless MPLS on QoS
(AAU, 2018-11) Habtamu, Kumera; Yalemzewd, Negash (PhD)
The need for timely delivery of real time and mission-critical applications has led to a high demand for end-to-end QoS guarantees. Service providers, including Ethio telecom, have deployed mobile backhaul in addition to IP core network with separate MPLS domains for each network. Internal network topology of one domain and its policy implementations are proprietary information and inter-domain routing must be done without detailed knowledge of the entire network topology, policies and performance of the other domains. Lacks of global coordination between policies used in different domains, limitations of BGP for implementing end-toend QoS in inter-domain routing and its slow convergence during network failure are major challenges of the current inter-domain routing architecture. As stated by different authors such as in RFC 8277 (Using BGP to Bind MPLS Labels to Address Prefixes), Seamless MPLS provides framework for taking MPLS end-to-end in a scalable fashion, extending benefits of traffic engineering (TE) and guaranteed service-level agreements (SLAs) with deterministic network resiliency. It offers an alternative for implementing end-toend MPLS networks by integrating different domains into a single MPLS domain. The motivation of this thesis is to investigate and analyze impacts of implementing Seamless MPLS on QoS parameters. The impact on QoS parameters is analyzed by using two scenarios; MPLS with multiple domains and Seamless MPLS integrating three domains in to single MPLS domain. Simulation tools such as eNSP, Ostinato, NQA and MATLAB are used to compare performances of the two scenarios. The analysis results show that in Seamless MPLS throughput is improved by 36.87%, latency is improved by 15.98%, packet loss is improved by 20% and jitter is improved by 12.5% compared to MPLS. From the results one can understand that any service provider can benefit from deploying Seamless MPLS.
Short Message Service Fraud Mitigation Taxonomy: The Case of ethio telecom
(AAU, 2018-11) Tarikua, Worku; Mesfin, Kifle (PhD)
Telecommunication fraud is remaining challenging since the beginning of commercial telecom service. There are various reasons that makes telecom fraud mitigation inefficient. Some of them are; integration of new technologies without evaluating the security hole, lack of knowledges on the fraud root causes, the changing behavior of fraud and effective use of mitigation techniques. Short Message Service (SMS) is one of the main and victim telecom services. A variety of technologies, services and actors are involved on SMS ecosystem. This technology diversity makes the service vulnerable for different type of messaging frauds. In this study SMS fraud mitigation taxonomy is proposed to improve fraud mitigation deployment method. The taxonomy is constructed from four main nodes namely Technology (Which), Vulnerability (Where), Fraud (How) and Mitigation (What) as a cause and effect way. These main nodes are also categorized in to three sub technological layers which are network/protocol, service and actor. In addition to this classification the mitigation techniques are characterized as technical and none technical. The evaluation process is done first selecting 100,000 fraudulent short message records from ethio telecom. Then taking appropriate mitigation techniques from Network /protocol, Service and Actor layers. Finally, the selected records are examined by each layer mitigation techniques based on the fraud scheme. The layered evaluation result confirmed the proposed approach can mitigate 70% of the fraud messages at network and protocol level, 57.2% at service level, and 84% at actor level before any impact. So that overall efficiency of this taxonomy based layered mitigation approach is recommended to use, instead of detecting the fraud after impacting the service.
Cross Boarder Interference on UMTS Mobile Network and Its Impact on Revenue: the Case of Border Town Togowchale
(Addis Ababa University, 2018-11) Hagere, Demessie; Murad, Ridwan (PhD)
Universal Mobile Telecommunications System (UMTS) which has been standardized by the 3rd Generation partnership project (3GPP) organization, utilizes Wideband Code Division Multiple Access (WCDMA) as radio access technology. UMTS are designed to meet the increasing demands for high data rate applications and greater mobility. High data rate applications include voice, video telephony, Video streaming, File Transfer protocol (FTP), high quality image and wireless internet access. But there are many factors like loading of cell, interference etc. that limits to access these high data rate services or limits the capacity of the systems. The uplink channel is, by nature, an interference-constrained multiple-access channel. Cross border interference is caused due to the usage of the same set frequencies between two adjacent boundaries of different operators. This is the case where other operator locates their base stations in close proximity in order to provide comparable coverage to their customers. This thesis attempts to identify cross boarder interference, analysis to its impacts to the uplink capacity of all service in UMTS systems and loss of revenues is presented. The work starts with UMTS overview and learn some important concept about UMTS uplink channel interference. Then go deep into uplink channel capacity and apply the two load analysis methods to estimate the capacity loss. In the end revenue is calculated using the tariff incurred for each types of service. Ethio telecom can use this research to gain a better understanding of the problem in order to plan a strategy required to be profitable. The results described in this thesis will be a valuable contribution towards the awareness of how interference of another operator’s network (which we can’ t control) affects the performance of the network elements at boarder town.
Performance Evaluation of 6-Sector Site and Small Cell for Addis Ababa UMTS Deployment Scenario
(AAU, 2018-11) Tirufire, Aberra; Beneyam, Hailu (PhD)
The demand for high data rate has been increasing due to various innovative data services that improve human life. To accommodate the increasing demand with satisfactory Quality of Service (QoS), operators must continuously improve their network capacity. To that end, besides deploying a Fourth-Generation (4G) network, already operational Third-Generation (3G) network capacity can be improved by applying various capacity improving techniques including six sectorization, adding small cells, macro site densification, Multiple Input Multiple Output (MIMO) and using multiple carrier. In this thesis, capacity challenges of Addis Ababa Universal Mobile Telecommunications System (UMTS) network are analyzed using real data from network management system. Further, performance evaluation and comparison of six-sector sites, small cell deployment and their hybrid deployments are presented in the context of addressing the capacity challenges. The evaluation is performed using carrier to interference plus noise ratio (CINR) and throughput metrics and WinProp based propagation computation and network simulation by applying dominant path pathloss model over building map of selected deployment area of Addis Ababa. Results show that full deployment of six-sector has performance improvement compared to full 3-sector with 2.9 dB CINR and 284.23% DL throughput gains. In addition, 6-sector with 3-sector (hybrid) has 2.2 dB and 231.05% gain in CINR and DL throughput compared to full 3-sector deployment respectively. Furthermore, femto with 5% penetration has 0.3 dB CINR gain over full 3-sector. Based on the results, deployment of 6 sector site (both full and hybrid) can enhance capacity of UMTS network significantly and can elevate the capacity challenges faced. Furthermore, femto also has improved the capacity and with an increase in penetration ratio, the gain can be further increased.
Cell Outage Detection Through Density-based Local Outlier Data Mining Approach: In case of Ethio telecom UMTS Network
(AAU, 2018-11) Solomon, Bekele; Dereje, Hailemariam (PhD)
Mobile traffic growth increases exponentially over the years. To gratify the growing traffic, which requires capacity and coverage, densification of a network is a key solution. As mobile network becomes larger and larger, it is difficult to manage the network manually rather it requires automated network management. Self-healing is one of self-organizing network (SON’s) functionalities that implements automatic fault management in radio access network (RAN). In practice, mobile cell outage is the major problem in the radio access network and leads to the lack of network service. The automated and timely detection of a malfunctioning cell is one of the crucial challenges for network operators. In this thesis, data mining model has been introduced to detect cell outage automatically. Density-based Local Outlier Factor (LOF) detection algorithm, which is a decisive part of the model, has been adopted and implemented using incoming handover statistical data to detect cell outage and sleeping cells in self-organizing manner. For this purpose, statistical handover data has been collected from real UMTS network and then preprocessed using filtering, aggregation, normalization and then profiling. Moreover, an improved version of LOF algorithm, fast anomaly detection with duplication (FADD), has also been implemented to improve the detection capability. Receiver Operating Characteristic (ROC) curve is used to show the degree of the performance of the algorithms. The study shows that the two versions of LOF cell outage detectors have detected most cells in outage and locate their positions. But, FADD has detected 89% compared to 75% of the original LOF.
A Framework for Private Cloud Infrastructure Monitoring
(AAU, 2018-11) Selamawit, Belete; Mesfin, Kifle (PhD)
Cloud computing platforms consists large number of physical and virtual resources. With the increasing number of resources, cloud computing platforms management has become more and more complex. Hence, proper resource monitoring is needed. The knowledge of the environment, which we want to monitor help a lot on the monitoring process. Specifically, in private cloud the environment is known; the application which run on top of the infrastructure and the end users are predetermined. Relatively, this knowledge did not consider in private cloud monitoring systems so far. In monitoring systems, it is a well-known practice to define a static threshold as the alert condition, but it would emit some important information if the alert threshold is set to a progressive value (dynamic). This research paper primarily reviews different papers, white papers books and websites to understand state-of-the-art private cloud infrastructure monitoring and current monitoring practices. Then, a new framework is proposed which incorporate private cloud environment knowledge. Next to that, the solution has validated using actual cloud resource utilization, bitbrain workload trace data which is collected from a real cloud environment. This research work verified that the environmental knowledge of private cloud helps to implement dynamic threshold which help to increase resource utilization efficiency and QoS through elasticity. The new proposed framework has two contributions; first it allows private cloud owners to get private cloud-based monitoring system for their cloud environment and researchers in the area will get a better insight about private cloud monitoring.
Investigation and Optimization of Electrical Tilt and Azimuth for Addis Ababa LTE Network
(AAU, 2018-11) Taddege, Assefa; Beneyam, Berehanu (PhD)
Demand for mobile broadband connectivity is significantly increasing and mobile network operators are continuously improving their network by introducing new technologies to accommodate the demand. After mobile network is operational for the first time, to maximize network performance, it is common to perform further optimization of initially planned and configured network parameters. One important category of such network parameters that incur significant network performance impact is antenna parameters that mainly include antenna tilt and azimuth. Optimization of antenna tilt and azimuth plays a major role in optimization of coverage, capacity, load balancing and interference of mobile networks. Long Term Evolution (LTE) network is operational in some parts of Addis Ababa since 2015. This thesis work reviews Addis Ababa LTE network, deployed antenna solutions and ethio telecom antenna optimization practices. The work continues by evaluating the performance of this LTE network from electrical down tilt and azimuth steering perspective. Based on observed performance, the tilt and azimuth parameters are also optimized by applying educated trial and error approach. The performance evaluation and optimization of the network are performed through Signal-to-Interference-Plus-Noise-Ratio (SINR) and throughput parameters obtained using WinProp based network simulation backed by Matlab. Results show that, by optimizing electrical tilt, SINR and throughput can be improved by 12.5% and 4.4% respectively. On the other hand, there is negligible performance gain by optimizing the azimuth.
Constraint-Based Hybrid Resiliency Mechanisms for Better Resource Utilization and Service Performance Quality in ASON SLA
(AAU, 2018-11) Wondale, Kebede; Yalemzewd, Negash (PhD)
In optical transport networks, contents of service level agreement (SLA) are not standardized yet. There is a general recommendation to include network resiliency mechanisms in SLA contracts by considering trade-off between resource utilization and service performance quality. This research work proposes two solutions to fulfil these trade-off requirements in network resiliency mechanisms. The first solution is employing a Routing and Wavelength Assignment (RWA) algorithm that can utilize network resources efficiently in Automatically Switched Optical Network (ASON) service provisioning. The research work conducts blocking probability and network availability comparison simulations for Shared Risk Group (SRG)-Disjoint Aware First-Fit Routing, Alternate Routing, Least Congested Routing and Load Sharing routing algorithms coordinated with First-Fit wavelength assignment algorithm in 1+1 Dedicated Path Protection (DPP) and Restoration schemes. The simulations performed on Net2Plan tool show that Alternate Routing algorithm has best overall results in blocking probability and network availability for both protection and restoration. The second solution is enhancing service performance qualities by combining protection and restoration. This research work proposes new constraint-based hybrid resiliency mechanisms (1+1 Link-Disjoint + Restoration, 1+1 Node-Disjoint + Restoration and 1+1 SRG-Disjoint + Restoration). The performance of these hybrid resiliency mechanisms is evaluated using Net2plan simulation tool. The results show that the network availability and recoverability performances are improved when it is compared to non-combined counter parts. 1+1 Node-Disjoint + Restoration shows best recoverability at lower traffic loads during link or SRG failures. At higher traffic loads, 1+1 SRG-Disjoint + Restoration performs best in recoverability during SRG failures. For instance, 1+1 SRG-Disjoint + Restoration has on average 16.8% higher recoverability than 1+1 Link-Disjoint + Restoration, at higher traffic loads. These performance enhancements are obtained with cost of relatively higher blocking probability.
Neural Network based 3G Mobile Sites Fault Prediction: A Case Study in Addis Ababa, Ethiopia
(AAU, 2018-11) Asmelash, Tesfay; Yihenew, Wondie (PhD)
As cellular mobile networks are evolving in technology and service type, the number of mobile network site infrastructure is increasing. Nowadays, faulty cellular mobile network sites per day in ethio telecom are significant in number and have big impact to customers and operator in QoS, revenue and maintenance cost. Fault maintenance techniques commonly applied by ethio telecom is corrective maintenance approach. This only helps to recover services after interruption. However, it is important to implement the proactive maintenance approach to make mobile sites reliable and available. This helps to provide services according to standards and improve the quality of service delivery to customers. To mitigate mobile network site faults before happening, fault occurrence time prediction is an important technique for the implementation of proactive maintenance strategy. This Neural Network based 3G Mobile Fault Occurrence Prediction research work is conducted based on the Nonlinear Auto regressive (NAR) Neural Network time series prediction method using Addis Ababa 3G mobile sites in a case study. To train the neural network 15,950 actual fault occurrence time data are used. The algorithm used to train the neural network is Levenberg-Marquardt which is fast and efficient, and an iterative approach of hidden layer neuron number selection is applied. Finally, the best model is selected with minimum value of mean square error of prediction. Also, the model is tested with actual fault occurrence time which was not used in the training and achieved 90.71% in prediction. Therefore, it is efficient in prediction accuracy, fast and adaptive with future data.

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