scholarly journals Intelligent Performance Prediction: The Use Case of a Hadoop Cluster

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2690
Author(s):  
Dimitris Uzunidis ◽  
Panagiotis Karkazis ◽  
Chara Roussou ◽  
Charalampos Patrikakis ◽  
Helen C. Leligou
Keyword(s):  
Service Providers ◽  
Single Layer ◽  
Machine Learning Algorithms ◽  
Service Characteristics ◽  
Potential System ◽  
Break Points ◽  
Network Functions ◽  
Hadoop Cluster ◽  
Service Life Cycle

The optimum utilization of infrastructural resources is a highly desired yet cumbersome task for service providers to achieve. This is because the optimal amount of such resources is a function of various parameters, such as the desired/agreed quality of service (QoS), the service characteristics/profile, workload and service life-cycle. The advent of frameworks that foresee the dynamic establishment and placement of service and network functions further contributes to a decrease in the effectiveness of traditional resource allocation methods. In this work, we address this problem by developing a mechanism which first performs service profiling and then a prediction of the resources that would lead to the desired QoS for each newly deployed service. The main elements of our approach are as follows: a) the collection of data from all three layers of the deployed infrastructure (hardware, virtual and service), instead of a single layer of the deployed infrastructure, to provide a clearer picture on the potential system break points, b) the study of well-known container based implementations following that microservice paradigm and c) the use of a data analysis routine that employs a set of machine learning algorithms and performs accurate predictions of the required resources for any future service requests. We investigate the performance of the proposed framework using our open-source implementation to examine the case of a Hadoop cluster. The results show that running a small number of tests is adequate to assess the main system break points and at the same time to attain accurate resource predictions for any future request.

scholarly journals Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

2020 ◽  
Vol 17 (24) ◽  
pp. 9347 ◽  
Author(s):  
Md Mamunur Rashid ◽  
Joarder Kamruzzaman ◽  
Mohammad Mehedi Hassan ◽  
Tasadduq Imam ◽  
Steven Gordon
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Service Providers ◽  
Detection System ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Increased Risk ◽  
Iot Devices

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

scholarly journals Service Characteristics and Service Quality of Organizations within the Telecommunications Sector; A Case of Safaricom Public Limited in Nakuru County

2019 ◽  
Vol 3 (V) ◽  
pp. 57-75
Author(s):  
Caroline Magembe ◽  
Reuben Njuguna
Keyword(s):  
Quality Of Service ◽  
Service Quality ◽  
Service Providers ◽  
Multiple Regression Model ◽  
Mobile Service ◽  
Service Characteristics ◽  
Quality Aspects ◽  
Limited Company ◽  
Quality Threshold

Safaricom Public Limited Company faces diverse challenges regarding service quality aspects. The communication authority of Kenya ensures that the quality of service amongst the mobile service providers in Kenya is in compliance with Kenya Information and Communication Act of 1998. While the other telecommunication players like Airtel and Telkom Kenya improved in their service quality, Safaricom failed to register any service quality improvement. Safaricom further failed to meet the minimum service quality on eight of the ten regions that were checked by Communication Authority of Kenya. Safaricom has consistently performed poorly and below the minimum set quality threshold in relation to service quality for the four years preceding 2016 financial years in its performance. This study therefore sought to examine the influence of service characteristics on service quality of Safaricom Public Limited Company in Nakuru County. The study was guided by the following specific objectives: to examine the role of service intangibility, service inseparability, service perishability and service variability on the service quality of Safaricom public limited company in Nakuru County. This study adopted expectancy theory and servqual methods in meeting its objectives. This study used descriptive research design to guide the study in meeting its objectives. The study targeted the customers who enter into Safaricom Public Limited Company shop in Nakuru County in any particular day. A sample size of 95 customers was used. This study used structured questionnaires to obtain data from respondents of the study. This study used subject matter experts who comprises of the research supervisor and the four managers from the Safaricom Public Limited Company. Cronbach’s Alpha test of internal consistency was used to test the reliability of the questionnaire using the data obtained from the pilot study carried out using 10 respondents from Airtel Kenya. The filled questionnaires were checked for completeness and then coded and entered into Statistical Package for Social Sciences (SPSS) for analysis. Both descriptive and inferential statistics were used in the analysis of data. The entire analysis was presented in form of tables. The study revealed that the multiple regression model used in this study was statistically significant in predicting the level of service quality at Safaricom Public Limited in Nakuru County. In respect to this, it was found that quality of service at Safaricom Public Limited in Nakuru County could be significantly be predicted using service variability, service perishability, service intangibility, and service inseparability as predictor variables. It was also revealed that 77.6% of the variability in service quality at Safaricom Public Limited in Nakuru County is due to changes that occur in service variability, service perishability, service intangibility, and service inseparability. The model was found to be accurate in its prediction due to a small standard error of the estimate of 0.11327. The study findings and recommendations are of great importance to Safaricom Public Limited Company in understanding what needs to be addressed in order to improve the quality of service they offer to their customers. This study will also benefit customers of Safaricom Public Limited Company in receiving quality services as a result of readdressing the previous methods of service delivery. Future researchers and academicians stand to benefit from this study as it lays the foundation on which their studies was based.

scholarly journals Augmenting Speech Quality Estimation in Software-Defined Networking Using Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3477
Author(s):  
Jan Rozhon ◽  
Filip Rezac ◽  
Jakub Jalowiczor ◽  
Ladislav Behan
Keyword(s):  
Network Performance ◽  
Service Providers ◽  
Software Defined Networking ◽  
Speech Quality ◽  
Machine Learning Algorithms ◽  
Link Quality ◽  
Multimedia Communications ◽  
Speech Communication ◽  
Sdn Controller

With the increased number of Software-Defined Networking (SDN) installations, the data centers of large service providers are becoming more and more agile in terms of network performance efficiency and flexibility. While SDN is an active and obvious trend in a modern data center design, the implications and possibilities it carries for effective and efficient network management are not yet fully explored and utilized. With most of the modern Internet traffic consisting of multimedia services and media-rich content sharing, the quality of multimedia communications is at the center of attention of many companies and research groups. Since SDN-enabled switches have an inherent feature of monitoring the flow statistics in terms of packets and bytes transmitted/lost, these devices can be utilized to monitor the essential statistics of the multimedia communications, allowing the provider to act in case of network failing to deliver the required service quality. The internal packet processing in the SDN switch enables the SDN controller to fetch the statistical information of the particular packet flow using the PacketIn and Multipart messages. This information, if preprocessed properly, can be used to estimate higher layer interpretation of the link quality and thus allowing to relate the provided quality of service (QoS) to the quality of user experience (QoE). This article discusses the experimental setup that can be used to estimate the quality of speech communication based on the information provided by the SDN controller. To achieve higher accuracy of the result, latency characteristics are added based on the exploiting of the dummy packet injection into the packet stream and/or RTCP packet analysis. The results of the experiment show that this innovative approach calculates the statistics of each individual RTP stream, and thus, we obtain a method for dynamic measurement of speech quality, where when quality decreases, it is possible to respond quickly by changing routing at the network level for each individual call. To improve the quality of call measurements, a Convolutional Neural Network (CNN) was also implemented. This model is based on two standard approaches to measuring the speech quality: PESQ and E-model. However, unlike PESQ/POLQA, the CNN-based model can take delay into account, and unlike the E-model, the resulting accuracy is much higher.

scholarly journals Prediction of Future Terrorist Activities Using Deep Neural Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
M. Irfan Uddin ◽  
Nazir Zada ◽  
Furqan Aziz ◽  
Yousaf Saeed ◽  
Asim Zeb ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Single Layer ◽  
Machine Learning Algorithms ◽  
Suitable Model ◽  
Law And Order ◽  
The People

One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

scholarly journals Predictors of Client Satisfaction Among Recent Users of Health Services in Lagos, Nigeria

2020 ◽  
Vol 13 ◽  
pp. 117863292093449
Author(s):  
Modupe Rebekah Akinyinka ◽  
Esther Oluwakemi Oluwole ◽  
Olumuyiwa Omotola Odusanya
Keyword(s):  
Health Care ◽  
Odds Ratio ◽  
Client Satisfaction ◽  
Service Providers ◽  
Community Members ◽  
Cross Sectional ◽  
Service Characteristics ◽  
Health Service Providers ◽  
Satisfaction Rates

Client satisfaction is an important measure of quality of care as it provides information on how well health service providers meet clients’ values and expectations. The study was cross-sectional and analytical in nature. Data were obtained with the use of an interviewer-administered questionnaire. Respondents (n = 994) were a subset of a larger group of community members recruited for a study on quality of health care who had used a health facility for care within 3 months prior to data collection. A total of 94% of clients were satisfied with services received although client satisfaction rates were higher with private than public health facilities. Waiting time of less than 20 minutes (adjusted odds ratio [AOR] = 9.35, 95% confidence interval [CI] = 2.08-41.67), cheap cost of all services received (AOR = 7.58, 95% CI = 1.95-29.41), and the ability of the health care provider to offer explanations clearly to clients (AOR = 6.21, 95% CI = 1.90-20.41) were predictors of client satisfaction. However, the use of a government-owned hospital (AOR = 0.23, 95% CI = 0.08-0.63) was predictive of client dissatisfaction. Only service characteristics were predictive of client satisfaction. Improvement in service delivery is recommended.

scholarly journals The Classification of Customers’ Sentiment using Data Mining Approaches

2019 ◽  
Vol IV (IV) ◽  
pp. 146-156
Author(s):  
Dost Muhammad Khan ◽  
Tariq Aziz Rao ◽  
Faisal Shahzad
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Supervised Machine Learning ◽  
Market Trends ◽  
Using Data

Data mining is a procedure of extracting the requisite information from unprocessed records by using certain methodologies and techniques. Data having sentiments of customers is of utmost importance for managers and decision-makers who intend to monitor the progress, to maintain the quality of their products or services and to observe the latest market trends for business support. Billions of customers are using micro-blogging websites and social media for sharing their opinions about different topics on daily basis. Therefore, it has become a source of acquiring information but to identify a particular feature of a product is still an issue as the information retrieves from varied sources. We proposed a framework for data acquisition, preprocessing, feature extraction and used three supervised machine-learning algorithms for classification of customers’ sentiments. The proposed framework also tested to evaluate the system’s performance. Our proposed methodology will be helpful for researchers, service providers, and decisionmakers.

A GAP ANALYSIS IN SERVICE QUALITY OF THAI LOGISTICS SERVICE PROVIDERS

2013 ◽  
Author(s):  
Chatwadee Tansakul ◽  
◽  
Jirachai Buddhakulsomsiri ◽  
Thananya Wasusri ◽  
Papusson Chaiwat ◽  
...  
Keyword(s):  
Service Quality ◽  
Gap Analysis ◽  
Service Providers ◽  
Logistics Service ◽  
Logistics Service Providers

Проблемы оценки качества при проектировании транспортных услуг

2020 ◽  
pp. 30-40
Author(s):  
S.U. Lyapina ◽  
◽  
V.N. Tarasova ◽  
V.B. Ruchkin ◽  
E.O. Koscheeva ◽  
...  
Keyword(s):  
Life Cycle ◽  
Business Processes ◽  
Service Industries ◽  
Service Organizations ◽  
Quality Of Services ◽  
Market Failures ◽  
Expert Opinions ◽  
Service Life Cycle ◽  
Quantitative Indicators

The quality issues of new services directly affect the competitiveness of service organizations. However, the introduction of new services to the market is often limited only to the construction of the logistics of business processes, and the design applies only to technological equipment and infrastructure, the quality of which ultimately does not always ensure the quality of the services provided. At the same time, quality management affects mainly operational aspects, that is, it covers the later stages of the service life cycle. In resource-intensive service industries (for example, transport, communications, etc.), the high cost of equipment and infrastructure reduces the possibility of changes in service delivery technologies to improve their quality, which leads to inefficiency and market failures due to the fact that the new service does not match the real needs of customers. Despite this, forecasting and planning the quality of a service at the stage «making a decision» to launch a new service on the market remains largely without sufficient attention. The authors prove the need to design the quality of services at the stage «making a decision» to bring new services to the market. The purpose of the article is to describe the approach developed by the authors to assessing the quality of projected services at the early stages of their life cycle, which makes it possible to integrate qualitative and quantitative indicators of the future service and take into account the forecast requests of customers. The proposed approach has two features: (1) forecasting customer requirements for the quality of services is based on the results of machine learning based on data on existing and potential customers, as well as on the basis of the accumulated knowledge base of customer experience and expert opinions; (2) multi-criteria optimization is used, while some of the optimized parameters are discrete and high-quality. In conclusion, the authors demonstrated the advantages of the developed model on the examples of transport and logistics business in the field of passenger and freight transportation in Russia.

PUBLIC SECTOR SERVICE INNOVATION COMPARISON

2020 ◽  
Vol 4 (2) ◽  
pp. 826-844
Author(s):  
Ellyza Octaleny
Keyword(s):  
Public Sector ◽  
Service Providers ◽  
Employee Performance ◽  
Work Load ◽  
Employee Welfare ◽  
Low Education ◽  
And Function ◽  
Public Sector Service ◽  
Sector Service

Abstrak Inovasi merupakan suatu hal penting yang harus dimiliki oleh sebuah organisasi pemberi layanan sektor publik. Instansi pemerintah sebagai pemberi layanan dituntut memiliki inovasi untuk meningkatkan kualitas pelayanan kepada masyarakat. Penelitian ini bertujuan untuk membandingkan inovasi pelayanan sektor publik di RSUD Prof. Margono dan Lembaga Permasyarakatan Nusakambangan Cilacap. Metode penelitian yang digunakan adalah metode deskriptif kualitatif. Teknik pengumpulan data melalui wawancara, observasi, dan dokumentasi. Temuan dalam penelitian ini adalah: 1). Kurangnya personil pegawai yang sesuai dengan tugas dan fungsinya; 2). Kuangnya kesejahteraan pegawai sehingga kinerja pegawai rendah dan tidak berkualitas; 3). Pegawai berpendidikan rendah sehingga tidak sesuai dengan beban tugas dan fungsinya. Rekomendasi untuk kedua Lembaga sector public tersebut dalam penelitian ini adalah: 1). Penambahan personel pegawai sesuai dengan tugas dan fungsinya sehingga cakupan kewenangannya luas; 2) Lebih memperhatikan kesejahteraan pegawai sehingga pegawai termotivasi untuk bekerja dengan baik dan berkualitas; 3) Memberikan Kemudahan kepada pegawai yang ingin melanjutkan pendidikannya kejenjang yang lebih tinggi sehingga kualitas Pendidikan personel lebih seimbang dengan beban kerja. Kata Kunci: Inovasi, Pelayanan, SektorPublik   Abstract Innovation is an important thing that must be owned by an organization that provides public sector services. Government agencies as service providers are required to have innovations to improve the quality of services to the community. This study aims to compare public sector service innovations in hospitals. Prof. Margono and the Nusa kambangan Penitentiary. The research method used is descriptive qualitative method. Data collection techniques through interviews, observation, and documentation. The findings in this study are: 1). Lack of employee personnel in accordance with their duties and functions; 2). The lack of employee welfare so that employee performance is low and not qualified; 3). Employees have low education so that it is not suitable with their work load and function. The recommendations for the two public sector institutions in this study are: 1). The addition of employee personnel in accordance with their duties and functions so that the scope of their authority is broad; 2) Pay more attention to employee welfare so that employees are motivated to work well and quality; 3) Providing convenience to employees who want to continue their education to a higher level so that the quality of personnel education is more balanced with the workload. Keywords: Innovation, Service, Public Sector  

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