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Comparison of Three Resouces Allocation Technique in Cloud Computing Sekwatlakwatla, Sello Prince
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.118

The shift to the cloud enables organizations of all sizes to swiftly, efficiently, and innovatively move their operations. The adoption of cloud computing has significantly transformed most organizations' work, communication, and collaboration methods, making it a crucial necessity for maintaining competitiveness in the digital age. Organizations are implementing cloud bursting to handle IT demand peaks by utilizing private cloud capacity and public cloud capacity, freeing up local resources for critical applications, and reverting data back to the private cloud. Organizations face challenges in allocating resources in cloud computing to automatically switch from private cloud to public cloud, leading to system issues, user frustration, operational failure, increased stress, and revenue loss. To address these concerns. This paper investigates traffic predictions by comparing three prediction tools, such as support vector machines, spatio-temporal, and edge-cloud collaborative schemes, and proposing conceptual solutions. Efficient cloud computing traffic management can prevent system bottlenecks, especially during peak periods, potentially leading to dissatisfied clients.

Bibliometric Analysis of Mixed Text Using Transformer-Based Architecture in Africa Sekwatlakwatla, Sello Prince; Malele, Vusumuzi; Ramalepe, Phetole Simon; Modipa, Thipe
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.131

Deep learning techniques based on neural networks have been developed for text creation, a critical sub-task of natural language generation that aims to create human-readable content. Natural language processing (NLP) tasks are utilized to recognize speech in code-mixed comments on social media platforms like Facebook and Twitter, which enable users to interact and exchange ideas, views, status updates, pictures, and videos with people all over the world. Although NLP is widely investigated in the world and Africa is home to approximately 3,000 languages, many of which are derived from significant language families, in this regard, there are challenges that Africa faces in Natural Language Processing (NLP), especially mixed text using transformer-based architecture. The purpose of this study is to investigate the prevalence of mixed text using transformer-based architecture in Africa. Bibliometric analysis was used to assess natural language and mixed text in Africa, utilizing transformer-based architecture. show that sentiment analysis is the holistic tool that is used for mixed text using transformers, where social media, deep learning, codes, computational linguistics, and social networking are critical tools in generating human-like quality text. Therefore, this study proposes artificial intelligence, artificial neutral networks, and neural networks, as well as a prediction to estimate the technique or fluctuation as the method for mixed text using transformer-based architecture in Africa. This research sets the path for future studies that use mixed text using transformer-based architecture in Africa

A Literature Review to Investigate Data Analytics Tools for The Allocation of Resources in Cloud Computing Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.136

To ensure efficient operations and cost-effectiveness, resource management in cloud computing entails managing cloud resources to satisfy application needs, financial restrictions, and security. In this regard, utilizing data analytics tools for the allocation of resources in cloud computing to efficiently predict, track, allocate, and monitor resources enables businesses to make informed decisions based on real-time data, which plays a crucial role in resource allocation. Organizations adopting cloud computing services face increased network traffic, limiting traffic routing flexibility and causing excess traffic to reach unprepared physical nodes due to an inability to adjust to real-time traffic changes. This paper uses a systematic literature review to investigate the data analytics techniques used for resource allocation in cloud computing. It uses data from 2019 to 2024, sourced from different research databases. The results show that the majority of data analytics tools, including ARIMA and SVM, are employed for resource allocation in cloud computing. This study offers guidance to organizations regarding data analytics tools for the allocation of resources in cloud computing, and the recommendations can be utilized for the enhancement of the results in cloud computing, as well as to scholars by suggesting techniques to further investigate resource allocation to address the current gaps in cloud computing

Data Analytics Techniques for Addressing Cloud Computing Resources Allocation Challenges: A Bibliometric Analysis Approach Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.640

The increase in the use of digital technology led to an increase in online activities. In this regard, many organizations adopted cloud computing systems to manage this online traffic. It is plan of every cloud computing resource provider to manage their system effectively and efficiently. This paper uses bibliometric analysis technique to look at the prevalence of utilization of data analytics techniques in addressing cloud computing resource allocation challenges. In this regard, the following research databases the Association for Computing Machinery, the Institute of Electrical and Electronics Engineering, Web of Science and Scopus databases, were consulted. The research articles published before the beginning of 2017 to 2023 were considered as part of the analysis. The results showed that the prevalent data analytics techniques used to address the cloud computing resources allocation challenge are Support Vector Machine, Spatio-temporal and edge-cloud collaborative scheme. Failure to effectively and efficiently provide cloud computing management resource allocation will lead to system bottlenecks especially during peak periods. In this regard, such a failure could lead to dissatisfied clients.

Model for Enhancing Cloud Computing Resource Allocation Management Using Data Analytics Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.679

The cloud computing environment requires an adequate and accurate traffic prediction tool to fulfill the needs of customers and support organizations effectively. In the absence of an effective tool for forecasting cloud computing traffic, many organizations might fail. It is difficult to predict the network resources that are suitable to meet the needs of all network clients at a given time in a cloud computing environment because of the inconsistent network traffic flow. There is still room for improving the predictive accuracy of the model in cloud computing. The higher the accuracy of the traffic flow, the better the allocation of resources. Therefore, this study proposes an ensemble method called SGLA (Stepwise Gaussian Linear Autoregressive) by combining linear regression, support vector machines, Gaussian process regression, and the autoregressive integrated moving average technique. SGLA performed better than all methods with a minimum MAPE of 1.03% of the ensemble approach by using the averaging strategy, SGLA shows a clear advantage in handling resource allocation better despite traffic fluctuations, with 91.7% traffic prediction accuracy. Overall experimental results indicate that this method performed better than single models in terms of prediction accuracy. The main contribution of this study is to propose a data analytics model for enhancing cloud computing resource management.

Bibliometric Analysis of Data Analytics Techniques in Cloud Computing Resources Allocation Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.782

Cloud computing provides on-demand computing services over the Internet, allowing for quicker innovation, more flexible resources, and economies of scale while reducing the need for physical data centers and servers. With this benefit, most organizations are adopting this technology, and some organizations are also operating fully on cloud computing. This causes traffic to increase, and most of these organizations are struggling with resource allocation, resulting in complaints from users regarding inactive system performance, timeouts in applications, and higher bandwidth use during peak hours. In this regard, this study investigates data analytics techniques and tools for the allocation of resources in cloud computing. The study indexed journal articles from the Scopus Database and Web of Science (WOS) between 2010 and 2024. This article brings new insights into the analysis of data analytics techniques in Africa and collaborations with other developing countries. The findings present tools and approaches that may be used to allocate cloud computing resources and give recommendations.

Evaluations of Large Language Models a Bibliometric analysis sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3767

The development of artificial intelligence (AI) and the increased curiosity about how large language models (LLMs) may maximize an organization's opportunities and the ethical implications of LLMs, such as the ability to generate human-like text, give rise to concerns regarding disinformation and fake news. As a result, it is crucial to develop evaluation benchmarks that take into account the social and ethical implications involved. The great challenges of LLMs lack awareness of their own limitations, yet they persist in producing responses to the best of their capabilities. This often results in seemingly plausible but ultimately incorrect answers, posing challenges to the implementation of reliable generative AI in industry. This paper aims to delve into the evaluation metrics of machine-learning models' performance, specifically focusing on LLM. Therefore, bibliometric analysis utilized to explore and analyze various techniques and methods used in evaluating large language models. Additionally, it sheds light on the specific areas of focus when evaluating these models. The results show that natural language processing systems, classification of information, and computational linguistics are some of the techniques used to evaluate large language models. This work paves the way for future investigations employing extensive language models.

Data science for energy applications: A Bibliometric Analysis Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3781

Global digitalization is altering the energy sector, demanding the adoption of data science applications to improve efficiency and innovation, despite the industry's existing data analytics. Data science is revolutionizing the energy and utilities industries, enabling efficient, sustainable, and innovative decision-making through data analysis and smart grid optimization. In the energy industry, organizations are turning to data science to reduce waste, optimize energy usage, and provide alternative energy sources. With the different parts of Africa facing energy crises, different applications are needed to provide a solution. Data science has the potential to provide good information and knowledge that could be used to contribute to energy solutions. To address these concerns, data science models enable utilities to accurately forecast energy demand, enabling efficient generation, distribution, waste reduction, and informed investment decisions by leveraging historical consumption data, weather patterns, and economic indicators. This article aims to explore data science for energy applications. The findings show tools and techniques that can be utilized to provide energy efficiency and energy sustainability through data science applications.

Mapping Trends in Air Quality Research in South Africa: A Bibliometric Analysis, 1998-2024 Sekwatlakwatla, Sello Prince; Malele, Vusi; Toona, Priscilla; Tshilongo, James; Mkhohlakali, Andile; Letsoalo, Refiloe; Mabowa, Happy; Ntsasa, Napo
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1025

The foundation of South Africa is the Constitution, which guarantees every citizen access to a safe and healthy environment. Despite a wealth of research on lower-income households, the effects of burning wood for cooking, heating, and comfort in South African homes are also affecting the air quality; even if the government is working very hard to put measures in place to improve air quality, it will be very difficult to accommodate every household in South Africa. South Africa's low-income urban settlements focus on air quality monitoring for policy formulation and strategy building and Lack of garbage removal services and systems is another characteristic of low-income communities that exacerbates ambient air pollution levels. Based on the quantity of South African publications and citations in air quality that are listed in the Scopus and Web of Science databases, the study used bibliometric analysis to look at the country's air quality and the factors that affect it. Data was collected from 1998 to 2024; the results show that air pollution, nitrogen dioxide and emissions are causing a risk to children, and also having a high impact in causing diseases like asthma, respiratory health and climate change is playing a critical role in increasing the risk. Moreover, the word cloud reflects a growing emphasis on certain air pollutants, including NO₂, PM2.5, black carbon, and SO₂. NO₂ has been linked to substantial health implications, including respiratory disorders, asthma aggravation, and cardiovascular issues.

Performance of Cloud Computing Resources Allocations SGLA Model Compared to ARIMA Sekwatlakwatla, Sello Prince; Malele, Vusumuzi
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4205

Solutions for cloud computing are growing in popularity as a means for businesses to streamline operations, save expenses and increase productivity. The benefit of cloud services is that they let customers access on-demand apps and services from a shared pool of programmable computer resources and store data offsite. Cloud computing resource allocation requires sophisticated tools and methodologies for optimal utilization. These problems include load balancing, efficient resource management and compliance with legal and regulatory requirements. The majority of businesses are switching to cloud services and advising their customers to use internet services. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the performance ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA), and the individual method which is autoregressive integrated moving average (ARIMA). The Matlab tool is used for simulation and evaluation of the results. The results show SGLA prediction accuracy increased to an average of 98.9%, and ARIMA prediction showed an accuracy of 75.5%. In this regard, the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing.

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