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All Journal Sistemasi: Jurnal Sistem Informasi International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Indonesian Journal of Education and Social Science Jurnal Ilmiah Ilmu Terapan Universitas Jambi The Indonesian Journal of Computer Science
Zeebaree, Subhi R. M.
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : The Indonesian Journal of Computer Science

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Enhancing AdaBoost Performance: Comparative Analysis of CPU Parallel Processing on Breast Cancer Classification Ashqi Saeed, Vaman; Zeebaree, Subhi R. M.
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.v13i2.3793

Abstract

The implementation of time-sharing across processes in a real-time way has the potential to increase the execution efficiency of multiprocessor systems like the one described above. The system is able to carry out tasks that make use of a large number of processors in an effective way as a result of this. The aim of this research is to design a system with two primary goals: to enhance accuracy and to minimise the amount of time necessary with processing. This will be accomplished by integrating the ADABoost model with the decision tree algorithm. Furthermore, the statistics unambiguously demonstrate that the accuracy remains the same regardless of whether or not the central processing unit (CPU) makes use of parallel processing, which suggests that there is no variation in parallelization. As a consequence of this, there is a direct connection between the amount of time that is spent and an increase in the amount of parallel processing that is carried out by the central processing unit pertaining to the breast cancer dataset that is being investigated. This research was carried out using Python, which was the programming language that was used for the coding technique that was carried out during the course of its execution.
Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges Fadhil, Jawaher; Zeebaree, Subhi R. M.
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.v13i2.3794

Abstract

The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.
Parallel Processing Impact on Random Forest Classifier Performance: A CIFAR-10 Dataset Study Sadiq, Bareen Haval; Zeebaree, Subhi R. M.
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.v13i2.3803

Abstract

Using the CIFAR-10 dataset, this research investigates how parallel processing affects the Random Forest method's machine learning performance. Accuracy and training time are highlighted in the study as critical performance indicators. Two cases were studied, one with and one without parallel processing. The results show the strong prediction powers of the Random Forest algorithm, which continues to analyze data in parallel while retaining a high accuracy of 97.50%. In addition, training times are notably shortened by parallelization, going from 0.6187 to 0.4753 seconds. The noted increase in time efficiency highlights the importance of parallelization in carrying out activities simultaneously, which enhances the training process's computational efficiency. These results provide important new information about how to optimize machine learning algorithms using parallel processing approaches.
Embracing Distributed Systems for Efficient Cloud Resource Management: A Review of Techniques and Methodologies Abdi, Abdo; Zeebaree, Subhi R. M.
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.v13i2.3806

Abstract

The development of parallel computing, distributed computing, and grid computing has introduced a new computing model, combining elements of grid, public computing, and SaaS. Cloud computing, a key component of this model, assigns computing to distributed computers rather than local computers or remote servers. Research papers from 2017 to 2023 provide an overview of the advancements and challenges in cloud computing and distributed systems, focusing on resource management and the integration of advanced technologies like machine learning, AI-centric strategies, and fuzzy meta-heuristics. These studies aim to improve operational efficiency, scalability, and adaptability in cloud environments, focusing on energy efficiency and cost reduction. However, these advancements also present challenges, such as implementation complexity, adaptability in diverse environments, and the rapid pace of technological advancements. These issues necessitate practical, efficient, and forward-thinking solutions in real-world settings. The research conducted between 2017 and 2023 highlights the dynamic and rapidly evolving field of cloud computing and distributed systems, providing valuable guidance for ongoing and future research. This body of work serves as a crucial reference point for advancing the field and emphasizing the need for practical, efficient, and forward-thinking solutions in the ever-evolving landscape of cloud computing and distributed systems.
Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics Atrushi, Diler; Zeebaree, Subhi R. M.
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.v13i2.3810

Abstract

The rapid growth of graph data in various domains has propelled the need for efficient distributed graph processing techniques in cloud computing environments. This paper presents a comprehensive review of distributed graph processing for graph analytics of massive size in the context of cloud computing. The paper begins by highlighting the challenges associated with distributed graph processing, including load balancing, communication overhead, scalability, and partitioning strategies. It provides an overview of existing frameworks and tools specifically designed for distributed graph processing in cloud environments. Furthermore, the review encompasses various techniques and algorithms employed in distributed graph processing. The paper also reviews recent research advancements in optimizing distributed graph processing in cloud computing. To provide practical insights, the paper presents a comparative analysis of representative large-scale graph analytics applications implemented on different cloud computing platforms. Performance, scalability, and efficiency metrics are evaluated under varying workload sizes and graph characteristics. Overall, this comprehensive review paper serves as a highly prized asset for researchers and large-scale graph analytics professionals who are practitioners in the field. It provides a holistic understanding of the state-of-the-art distributed graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud environments.
The Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybrid and Federated Cloud Environment Merseedi, Karwan Jameel; Zeebaree, Subhi R. M.
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.v13i2.3811

Abstract

The concept of several clouds has greatly extended the use of cloud computing and gained popularity in academic and business circles. The use of multi-cloud techniques has increased as businesses use cloud computing more and more to meet their computational demands. A thorough analysis of cloud architectures intended for distributed multi-cloud computing is presented in this study, with an emphasis on federated and hybrid cloud systems. The study looks at the opportunities and difficulties of adopting and overseeing a variety of cloud resources from several providers. The review starts out by going over the basic ideas and reasons for using multi-cloud strategies, emphasizing how important flexibility, scalability, and resilience are in contemporary computing settings. The study then explores the nuances of hybrid cloud architectures, with a focus on how private and public cloud resources can be seamlessly combined. In the context of hybrid cloud installations, important factors including data sovereignty, security, and workload orchestration are covered. In addition, the research delves into federated cloud architectures, clarifying how enterprises can coordinate and oversee workloads across several cloud providers. An examination of resource identification, policy enforcement, and interoperability procedures sheds light on the intricacies of federated cloud computing. The review delves into new developments in standards, best practices, and technology that help multi-cloud ecosystems mature. The study analyses the state of research and industry practices now, pointing out gaps and possible directions for future development. The intention is to provide decision-makers, researchers, and practitioners with a comprehensive grasp of the changing cloud architectural scene so they can plan and execute distributed multi-cloud solutions with knowledge. In conclusion, this article provides a thorough overview of hybrid and federated cloud architectures by combining information from many sources. Through a comprehensive analysis of the difficulties and possibilities associated with multi-cloud computing, the study hopes to add to the current conversation on cloud environment design and optimization in the rapidly changing technological landscape.
Distributed Architectures for Big Data Analytics in Cloud Computing: A Review of Data-Intensive Computing Paradigm Al-Atroshi, Chiai; Zeebaree, Subhi R. M.
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.v13i2.3812

Abstract

Big Data challenges are prevalent in various fields, including economics, business, public administration, national security, and scientific research. While it offers opportunities for productivity and scientific breakthroughs, it also presents challenges in data capture, storage, analysis, and visualization. This paper provides a comprehensive overview of Big Data applications, opportunities, challenges, and current techniques and technologies to address these issues. This study presents a system for managing big data resources using cloud for the development of data-intensive applications. It addresses even the challenges related to technologies that combine cloud computing with other allied technologies and devices. In addition, the increasing volume, velocity, and variety of data in the era of Big Data necessitate advanced methods for data processing and management. This study delves into the intricacies of data scalability, real-time processing, and the integration of diverse data types. Furthermore, it explores the role of machine learning algorithms and artificial intelligence in extracting meaningful insights from massive datasets.
Harnessing the Power of Distributed Systems for Scalable Cloud Computing A Review of Advances and Challenges Taher, Hanan; Zeebaree, Subhi R. M.
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.v13i2.3815

Abstract

In the realm of cloud computing, the literature defines scalability as the inherent ability of a system, application, or infrastructure to adapt and accommodate varying workloads or demands efficiently. It encompasses the system's capability to handle increased or decreased usage with compromising performance, responsiveness, or stability. In this paper, a comprehensive review is presented regarding the scalability in the cloud computing network. In addition, the research community define the scalability as a dynamic attribute, emphasizing its ability to facilitate both horizontal and vertical scaling. Horizontal scalability involves adding or removing instances or nodes to distribute workloads across multiple resources, while vertical scalability focuses on enhancing the capacity of existing resources within a single entity. They established a global frameworks to evaluate scalability, often emphasizing response time, throughput, resource utilization, and cost-efficiency as critical metrics. These metrics serve as benchmarks to assess the system's ability to scale effectively without compromising performance or incurring unnecessary costs [1]. The literature underscores scalability's interconnectedness with elasticity, highlighting the need for on-demand resource provisioning and de-provisioning to maintain an agile and adaptable infrastructure. Overall, in academic papers, cloud scalability is portrayed as a fundamental attribute crucial for modern computing infrastructures, enabling systems to flexibly and efficiently adapt to dynamic computing needs.
Distributed Systems for Real-Time Computing in Cloud Environment: A Review of Low-Latency and Time Sensitive Applications Abd Alnabe, Nisreen; Zeebaree, Subhi R. M.
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.v13i2.3821

Abstract

As a result of its many benefits, including cost-efficiency, speed, effectiveness, greater performance, and increased security, cloud computing has seen a boom in popularity in recent years. This trend has attracted both consumers and businesses. Being able to process and provide data or services in a quick and effective manner while adhering to low latency and time limits is the hallmark of an efficient distributed system that is designed particularly for real-time computing in cloud environments. It is essential to place a high priority on low latency and time sensitivity while developing and putting into action a distributed system for real-time computing in a cloud environment. In order to fulfil the particular requirements of the application or service, consideration must be given to a number of different aspects. In particular, the topic of load balancing will be discussed in this paper. It is possible to ensure a more effective distribution of workload and reduce latency by using load balancers, which distribute incoming traffic over many servers or instances. The throttled algorithm is believed to be the most efficient load balancing strategy for reducing service delivery delay in cloud computing. This research investigates a hybrid method known as Equally Spread Current Execution (ESCE), which is known for its combination with the throttled algorithm.
Distributed Resource Management in Cloud Computing: A Review of Allocation, Scheduling, and Provisioning Techniques Ali, Nabeel N.; Zeebaree, Subhi R. M.
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.v13i2.3823

Abstract

This review paper provides an in-depth examination of distributed resource management in cloud computing, focusing on the critical elements of allocation, scheduling, and provisioning. Cloud computing, characterized by its dynamic and scalable nature, necessitates efficient resource management techniques to optimize performance, cost, and service. The study encompasses a comprehensive analysis of various strategies in resource allocation, scheduling methodologies, and provisioning techniques within the cloud computing paradigm. Through comparative analysis, this paper aims to highlight the synergies and trade-offs inherent in these methods, offering a holistic view of distributed resource management. It contributes to the field by bridging the gap in existing literature, presenting a critical, comparative analysis of current strategies and their interplay in distributed cloud environments.
Co-Authors Abd Alnabe, Nisreen Abdi, Abdo Abdulrahman, Saman Mohammed Ahmad, Hawar Bahzad Al-Atroshi, Chiai Ali, Nabeel N. Ali, Natheer Yaseen Almufti, Saman M Asaad, Renas Rajab Ashqi Saeed, Vaman Atrushi, Diler Fadhil, Jawaher Ferzo, Barwar Hani, Ahmed Alaa Ihsan, Rasheed Majeed, Dilovan Asaad Merseedi, Karwan Jameel Mohammed, Ayaz Khalid Sadiq, Bareen Haval Sallow, Amira Bibo Taher, Hanan Zangana, Hewa Majeed