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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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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.
Parallel Processing in Distributed and Hybrid Cloud-Fog Architectures: A Systematic Review of Scalability and Efficiency Strategies Ihsan, Rasheed; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): 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.v14i1.4661

Abstract

In distributed computing, hybrid cloud-fog architectures have become a revolutionary concept for tackling the pressing issues of latency, scalability, and energy efficiency. These systems allow real-time data processing closer to end users by fusing the localized capabilities of fog computing with the centralized capacity of cloud computing. This makes them especially useful for latency-sensitive applications like smart cities, healthcare, and the Internet of Things. The technological developments, application areas, and difficulties related to hybrid systems are all examined in this study's methodical analysis of the body of existing research. With a focus on utilizing technologies like SDN, NFV, and AI-driven optimization frameworks, key focus areas include resource management, dynamic job allocation, privacy-preserving procedures, and scaling tactics. Although hybrid designs show great promise for increasing system responsiveness and efficiency, unresolved problems including resource allocation complexity, privacy concerns, and interoperability underscore the need for more study. This work offers actionable recommendations to address these gaps, including standardization of communication protocols, integration of advanced AI techniques, and the development of energy-efficient designs. The findings lay a strong foundation for advancing hybrid cloud-fog systems and ensuring their broader adoption across diverse industries.
Distributed Transactions in Cloud Computing: A Review Reliability and Consistency Ferzo, Barwar; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.3830

Abstract

The challenges of managing distributed transactions in cloud computing are discussed in this paper. The paper places an emphasis on the critical balance that must be maintained between reliability and consistency in the face of complexities such as hardware failures, network outages, and varying latencies. It sheds light on the delicate balance that must be maintained in order to guarantee that transactions in cloud environments are both reliable and consistent. Cloud environments are prone to hardware glitches and network disruptions. In addition, the paper delves into novel approaches with the objective of cultivating a computing ecosystem that is both resilient and dependable in the face of the ever-changing requirements of cloud computing, also a comparison table is presented for all the literature reviewed.
Bridging the Gap: Integrating Organizational Change Management with IT Project Delivery Zangana, Hewa Majeed; Ali, Natheer Yaseen; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4450

Abstract

In today's rapidly evolving technological landscape, the successful implementation of IT projects is increasingly contingent upon effective organizational change management (OCM). This research paper explores the intersection of OCM and IT project delivery, proposing a comprehensive framework that integrates these two critical domains. Through a review of existing literature and analysis of case studies, we identify key challenges and best practices for synchronizing OCM strategies with IT project management processes. Our findings reveal that the alignment of OCM with IT project delivery not only enhances project success rates but also promotes sustainable organizational transformation. This integrated approach ensures that technological advancements are supported by a well-prepared workforce, thereby minimizing resistance and maximizing adoption. The paper concludes with practical recommendations for practitioners aiming to bridge the gap between OCM and IT project delivery, ultimately fostering a more agile and resilient organizational environment.
Systematic Review of Decentralized and Collaborative Computing Models in Cloud Architectures for Distributed Edge Computing Zangana, Hewa Majeed; Mohammed, Ayaz khalid; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4169

Abstract

This systematic review paper delves into the evolving landscape of cloud architectures for distributed edge computing, with a particular focus on decentralized and collaborative computing models. The aim of this systematic review is to synthesize recent advancements in decentralization techniques, collaborative scheduling, federated learning, and blockchain integration for edge computing. As edge computing becomes increasingly vital for supporting the Internet of Things (IoT) and other distributed systems, innovative strategies are needed to address challenges related to latency, resource management, and data security.The key findings highlight the benefits of latency-aware task management, autonomous serverless frameworks, and the collaborative sharing of computational resources. Additionally, the integration of federated learning and blockchain technologies offers promising solutions for enhancing data privacy and resource allocation. The versatility of edge computing is showcased through its applications in diverse domains, including healthcare and smart cities. Future research directions emphasize the need for optimized resource management, improved security protocols, standardization efforts, and application-specific innovations. By providing a comprehensive review of these developments, this paper underscores the critical role of decentralized and collaborative models in advancing the capabilities and efficiency of edge computing systems.
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