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All Journal The Indonesian Journal of Computer Science
Subhi R. M. Zeebaree
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : The Indonesian Journal of Computer Science

 1 
Unveiling the Synergistic Relationship between Distributed Systems and Cloud Computing: A Review of Architectural Trends Salih, Sardar; Subhi R. M. Zeebaree
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.v13i2.3801

Abstract

Cloud providers use distributed systems for scalability, availability, performance, automation, multi-tenancy, and innovation. Distributed cloud computing distributes workload across multiple locations, improving application performance and responsiveness. Significantly potential computational resources are developed in cloud, where large-scale, intricate tasks are performed with the backbone of distribute infrastructure in cloud systems, similar to supercomputing. Cloud computing development has significantly impacted software development and testing, necessitating applications compatible with the cloud, large data users, and high security. Distributed applications hoist on to cloud platforms where increased efficiency, reliability and low costs are favored and further be stored in the cloud for flexibility and scalability. Cloud service models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), each offering different application services, programming languages, and hosting environments. The synergistic aspects of Distributed Systems and Cloud Systems with respect to their basic capabilities are discussed and systematically reviewed.
Optimizing Performance in Distributed Cloud Architectures: A Review of Optimization Techniques and Tools Jajan, Khalid Ibrahim Khalaf; Subhi R. M. Zeebaree
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.v13i2.3805

Abstract

This research paper presents a groundbreaking hybrid transactional/analytical processing (HTAP) architecture designed to revolutionize real-time point cloud data processing, particularly in autonomous driving environments. Integrating elements from both columnar and row-based tables within a spatial database, the proposed architecture offers unparalleled efficiency in managing and updating point cloud data in real-time. The architecture's distributed nature operates through a seamless synergy of Edge and Cloud components. The Edge segment operates within the Robot Operating System (ROS) environment of the vehicle, while the Cloud counterpart functions within the PostgreSQL environment of cloud services. The communication between these components is facilitated by Kafka, ensuring rapid and reliable data transmission. A pivotal aspect of the proposed system lies in its ability to autonomously detect changes in point cloud data over time. This is achieved through a sophisticated algorithm that analyzes dissimilarities in the data, triggering real-time updates in areas where high dissimilarity is detected. The system ensures the maintenance of the latest state of point cloud data, contributing significantly to the generation of safe and optimized routes for autonomous vehicles. In terms of optimization, the paper demonstrates how the HTAP architecture achieves real-time online analytical processing through query parallelization in a distributed database cluster. The system's efficacy is evaluated through simulations conducted in the CloudSim framework, showcasing its scalability, adaptability, and robustness in handling point cloud data processing for a single vehicle. While acknowledging the achievement of the proposed architecture, certain limitations are recognized. The study highlights the need for further investigation into the system's performance under simultaneous analysis and updates from multiple vehicles. Additionally, ensuring seamless scalability and robustness for uninterrupted operation and expansion during runtime is identified as an area requiring further development.
Distributed Systems for Data-Intensive Computing in Cloud Environments: A Review of Big Data Analytics and Data Management Zeravan Arif; Subhi R. M. Zeebaree
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.v13i2.3819

Abstract

Because of the increasing increase of data, which is frequently referred to as "big data," many different businesses have been severely impacted in recent years, necessitating the implementation of sophisticated data management and analytics solutions. By virtue of the fact that it provides scalable resources for applications that are data-intensive, cloud computing has emerged as an indispensable platform for the management of these enormous databases. The evolving landscape of distributed systems in cloud settings is the primary emphasis of this study, which is situated within the framework of big data analytics and data management. With the purpose of providing a comprehensive overview of distributed systems that are used in cloud settings for data-intensive computing, the review article seeks to offer. Furthermore, it evaluates the many ideas, techniques, and technical improvements that have been established in order to properly manage, store, and analyse large amounts of data. A comprehensive literature evaluation of recently published scientific references was successfully completed by our team. The analysis takes into account the theoretical foundations, as well as the research that has already been conducted on distributed computing systems, cloud-based data management, and enormous data analytics. The study places an emphasis on the significant role that distributed computing plays in ensuring the success of big data analytics. The interplay between distributed systems and cloud computing paradigms has resulted in the development of solutions that are robust, scalable, and economical for activities that need a significant amount of data. It is still a huge problem to ensure that data security, privacy, and interoperability are maintained across the many cloud services.
Co-Authors Jajan, Khalid Ibrahim Khalaf Salih, Sardar Zeravan Arif