Computer Systems Lab
Universitas Indonesia

Propelling the next era of breakthroughs in Computer Systems and Networks, from Big Data and High-Performance Computing,
to Real-Time System Infrastructures, through advanced algorithms, protocols, and frameworks that drive real-world innovation.

Big Data and AI Infrastructures

Dr. Ari Wibisono led the big data and AI infrastructures research project that is dedicated to designing and building scalable, high-performance systems that support a wide range of data-intensive applications. The focus lies in advancing real-time data processing, intelligent inference, and efficient data management to handle the increasing volume, variety, and velocity of modern datasets. By developing flexible and robust infrastructures, this research enables seamless integration of AI technologies and fosters advanced analytics and knowledge discovery across complex and dynamic environments. The project aims to provide a strong foundation for emerging applications in both scientific and practical domains.

  1. Wibisono, A., & Rahmadika, R. N. (2025). Comparative Evaluation of Database Systems for High-Volume Seismic Prediction Data Management in Real-Time Applications. Jurnal Ilmu Komputer dan Informasi, 18(2), 251-259.
  2. Mursanto, P., Wibisono, A., Fahira, P. K., Rahmadhani, Z. P., & Wisesa, H. A. (2023). In-TFK: a scalable traditional food knowledge platform, a new traditional food dataset, platform, and multiprocess inference service. Journal of Big Data, 10(1), 47.

Blockchain Research and Applications

Dr. Setiadi Yazid led the blockchain research and applications project that focuses on advancing the scalability, efficiency, and adaptability of blockchain technologies through both foundational and applied research. Key areas of exploration include architectural enhancements, consensus mechanisms, and seamless integration with emerging digital infrastructures. By addressing core challenges such as scalability and interoperability, the project aims to support the development of robust, real-world blockchain solutions across various domains—including finance, supply chains, data integrity, and decentralized systems. It provides a forward-looking platform for continued innovation in the rapidly evolving blockchain ecosystem.

  1. Purnaadi, C. W., & Yazid, S. (2024). Sidechain implementation strategies to improve blockchain scalability. In AIP Conference Proceedings (Vol. 2920, No. 1, p. 020005). AIP Publishing LLC.

Computer Systems and Network Protocols

Dr. Made Harta Dwijaksara led the computer systems and network Protocols research project that focuses on advancing the performance, reliability, and adaptability of modern communication networks through innovative protocol design and intelligent system optimization. Key areas of investigation include congestion control strategies, transport protocol enhancements, autonomous system-level internet topology mapping, and efficient resource management in dynamic, bandwidth-constrained environments. By leveraging emerging technologies such as deep reinforcement learning and next-generation transport protocols, the project addresses critical challenges in network scalability, stability, and quality of service. It provides a robust platform for continued research and development in the evolving landscape of computer networks and distributed systems.

  1. Dwijaksara, M. H., Hilman, M. H., Lee, C., & Lee, W. (2024). Rate Adaptation Technique for Media Streaming Over QUIC With Limited Backhaul. IEEE Access.
  2. Witono, T., Yazid, S., & Sucahyo, Y. G. (2024). The Art of Internet Mapping: A Comprehensive Guide to Regional Internet Topology Mapping at the Autonomous System Level. Journal of Information Systems Engineering and Business Intelligence, 10(2), 191-205.
  3. Naqvi, H. A., Hilman, M. H., & Anggorojati, B. (2023). Implementability improvement of deep reinforcement learning based congestion control in cellular network. Computer Networks, 233, 109874.
  4. Naqvi, H. A., Hilman, M. H., & Anggorojati, B. (2023). Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control. Jurnal Ilmu Komputer dan Informasi, 16(2), 151-161.

High Performance Computing

Prof. Heru Suhartanto led the high-performance computing (HPC) research project that focuses on developing scalable, efficient, and accessible computing solutions to address complex computational challenges across diverse domains. Key areas of exploration include containerized environments for parallel programming, as well as the application of HPC techniques to large-scale optimization problems, such as disaster response and resource planning. The project emphasizes both the advancement of HPC infrastructure and its real-world applications, aiming to support scientific discovery, high-demand simulations, and data-intensive processing. It serves as a foundation for continued innovation in the rapidly evolving field of computational science.

  1. Arisal, A., & Suhartanto, H. (2024). HPC-in-Containers: A Containerized Parallel Environment for Parallel Programming Learning Using Docker. In 2024 International Conference on Information Technology Systems and Innovation (ICITSI) (pp. 101-105). IEEE.
  2. Tarhan, İ., Zografos, K. G., Sutanto, J., Kheiri, A., & Suhartanto, H. (2023). A multi-objective rolling horizon personnel routing and scheduling approach for natural disasters. Transportation research part C: emerging technologies, 149, 104029.

Multi-tenant Container Orchestration

Dr. Muhammad Hilman led the multi-tenant container orchestration research project that aims to develop intelligent, scalable, and adaptive strategies for managing containerized applications in shared, cloud-native environments. Central to this work are techniques for dynamic resource allocation, status-aware scaling, and efficient orchestration of microservices to ensure performance, reliability, and cost-efficiency across multiple tenants. By tackling challenges such as workload variability, elasticity, and system heterogeneity, the project lays the groundwork for next-generation container orchestration platforms that support seamless, resilient, and autonomous operations at scale.

  1. Wen, L., Xu, M., Gill, S. S., Hilman, M. H., Srirama, S. N., Ye, K., & Xu, C. (2025). StatuScale: Status-aware and elastic scaling strategy for microservice pplications. ACM Transactions on Autonomous and Adaptive Systems, 20(1), 1-25.

Real-Time Streaming Infrastructure

Dr. Muhammad Hilman led the real-time streaming infrastructure research project that is dedicated to developing low-latency, high-reliability systems for dynamic and distributed environments. The project focuses on enabling real-time data transmission, adaptive streaming, and edge-assisted processing to support emerging applications such as remote sensing, autonomous systems, and UAV-assisted operations. By addressing key challenges—including mobility, signal variability, and responsiveness—the research aims to create robust, flexible streaming frameworks capable of operating efficiently in complex and rapidly changing conditions. This work lays the foundation for future innovations in real-time, data-driven technologies.

  1. Bakhuraisa, Y., Lim, H. S., Chan, Y. K., & Hilman, M. H. (2025). UAV-Assisted Localization of Ground Nodes in Urban Environments Using Path Loss Measurements. Drones, 9(6), 450.