Moumita Asad

Work place: Institute of Information Technology, University of Dhaka



Research Interests: Software Construction, Software Engineering, Computer Architecture and Organization, Data Structures and Algorithms, Mathematical Software


Moumita Asad received the Bachelor of Science in Software Engineering (BSSE) and Master of Science in Software Engineering (MSSE) degrees from the Institute of Information Technology, University of Dhaka. She is currently working as a lecturer in Independent University, Bangladesh. Her research interest includes automated program repair, software testing and metrics.

Author Articles
Decentralized Self-adaptation in the Presence of Partial Knowledge with Reduced Coordination Overhead

By Kishan Kumar Ganguly Moumita Asad Kazi Sakib

DOI:, Pub. Date: 8 Feb. 2022

Decentralized self-adaptive systems consist of multiple control loops that adapt some local and system-level global goals of each locally managed system or component in a decentralized setting. As each component works together in a decentralized environment, a control loop cannot take adaptation decisions independently. Therefore, all the control loops need to exchange their adaptation decisions to infer a global knowledge about the system. Decentralized self-adaptation approaches in the literature uses the global knowledge to take decisions that optimize both local and global goals. However, coordinating in such an unbounded manner impairs scalability. This paper proposes a decentralized self-adaptation technique using reinforcement learning that incorporates partial knowledge in order to reduce coordination overhead. The Q-learning algorithm based on Interaction Driven Markov Games is utilized to take adaptation decisions as it enables coordination only when it is beneficial. Rather than using unbounded number of peers, the adaptation control loop coordinates with a single peer control loop. The proposed approach was evaluated on a service-based Tele Assistance System. It was compared to random, independent and multiagent learners that assume global knowledge. It was observed that, in all cases, the proposed approach conformed to both local and global goals while maintaining comparatively lower coordination overhead.

[...] Read more.
Other Articles