Kazi Sakib

Work place: Institute of Information Technology, University of Dhaka

E-mail: sakib@iit.du.ac.bd


Research Interests: Software Creation and Management, Software Development Process, Software Engineering, Autonomic Computing


Kazi Sakib is a Professor at the Institute of Information Technology (IIT), University of Dhaka, Bangladesh. He received his Ph.D. in Computer Science at the School of Computer Science and Information Technology, RMIT University. His research interests include software engineering, cloud computing, software testing, software maintenance, etc. He is an author of a great deal of research studies published at national and international journals as well as conference proceedings.

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

By Kishan Kumar Ganguly Moumita Asad Kazi Sakib

DOI: https://doi.org/10.5815/ijitcs.2022.01.02, 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.

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An Environment Aware Learning-based Self-Adaptation Technique with Reusable Components

By Kishan Kumar Ganguly Md. Saeed Siddik Rayhanul Islam Kazi Sakib

DOI: https://doi.org/10.5815/ijmecs.2019.06.06, Pub. Date: 8 Jun. 2019

Self-adaptive systems appeared in order to reduce the effort of manual software maintenance. Apart from software attributes, for example, different alternative software modules, self-adaptation decisions depend on environmental attributes, for example, service rate, bandwidth etc. Current well-known self-adaptation approaches can be further improved by incorporating environmental attributes. Moreover, reducing maintenance effort includes minimizing both operational and development effort. To reduce the effort of developing self-adaptive software, the constituent components should be reusable. This paper proposes a technique to incorporate environmental attributes to learning-based self-adaptation and to increase the reuse potential of self-adaptive system components. The environmental attributes are provided as a constraint to an optimization problem which results in an optimal software attribute selection. Design patterns for self-adaptive system components are proposed to improve its reusability. The proposed technique was validated on a news serving website called Znn.com. According to renowned reusability metrics such as Lines of Code (LOC), Message Passing Coupling (MPC) and Lack of Cohesion of Methods 4 (LCOM4), the proposed technique improved reuse potential. The website was further tested for adaptation effectiveness under two scenarios – adaptation and without adaptation. According to our experiments, Adaptation gradually improved the main goal response time of the website where it performed poorly without adaptation.

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