DYNAMIC ADAPTATION APPROACH FOR DISTRIBUTED SYSTEM BEHAVIOR
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Date
2026
Authors
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Mohammed Amin TAHRAOUI / Ahmed HARBOUCHE
Abstract
As distributed systems (DS) evolve, managing dynamic workloads and resource availability
becomes essential for maintaining optimal performance. This thesis presents a novel Dynamic Adaptation (DA) framework designed to enhance the e”ciency and responsiveness
of DS, particularly in heterogeneous computing environments.
The central problem addressed in this work concerns how the performance of distributed
systems can be e!ectively enhanced through dynamic adaptation, particularly at the level of
a single-node multiprocessor system considered as a fine-grained unit within a distributed
architecture. Furthermore, what key strategies and algorithmic approaches are essential
to optimize task allocation, ensure load balancing, and minimize execution time in such
heterogeneous environments.
A key contribution of this work is the introduction of two innovative algorithms for
dynamic task scheduling. The first, DyTAg (Dynamic Task Allocation using Dynamic
Programming), focuses on optimizing task allocation in heterogeneous multiprocessor systems with independent tasks. It leverages dynamic programming to minimize makespan
and balance workloads e!ectively, laying the groundwork for more advanced scheduling
approaches.
Building on DyTAg, the thesis introduces the Knapsack-based Algorithm Co-Scheduling
Task Allocation (KaCoSTA), which integrates dynamic programming with knapsack optimization techniques to address task precedence and resource constraints. KaCoSTA dynamically adapts to system states, task priorities, and processing capabilities to maximize
resource utilization (RU) and minimize makespan. Extensive experiments demonstrate its
superiority over existing methods, such as Min-Min, Max-Min, and HEFT (Heterogeneous
Earliest Finish Time), in both static and dynamic scenarios.
Results reveal significant advancements in system adaptability, load balancing, and
overall e”ciency under fluctuating workloads. By combining foundational research with
practical innovation, this work provides a comprehensive solution for optimizing DS behavior in real-world applications.
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Submitted in fulfilment of the requirements for the degree of
DOCTORAT LMD
Keywords
Distributed systems, dynamic adaptation, optimization