Autonomous agents—systems that make independent decisions without human input—are foundational to modern robotics and enable intelligent responses in dynamic situations. In this work, a hybrid agent-based system that integrates software agents, or programs that represent users with multiple decision-making modules. The design integrates perception (gathering and interpreting sensory data), planning (scheduling a sequence of actions), and reinforcement learning, in which agents use feedback from their surroundings to improve their actions through try and error. Mathematical modelling and experimental evaluation reveal efficiency gains over rule-based systems that rely only on predefined instructions. Consequently, the framework ensures adaptability, scalability, and robustness in uncertain, unpredictable environments. Results show a 17% higher success rate and reduced execution time.
S. K, Sheetal Kusal, Usha Desai· 2026 6th International Confe...· 0 citations
The rapid expansion of cloud computing and large-scale data centers has significantly increased energy consumption and carbon emissions, creating critical sustainability concerns for modern computing infrastructures. This paper proposes the Adaptive Carbon-Aware Virtualized Energy-efficient Scheduling (ACAVES) framework to improve resource utilization and reduce environmental impact in cloud environments. The framework combines workload monitoring, task classification, virtual machine consolidation, carbon-aware scheduling, and energy optimization within an integrated architecture. An adaptive scheduling mechanism allocates workloads according to utilization patterns, energy requirements, and carbon emission estimates. Experimental evaluation was performed using heterogeneous workloads containing 10,000 tasks executed over 50 physical servers and 200 virtual machines. Results demonstrate that the proposed ACAVES framework reduced energy consumption from 520 kWh to 385 kWh and carbon emissions from 310 kgCO2 to 215 kgCO2. Additionally, server utilization improved from 68% to 87%, while average task completion time decreased from 820 ms to 670 ms, confirming the effectiveness and scalability of the proposed sustainable scheduling framework.
S. K, Kishore Bitra, Usha Desai· 2026 International Conferenc...· 0 citations