Minimizing energy consumption and carbon emissions while maintaining system performance is a critical challenge in cloud task scheduling. This paper presents a multi-objective scheduling framework based on a Memetic Algorithm (MA) designed to optimize task-to-VM mapping with respect to energy efficiency, carbon footprint, and throughput. The algorithm employs a weighted fitness function that integrates actual and idle energy usage, simulated time-varying carbon intensity, and task throughput. To enhance solution quality, MA combines global evolutionary operations (selection, crossover, mutation) with local search heuristics that adaptively refine candidate solutions based on workload characteristics and green energy opportunities. The carbon emission model incorporates dynamic emission factors (γ) derived from location- and time-sensitive datasets, reflecting real-world variability in grid carbon intensity. The proposed method is evaluated using the NASA Ames iPSC/860 workload under both low and high resource utilization scenarios. Comparative results demonstrate that the proposed MA approach achieves reduces the carbon emission by 20.2%, minimizes energy consumption by 17.7%, and enhances throughput by 21.2% over conventional techniques such as HDDPGTS and RAPTS, while also ensuring competitive performance in terms of makespan and resource utilization. These improvements underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures. The findings highlight the importance of integrating eco-aware intelligence into task scheduling policies, particularly for mission-critical and energy-intensive cloud applications.

Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. In Cloud Computing (CC) environments, efficient Task Scheduling (TS) plays a critical role in minimizing operational expenses and enhancing system reliability. This paper presents a novel task scheduling approach that uses the Coati Optimization Algorithm (COA) to address two pivotal challenges: reducing the total cost (sum of computational cost and communication cost) and minimizing Virtual Machine (VM) failure rates. Inspired by the cooperative foraging and adaptive behavior of coatis in dynamic environments, the proposed algorithm leverages intelligent exploration and exploitation strategies to identify optimal task-to-VM mappings under fluctuating workloads. The COA incorporates cost-awareness and failure probability metrics into its fitness function to ensure robust scheduling decisions that align with budgetary constraints and fault tolerance requirements. To assess the performance of the proposed model, comprehensive simulations were conducted using the CEA-Curie real-world workload. The results were compared against three state-of-the-art approaches, MoHHOTS, RTATSA2C, and TS-GWO. Experimental evaluations demonstrate that COA significantly outperforms these existing methods by achieving a 19.8% reduction in overall cost and a 22.5% decrease in VM failure rate. These findings demonstrate that COA offer a promising pathway toward sustainable, cost-effective, and resilient task execution in large-scale cloud infrastructures, particularly under diverse and realistic workload scenarios.

Scheduling in cloud computing is an NP-hard problem, where traditional metaheuristic algorithms often fail to deliver approximate solutions within a feasible time frame. As cloud infrastructures become increasingly dynamic, efficient Task Scheduling (TS) remains a major challenge, especially when minimizing makespan, execution time, and resource utilization. To address this, we propose the Ant Colony Asynchronous Advantage Actor-Critic (A4C) algorithm, which synergistically combines the exploratory strengths of Ant Colony Optimization (ACO) with the adaptive learning capabilities of the Asynchronous Advantage Actor-Critic (A3C) model. While ACO efficiently explores task allocation paths, it is prone to getting trapped in local optima. The integration with A3C overcomes this limitation by leveraging deep reinforcement learning for real-time policy and value estimation, enabling adaptive and informed scheduling decisions. Extensive simulations show that the A4C algorithm improves throughput by 18.7%, reduces makespan by 16%, execution time by 14.60%, and response time by 21.4% compared to conventional approaches. These results validate the practical effectiveness of A4C in handling dynamic workloads, reducing computational overhead, and ensuring timely task completion. The proposed model not only enhances scheduling efficiency but also supports quality-driven service delivery in cloud environments, making it well-suited for managing complex and time-sensitive cloud applications.

This research presents a novel approach to evaluating student academic performance at Nalla Narasimha Reddy Group of Institutions (NNRG) by implementing a Student Training Based Optimization (STBO) algorithm. The proposed method draws inspiration from the structured training and adaptive learning behavior of students, simulating their progression through knowledge acquisition, skill refinement, and performance enhancement phases. The STBO algorithm is applied to optimize academic performance assessment by identifying key parameters such as attendance, internal assessments, learning pace, participation, and project outcomes. By modelling student development as a dynamic optimization process, the algorithm effectively predicts academic outcomes and recommends personalized strategies for improvement. Experimental evaluation on real academic datasets from NNRG CSE, CSE (Data Science), and CSE (AIML) Students demonstrates that the STBO algorithm achieves higher prediction accuracy and adaptive feedback generation when compared to traditional statistical and machine learning techniques. This approach also facilitates early identification of at-risk students and promotes data-driven decision-making for faculty and administration. Overall, the STBO-based framework not only enhances performance assessment but also contributes to academic excellence by aligning learning strategies with individual student needs.

Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient Task Scheduling (TS) in Cloud-Fog Computing (CFC) environments is crucial for meeting the diverse resource demands of modern applications. This paper introduces the Sewing Training-Based Optimization (STBO) algorithm, a novel approach to resource-aware task scheduling that effectively balances workloads across cloud and fog resources. STBO categorizes Virtual Machines (VMs) into low, medium, and high resource utilization queues based on their computational power and availability. By dynamically allocating tasks to these queues, STBO minimizes delays and ensures that tasks with stringent deadlines are executed in optimal environments, enhancing overall system performance. The algorithm leverages processing delays, task deadlines, and VM capabilities to assign tasks intelligently, reducing response times and improving resource utilization. Experimental results demonstrate that STBO outperforms existing scheduling algorithms in reducing makespan by 21.6%, improved energy usage by 31%, and maximizing throughput by 27.8%, making it well-suited for real-time, resource-intensive applications in CFC systems.