The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

Cloud computing offers a scalable and cost-effective platform by providing on-demand access to shared computational resources. Efficient load balancing is essential to maintain optimal performance and maximize resource utilization, ensuring an even distribution of network traffic across servers, preventing overload, enhancing response times, and improving system reliability. This paper proposes a Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing (Meta-RHDC), a novel approach for dynamic load balancing in cloud environments. The Meta-RHDC model leverages convolutional and recurrent neural networks to predict virtual machine loads and dynamically classify them into overloaded and underloaded categories. By integrating reinforcement learning with advanced optimization techniques, Meta-RHDC significantly improves task scheduling and load balancing compared to existing methods such as Load Optimization Algorithm (LOA), Reinforcement Learning (RL), and Falcon Optimization Algorithm (FOA). Extensive experiments conducted on the CloudSim platform demonstrate that Meta-RHDC achieves substantial improvements in key performance metrics. For task counts, makespan is reduced by 19.51%, energy consumption by 22.75%, and balanced CPU utilization by 21.98%. Resource utilization increases by 32.52%, scalability efficiency improves by 49.03%, and the failure rate decreases by 19.72%. For virtual machine (VM) counts, makespan improves by 30.57%, energy consumption by 42.59%, and balanced CPU utilization by 36.85%. Resource utilization rises by 31.75%, scalability efficiency increases by 36.61%, and the failure rate drops by 38.19%. These results confirm the robustness and efficiency of Meta-RHDC in optimizing execution, resource management, and scalability across diverse workloads, making it a superior solution for dynamic load balancing in cloud computing.

Cloud computing has transformed IT service delivery by offering scalable, on-demand resources essential for managing diverse and complex workloads. However, efficient task scheduling remains a critical challenge due to the NP-hard nature of mapping workflows to virtualized resources, impacting performance, resource utilization, energy efficiency, and reliability. This highlights the urgent need for advanced scheduling strategies.This paper presents ADWEH (A Dynamic Prioritized Workflow Task Scheduling approach based on the Enhanced Harris Hawk Optimization), a novel framework designed to overcome these challenges. ADWEH combines deep reinforcement learning with dynamic prioritization strategies to assign task priorities based on dependencies and runtime requirements. This integrated approach ensures optimal resource allocation, enhances scheduling efficiency, and improves adaptability to varying workloads.The proposed approach has been implemented and thoroughly tested using WorkflowSim, a simulation toolkit for modeling and evaluating workflow scheduling strategies. ADWEH’s performance is benchmarked against state-of-the-art algorithms, including DQN, A2C, and PWSA3C, using scientific workflows including Montage, Cybershake, SIPHT, LIGO. These Four workflows are tested on datasets like HPC2N, NASA, Uniform, and Random, ensuring robust evaluation under diverse scenarios. Simulation results demonstrate that ADWEH significantly outperforms DQN, A2C, and PWSA3C approaches, achieving up to 31.9% shorter makespan, a 24% reduced in energy consumption, a 29% Enhancement in scalability efficiency, and a 52% increase in resource utilization, while reducing failure rates by over 47%. The results position ADWEH as a robust, efficient solution for improving the performance , reliability of cloud-based workflows, addressing the evolving demands of modern cloud environments.

The Cloud computing environments provide scalable resources for complex workflows. Because of the dynamic nature of these environments, resource heterogeneity and varying dependencies of tasks in each workflow differ from one another. Consequently, workflow scheduling with several dependencies on an inappropriate virtual machine, such as one with low processing capacity, can negatively impact the execution, resulting in delays, increased costs, higher energy consumption. To essentially schedule workflows that have numerous task dependencies, we propose a new Prioritized Workflow Scheduling with Deep Reinforcement Learning in Cloud Computing Based on the Deep-Q Network Model (PWDCDQ). To optimize workflow execution, we use a Deep Reinforcement Learning approach, balancing performance with cost optimization by analysing workflows based on their internal dependencies and VMs based on their electricity costs. However, based on both the priorities of tasks, VMs, we employ a DQN model for dynamically scheduling tasks. Essentially, we prioritize tasks based on their dependencies and VMs based on their electricity costs. The priorities are used by the DQN scheduler to assign workflows to the most appropriate VMs, ensuring efficient workflow execution while maintaining low costs. Extensive simulations were conducted on WorkflowSim, examining Real-time scientific workflows, including LIGO, Epigenomics, Montage and CyberShake, are essential tools for conducting research in real-time scientific workflows. The proposed PWDCDQ algorithm was compared to existing approaches such as HEFT, BAT, ACO, and Cuckoo Search (CS) algorithm. The results revealed that PWDCDQ surpasses these approaches in terms of reduced costs, energy consumption.