Resource Management Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Resource Management ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Resource Management Projects
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Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks
This project focuses on improving how wireless devices share the same communication channel without causing interference. It uses artificial intelligence to make sure all devices get a fair chance to send data. The system learns automatically to balance speed and fairness. This makes communication faster, more reliable, and efficient even when many users share the same signal space. -
A Hierarchical Namespace Approach for Multi-Tenancy in Distributed Clouds
This project develops a cloud system that works close to users, at the edge of the network. It creates virtual clouds on physical machines and shares resources like CPU, memory, and storage efficiently. Each virtual cloud stays separate, keeping data and operations isolated. Users can also switch between different virtual clouds safely, while the system manages security automatically. -
An Adaptive Threshold-Based Modified Artificial Bee Colony Optimization Technique for Virtual Machine Placement in Cloud Datacenters
This project focuses on making cloud computing more energy-efficient. It introduces a new method to place virtual machines on physical servers in a way that reduces energy use. The approach uses a smart optimization technique to find underused servers and decide the best way to allocate resources. Simulations show it performs better than existing methods with high accuracy and precision. -
Dynamic Sizing of Cloud-Native Telco Data Centers With Digital Twin and Reinforcement Learning
This project focuses on making telecom edge data centers more efficient. It predicts how much computing power is needed at different times of the day. Then it adjusts the number of active servers to save energy and reduce costs. The system uses smart algorithms and machine learning to make these adjustments quickly and reliably. -
Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture
This project studies how to efficiently assign computing tasks across different types of devices, from sensors to data centers and quantum computers. The researchers test quantum algorithms to solve this complex scheduling problem. They find that one algorithm, VQE, works better when carefully set up. Real quantum hardware experiments show it can handle larger problems faster than classical computers. -
Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing
This project focuses on improving how tasks are assigned in cloud computing systems. The researchers created a new algorithm that schedules tasks faster and uses resources more efficiently. It avoids common problems of existing methods, such as getting stuck on poor solutions or taking too long to run. Tests show it works better than previous algorithms, even as the number of tasks grows. -
Artificial Intelligence-Defined Wireless Networking for Computational Offloading and Resource Allocation in Edge Computing Networks
This project focuses on improving how data and computing resources are managed in next-generation 5G networks. It brings computing closer to mobile users so applications run faster and more reliably. The researchers developed an AI-based system that decides how to share resources and offload tasks in real time. Their approach can serve more users efficiently, even in busy and fast-changing network conditions. -
Attention Mechanism-Aided Deep Reinforcement Learning for Dynamic Edge Caching
This project focuses on improving mobile networks by predicting what content users will need and storing it on nearby devices before it is requested. It designs a smart system that decides what to cache and when, using a type of artificial intelligence. The goal is to reduce network traffic and make better use of limited storage and network resources. The proposed method was tested and shown to work effectively in improving speed and efficiency. -
Bee System-Based Self Configurable Optimized Resource Allocation Technique in Device-to- Device (D2D) Communication Networks
This project focuses on improving mobile network performance as traffic keeps growing. It uses a method inspired by how bees search for food to efficiently allocate resources between nearby devices. If the network is overloaded, extra resources and relay nodes are added to maintain good service. The approach improves user experience by reducing delay, saving energy, and supporting mobility. -
Multi-Objective Deep Reinforcement Learning for Efficient Workload Orchestration in Extreme Edge Computing
This project focuses on managing computing tasks on small, limited-capacity devices at the edge of a network. It introduces a learning-based system called DEWOrch that decides how to use these devices efficiently. The system reduces energy use and resource waste while completing more tasks successfully. It works well even when large servers are not available. -
A QoS Improving Downlink Scheduling Scheme for Slicing in 5G Radio Access Network (RAN)
This project focuses on improving 5G networks. It looks at how to share radio resources fairly among different services. The method ensures each service meets its quality targets. Tests show it works better than existing approaches in efficiency and reliability. -
An Energy-Efficient Deep Mutual Learning System Based on D2D-U Communications
This project focuses on helping mobile devices learn from each other without sharing private data. The system uses direct device-to-device communication over unlicensed spectrum. It finds the best way to pair devices and allocate communication resources to save energy. The results show that this method improves learning between devices. -
Multi-Antenna Coded Caching for Location-Dependent Content Delivery
This project focuses on improving virtual reality experiences over wireless networks. It uses the extra memory in users’ devices to store parts of content, reducing the load on the network. The system decides how much content each user should store based on their location and connection quality. This approach makes content delivery faster and more reliable for everyone.
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