Summary Network architects and engineers face challenges in meeting the increasing complexity and low‐latency requirements of various services. To tackle these challenges, multi‐access edge computing (MEC) has emerged as a solution, bringing computation and storage resources closer to the network's edge. This proximity enables low‐latency data access, reduces network congestion, and improves quality of service. Effective resource allocation is crucial for leveraging MEC capabilities and overcoming limitations. However, traditional approaches lack intelligence and adaptability. This study explores the use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in MEC. DRL has gained significant attention due to its ability to adapt to changing network conditions and handle complex and dynamic environments more effectively than traditional methods. The study presents the results of applying DRL for efficient and dynamic resource allocation in MEC Computing, optimizing allocation decisions based on real‐time environment and user demands. By providing an overview of the current research on resource allocation in MEC using DRL, including components, algorithms, and the performance metrics of various DRL‐based schemes, this review article demonstrates the superiority of DRL‐based resource allocation schemes over traditional methods in diverse MEC conditions. The findings highlight the potential of DRL‐based approaches in addressing challenges associated with resource allocation in MEC.

In this paper, we propose Deep-RL-a marking decision with Deep Reinforcement Learning (DRL) via per-port for solving the erroneously marking problems in multi-queue multi service scenarios of Data Center Networks (DCNs). We formulate the statement as a DRL problem and use Deep Neural Network (DNN) to achieve the best possible policy for the agent. In this way we can model the complex DCNs in order to obtain the optimal threshold in the output port when the marked packets from queue buffers are not a concern. Unlike prior research that focused on mathematical models or used machine learning in DCN, Deep-RL is a novel DRL based method, which optimizes the real value of threshold with continues action space. Thus, this fact makes our work incomparable with previous research. To the best of our knowledge, we are the first to discuss the problem with DRL and DNN. Simulation results demonstrate that Deep-RL utilizes the buffer capacity at exactly 30% and achieves near optimal flow completion time.

Cloud computing provides more reliable and flexible access to IT resources, on-demand and self-service service request are some key advantages of it. Managing up-layer cloud services efficiently, while promising those advantages and SLA, motivates the challenge of provisioning and allocating resource on-demand in infrastructure layer, in response to dynamic workloads. Studies mostly have been focused on managing these demands in the physical layer and few in the application layer. This paper focuses on resource allocation method in application level that allocates an appropriate number of virtual machines to an application which requires a dynamic amount of resources. A Learning Automata based approach has been chosen to implement the method. Experimental results demonstrate that the proposed technique offers more cost effective resource provisioning approach while provisions enough resource for applications.

Fault localization is an important part in communication networks. Faults are unwanted and unavoidable in communication systems, and hence, their quick detection and localization is essential for sustaining the health of the network. This paper proposes an end-to-end approach that uses passive measurements for fault localization in communication networks. We formulate the fault localisation issue as an optimisation problem and then employ the genetic algorithm technique to solve it. Extensive simulation results show that although our algorithm needs to test only a small set of network components to localise all faults, it can infer the faulty nodes in at least 97% of cases. This simulation shows that the proposed algorithm, called genetic algorithm-based fault localisation is superior to the other approaches to localise all faults in a network.

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user’s applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.

A MANET is a set of mobile nodes connected by wireless link and are free to move dynamically and unpredictable in environment, but this dynamic nature of the network topology cause many challenges in MANET. Multicasting is an efficient way of providing necessary services for Ad hoc applications. Due to the dynamic nature of the network topology and restricted resources, finding and maintaining the routes for multicasting the data is still more challenging. Many Protocols have designed for multicasting in MANETs that On-Demand Multicast Routing Protocol is one of them. ODMRP is on-demand and mesh based protocol that uses forwarding group to establish a mesh for each multicasting group. In this paper, we discuss stable rout selection in ODMRP for forwarding data. In basic ODMRP route selection function uses minimum delay. But in proposed approach we consider nodes energy in route selection from source to destination. For presenting PDR improvement in proposed approach, we discuss group size and mobility speed in control overhead and end to end delay. Result of simulation illustrate that our approach can improve stability of route due to energy consumption.