IDEAL: Impact-Aware Virtual Data Center Embedding With Incremental Analysis for SDN-Controlled Networks

Rule-Aware VNE for SDN Resource Management

Optimizing Cloud Resource Management Through Energy-Efficient VM Placement Techniques

Cloud computing has seen rapid growth in recent years, leading to a surge in demand for data center services. To meet this demand, data centers deploy a large number of servers, resulting in substantial energy consumption. Virtual Machine Consolidation (VMC) is an effective strategy to reduce energy usage by shutting down underutilized servers while ensuring that Service Level Agreements (SLAs) are maintained. The VMC process comprises four key steps: detecting overloaded hosts, identifying underloaded hosts, selecting virtual machines (VMs), and determining their placement. This research presents the Energy-Efficient Virtual Machine Placement (EEVMP) approach, which aims to optimize resource utilization by selecting suitable destination hosts for migrating VMs based on utilization and resource skewness. The proposed method is evaluated using the CloudSim simulator. Experimental results show that EEVMP consistently outperforms existing placement strategies such as PABFD, IQRMC, PEBFD, ESVMP, and HVMAP in terms of energy efficiency and overall performance.

Data-Driven Online Slice Admission Control and Resource Allocation in NextG Mobile Networks

This work introduces Online Pricing-based Slice Admission Control and Resource Allocation (OPA) framework, which dynamically assigns pseudo-prices to resources that capture long-term scarcity and anticipated inter-temporal opportunity costs and designs an exponential pricing strategy that guarantees bounded worst-case performance.

Optimizing Next-generation Cloud and Data Center Networks: A Review of Routing, Resource Management, and Emerging Technologies

A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.

Enhancing Energy Efficiency in Cloud DataCenters through Deformable Graph ConvolutionalNetwork-Aware Virtual Machine Placement withHybrid Swarm Bipolar Walk-Spread Optimization

In big cloud data centers, the best physical machine (PM) is selected using the Virtual Machine Placement(VMP) process. To address this issue, a number of approaches have been proposed. Nevertheless, the existing solutionsonly take into account a small number of resource categories, which leads to an uneven load and ultimately, theactivation of superfluous physical computers within the data center. The aim of this research is to maximize resourceusage while lowering power use and carbon footprints by integrating a Hybrid Swarm Bipolar with Walk-SpreadAlgorithm with a unique Deformable Graph Convolutional Network (DGCN-SB-WSA)-aware virtual machine placement architecture. To ensGoogleent VM scheduling and management, real-world cloud workloads are analyzedusing the Google Cluster Dataset (GCD). The Deformable Graph Convolutional Network (DGCN) dynamically modelscloud infrastructure as a graph that captures intricate relationships among PMs and VMs, enabling adaptive placementchoices. The Hybrid Swarm Bipolar with Walk-Spread Algorithm (SB-WSA) then uses a dual-phase search approach tobalance local exploitation with global exploration, minimizing premature convergence and increasing performancewhile optimizing Virtual machine (VM) allocation. Comparing the proposed method to conventional VM placementtechniques, experimental results show that it dramatically 27 KW lowers power consumption, improves 98% resourceusage and 180 kg CO₂ decreases carbon emissions.

Split-Aware Function Placement with Availability Guarantees and Optical Provisioning in vRANs

The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators'profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a Genetic Algorithm (GA)-based metaheuristic that yields near-optimal solutions in real time. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5-10% lower normalized CPU usage than unshared counterparts.