Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 689-694· 0 citations· 26 references
Abstract
Network function virtualization (NFV) decouples network functions from dedicated hardware by implementing them as software-based virtual network functions (VNFs), thereby enabling flexible network resource management. Network services are provisioned through service function chains (SFCs) composed of multiple VNFs. Given SFC requests and physical machines (PMs), this paper investigates the VNF deployment problem with three objectives: maximizing the service acceptance ratio (SAR), minimizing the total energy consumption (TEC), and balancing the load among PMs. We then propose a load-aware and energyefficient VNF deployment (LEEV) scheme that dynamically adapts its deployment strategy based on the system load. Under lightload conditions, LEEV deploys adjacent VNFs of a given SFC on one PM to reduce energy consumption, while under heavy-load conditions, it can shift toward load-balancing deployment without sacrificing energy efficiency. Simulation results reveal that LEEV achieves a higher SAR, lower TEC, and improved load balance.
Space-air-ground integrated networks (SAGINs) offer seamless three-dimensional coverage and strengthened flexibility, which are recognized as a core network architecture of 6G. Software-defined networking (SDN) and network function virtualization (NFV) are two enabling technologies for SAGINs that can be utilized to sequentially arrange virtual network functions (VNFs) into service function chains (SFCs) to provide users with resource-efficient and delay-optimized multi-source multicast request (MMR) services. However, SAGINs exhibit significant dynamism and heterogeneity, it brings great challenges when dynamically deploying the MMR’s source nodes and SFCs for fulfilling MMR routing. This paper investigates the multi-source multicast SFC embedding problem (MMSEP) considering the determination of the source nodes for MMR, VNFs placement, as well as network resources and delay constraints in the SDN/NFV-enabled SAGIN. Firstly, we define and formulate the MMSEP and demonstrate its NP-hardness. Subsequently, we employ a heuristic algorithm to assign the optimal source nodes for all multicast destination nodes and utilize the markov decision process (MDP) to simulate dynamic transitions in network states. Finally, we propose a deep deterministic policy gradient with attention mechanism (DDPG-AM) to address the MMSEP, aiming to minimize resource consumption costs and delays while maximizing the revenue of the internet service provider. The simulation results demonstrate that the proposed algorithm surpasses the state-of-the-art DDPG algorithm by approximately 27% in network utility, 17% in latency reduction, and 5% in acceptance ratio.
Liang Liu, Yejun He, Yujie Zhang et al.· IEEE Transactions on Network...· 0 citations
The rapid growth of Internet of Things (IoT) deployments has intensified the need for efficient, decentralized computation management at the network edge. This paper presents a lightweight, neighbor-aware one-hop task offloading framework designed for resource-constrained IoT networks. The proposed adaptive scheme combines Exponential Weighted Moving Average (EWMA) load estimation with a queue-depth gate to prevent unnecessary offloading under transient load spikes, and an assignment-pressure mechanism to distribute tasks more evenly across neighboring nodes. We evaluate the framework using a custom-developed discrete-event simulator on a 90-node ringplus-chord topology with heterogeneous hotspot and light nodes, comparing against three baselines: local-only execution, random offloading, and least-loaded neighbor selection. Results show that a load-aware but pressure-unaware least-loaded strategy surprisingly produces the highest load variance (377.25), worse than random offloading (114.84), due to severe task funneling toward persistently fast nodes. The proposed scheme eliminates task drops entirely, achieves an average latency of 148.7 ms, and reduces task-count variance to 44.33 - an $8.5 \times$ improvement over the least-loaded baseline and 4.6× over local-only execution - while requiring only 28.21% of tasks to be offloaded. These results demonstrate that assignment-pressure tracking is essential for fair load distribution in energy-limited IoT deployments.
Faizan Haider, Alexandre dos Santos Roque, E. P. de Freitas· International Conference on...· 0 citations
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.
Mayank Ramnani, Shasank Dixit, Sushil K. Yadav et al.· 0 citations
Virtual Data Center Embedding (VDCE) maps Virtual Data Center Requests (VDCRs), comprising Virtual Machines (VMs) and Virtual Links (VLs), onto a Physical Network (PN). The problem is $\mathbb {NP}$ -hard because it requires joint allocation of computing and communication resources. In real Data Centers (DCs), VM placement is sequential and state-dependent: each accepted placement changes host utilization, energy use, and load distribution, thereby affecting subsequent mapping decisions. Existing VDCE schemes often rely on static resource snapshots or single-objective heuristics and therefore do not adequately assess the placement-wise impact on the evolving PN. This may lead to inefficient resource utilization and increased energy overhead. To address this limitation, this paper proposes Impact-Aware Virtual Data Center Embedding with Incremental Analysis for SDN-controlled Networks (IDEAL). IDEAL estimates the incremental system impact of each tentative VM placement using two criteria: marginal energy increment and incremental load-balance distortion. These criteria quantify the additional energy consumption and the change in residual-resource distribution introduced by a tentative placement. Host selection is performed using a lightweight decision-support process that combines Analytic Hierarchy Process (AHP) and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR). On the evaluated 54-host spine-leaf topology, comparison with heuristic baselines shows that IDEAL reduces active-host energy consumption by 48.70%, decreases active hosts by 47.61%, and improves load-balancing efficiency by 75.72% on average. Total PN energy reduction is smaller because idle hosts retain baseline power consumption. The comparison is limited to heuristic VDCE baselines, without learning-based baseline methods.
N. Preetham, S. K. Addya, I. A. K. S. Senior Member et al.· IEEE Access· 0 citations
As Data Center Networks (DCNs) continue to scale, the limitations of traditional centralized Software-Defined Networking (SDN) architectures become increasingly apparent, as they fail to meet the stringent demands for low latency and quality of service (QoS). In this paper, we propose an adaptive traffic-aware load balancing mechanism (ATL), a telemetrydriven in-switch scheme implemented on the programmable data plane (PDP) using P4 and driven by In-band Network Telemetry (INT). The current traffic regime is inferred by analyzing the remaining capacity (RC) of each link and its short-term variation (VAR), and adopts a dual-optimization strategy: (i) separating elephant flows (large flows) and mice flows (small flows) onto disjoint path sets to mitigate head-of-line blocking and packet reordering; (ii) dynamically adjusting the flowlet threshold $\left(F^{*}\right)$ to strike a balance between maximizing parallelism and ensuring in-order delivery. We prototyped and evaluated ATL in a Mininet/BMv2 environment, targeting bandwidth-constrained scenarios representative of IoT and edge deployments. The results show that, compared to existing methods such as ECMP, HULA, AWCMP, and APS, ATL consistently reduces both the average and 99th-percentile AFCT while achieving superior elephant-flow throughput, with notable improvements in traffic stability and packet-ordering preservation. Furthermore, ATL demonstrates a favorable cost-performance trade-off ratio of 1:0.99, confirming its efficiency and feasibility within the resource-constrained P4 switch environment.
The rapid proliferation of IoT devices and ecosystems creates significant challenges in managing increasing data traffic and service requests while maintaining system performance [1]– [3]. In oneM2M-based IoT systems, overloaded Common Service Entities (CSEs) can become bottlenecks, leading to resource saturation, higher latency, and request loss [4]. To address these challenges, this paper proposes IoTScal-2CoM-ALO, an adaptive load orchestration framework that introduces a two-level collaboration model (2CoM) enabling distributed CSEs to cooperate within and across domains. The framework incorporates an Adaptive Load Orchestration (ALO) mechanism that continuously monitors key performance indicators, including CPU utilization, memory consumption, round-trip time (RTT), and packet loss, to detect overload conditions and dynamically redirect traffic to suitable neighboring CSEs. The proposed approach is evaluated in a simulated distributed oneM2M environment under heterogeneous traffic conditions. Experimental results demonstrate significant performance improvements compared with non-collaborative and static collaboration approaches, achieving up to 73% reduction in memory consumption, RTT peak reductions of up to 4750 ms, and success rate improvements of approximately 4.8%. These results highlight the effectiveness of IoTScal-2CoM-ALO in improving resource utilization and maintaining service continuity in scalable IoT systems.
S. Abourriche, A. Zyane, A. Ghammaz· International Conference on...· 0 citations