Network Function Virtualization (NFV) facilitates on-demand and flexible service provisioning to meet the escalating demands of Internet of Things (IoT) applications, enabled by Service Function Chain (SFC) technique. The widespread deployment of 5G has connected a massive number of devices and users to IoT networks, accelerating the expansion of IoT scales. IoT users’ service requirements exhibit heightened diversity and dynamism. Consequently, the SFC placement problem in Next Generation Multi-domain IoT (NGMIoT) networks has garnered significant attention. How to efficiently place SFCs under uncertain resource demands to adapt to evolving service request dynamics poses substantial challenges. Therefore, this paper investigates the Robust SFC Placement (RSFCP) problem in NGMIoT networks under resource demand uncertainty. Specifically, we formulate the RSFCP problem as an integer linear programming model to minimize overall SFC placement cost while ensuring service quality. We further prove the RSFCP problem is NP-hard and propose a greedy strategy based heuristic SFC placement algorithm to solve it. Finally, extensive simulation experiments are conducted to evaluate performance, demonstrating that the proposed algorithm outperforms benchmark mechanisms in terms of service acceptance rate and placement cost.

Latency-sensitive services across the end-edge-cloud continuum require not only low mean latency but explicit control of tail latency and data freshness. We propose Control-Loop Freshness-aware Service Function Chain orchestration (CLFSFC), a freshness-aware orchestration framework for Service Function Chains (SFCs) that jointly selects model variants and function placements. We define a Control-Loop Freshness (CLF) objective that combines 95th/99th-percentile (P95/P99) end-toend latency with an Age of Information (AoI) proxy. To make this objective operational under uncertainty, we allocate per-stage risk budgets via the union bound and convert mean/variance profiles into percentile constraints using Cantelli's inequality, yielding a two-stage greedy solver with interpretable quotas. We implement CLF-SFC with offline profiling of YOLOv5 n/s/m variants across end/edge/cloud devices, and evaluate it with profiling-driven measurements on COCO 2017 under synthesized network regimes. Across bandwidth and round-trip time settings, CLF-SFC reduces P95/P99 latency and Service Level Objective (SLO) violations relative to Edge-only, Cloud-only, and a riskagnostic heuristic; at high bandwidth it remains comparable to the Shortest-Latency-Path (SLP) baseline. The proposed CLF-SFC framework naturally fits embodied-AI pipelines where perception, fusion, and policy modules operate in a closed loop. By explicitly incorporating the Age-of-Information (AoI), our orchestration ties data freshness to control stability, complementing tail-latency minimization.

With the recent advancement in network technologies, the vehicle based medical networks extend medical services to mobile vehicles, thereby offering flexible and efficient healthcare services for vehicle users in need. The integration of vehicle based medical network and edge computing enables computation intensive medical service tasks to be offloaded on edge servers, to provide fast service response for vehicle users. An efficient task offloading and resource allocation strategy is critical for Vehicle based Medical Edge Computing System (VMECS) to satisfy real-time and reliability requirements while ensuring service performance. To this end, in this paper, we investigate the problem of task computation allocation in VMECS networks. By introducing deep reinforcement learning, we first present a novel VMECS architecture to automatically achieve the optimal task offloading and resource allocation through the multi-agent collaboration, thereby improving service performance. Then, we formulate the problem of task offloading and resource allocation in VMECS networks as an optimization model with the aim of maximizing task success rate by jointly considering communication interferences, resource allocation and delay requirements. To solve it, we further devise a Distributed distributional deterministic policy gradients based Task offloading and Resource allocation (DTR) algorithm. Final simulation results demonstrate that compared with benchmark algorithms, DTR algorithm can obtain higher task success rate, smaller service time, and less task processing time.

As an emerging architecture, edge computing enables resource limited terminal devices to offload their computation tasks to edge servers in the vicinity, to efficiently reduce delay and energy consumption. However, the continuous expansion of network scale and rapid growth of network traffic in recent years have brought huge challenges to task offloading and resource allocation. To tackle the challenges, by integrating Knowledge Defined Networking (KDN) and edge computing technologies, we design a novel Knowledge defined Edge Computing (KEC) architecture, to achieve intelligent resource allocation and task offloading in dynamic large-scale edge computing networks. We formulate the task offloading and resource allocation optimization problem, to minimize delay and energy consumption, by considering resource requirements and controller deployment. To solve it, we present an intelligent Resource Allocation based Task Offloading (TORA) mechanism, where a Multi-Agent SD3 based resource allocation (MASD3) algorithm is devised to perform efficient resource allocation. To adapt to the rapid expansion of network scale, we design a resource Allocation based Controller Deployment and task offloading Decision (DACD) algorithm, to perform the optimal controller deployment and task offloading. Extensive simulation experiments demonstrate the effectiveness and efficiency of our proposed solution, and TORA mechanism outperforms comparison mechanisms on delay and energy consumption.

The Fifth-Generation mobile networks (5G) and Beyond 5G (B5G) have been proposed to support a variety of application scenarios, such as enhanced Mobile Broadband (eMBB), ultra-Reliable Low-Latency Communications (uRLLC), and massive Machine Type Communications (mMTC). On the other hand, Mobile Edge Computing (MEC) and Network Functions Virtualization (NFV) technologies have been widely advocated by service providers to meet diverse service demands and reduce operational costs. To alleviate the pressure on the edge network, resource consumption can be minimized by considering the reuse of Virtual Network Function (VNF) instances. However, implementing VNF chain deployment with latency guarantees and resource efficiency in a distributed network architecture remains an urgent issue to be addressed. In this paper, we explore the Service Function Chains (SFCs) orchestration problem with distributed edge network resources, aiming to design efficient service flow routing and resource allocation schemes to significantly respond to local user requests. We propose a low-complexity Distributed SFCs Orchestration algorithm with VNF Reuse (DSOR), which initially uses local information at the edge to explore the VNFs orchestration scheme and executes the distributed service orchestration. Subsequently, service chains are deployed based on asynchronous consensus to enhance network utility and reduce resource costs. Finally, the performance of DSOR is evaluated through extensive simulation experiments. The experimental results indicate that DSOR can improve the utilization of network resources, as well as the response rate to edge service requests.

Network Function Virtualization (NFV) achieves flexible provisioning of network services by using Service Function Chain (SFC) composed of a set of Virtual Network Functions (VNFs). However, complex multi-domain networks pose serious challenges to multi-domain service deployment with availability guarantee. In this paper, we study the availability and cost aware multi-domain service deployment optimization problem. We formulate a multi-objective optimization model with the aim to minimize resource consumption cost and operating cost, while guaranteeing availability by jointly considering VNF failures and server failures, as well as cross-domain deployment operating cost. Then, we design a VNF backup based multi-domain SFC deployment algorithm to reduce resource consumption cost and operating cost. The evaluation results demonstrate that our proposed algorithm can achieve lower resource consumption cost and operating cost than comparison algorithms.