Large language model (LLM) services increasingly rely on distributed inference across multiple GPU servers to sustain concurrent requests under limited compute, memory, and bandwidth resources. In such settings, a partitioned LLM can be represented as an inference chain (InFC), where the deployment decision determines both the sustainable concurrency ceiling (SCC) on the revenue side and the memory and communication overhead on the cost side. This paper studies the profit-aware inference chain deployment (InFCD) problem in heterogeneous data center networks. We show that increasing the InFC length does not monotonically improve profit: finer partitioning can relieve per-GPU resource bottlenecks and improve SCC, but may also increase deployment spread, inference path length, and internal traffic. To capture this tradeoff, we formulate profit-aware InFCD by jointly modeling static-weight vRAM occupation, per-user KV-cache occupation, user-side traffic, internal boundary traffic, and resource-coupled SCC, and prove its NP-hardness. We then propose the Maximum Sub-module Deployment Gain (MSDG) score and design an MSDG-based greedy algorithm. Theoretical analysis characterizes its online complexity and establishes a conditional positive-profit preservation property. Simulations show that MSDG improves total profit over SCC-oriented, cost-oriented, and local-profit-oriented baselines, characterize empirical optimality gaps and SLO sensitivity.

Incomplete multi-view clustering (IMVC) has recently attracted increasing attention and achieved notable progress in computer vision. However, existing IMVC approaches still face several critical challenges. First, most methods fail to capture the inherent nonlinear structures of real-world data. Second, they fail to sufficiently exploit low-rankness and smoothness of multiple views. To overcome these limitations, we propose a novel method, termed Incomplete Multi-View Kernel Subspace Clustering with Tensor-Correlated Total Variation Regularization (KSC-TCTV), which integrates the ability of kernels to capture nonlinear separability with the strength of tensors in characterizing high-order correlations. Specifically, KSC-TCTV first effectively models nonlinear structures by embedding data into a kernel Hilbert space. And then, we introduce a log-based tensor-correlated total variation (Logt-CTV) regularizer in the kernel space, which jointly enforces global low-rankness for inter-view dependency modeling and local smoothness for intra-view structure preservation. The Logt-CTV employs logarithmic non- convex relaxation to mitigate the estimation bias. Experiments on several public benchmark datasets demonstrate that KSC-TCTV outperforms the state-of-the-art IMVC methods.

The accuracy of digital twin models hinges on the prompt collection of information from the vehicular environment. However, the high mobility of vehicles and the dynamically changing network environment pose significant challenges. Dynamic twin model migration can reduce the Age of Information (AoI) by bringing twin models closer to their vehicles. Existing works rarely consider the inherent differences in optimization cycles between digital twin model migration and data upload, which potentially leads to suboptimal cost efficiency and information freshness. Specifically, real-time vehicular data must be rapidly uploaded to edge servers to ensure the accuracy and timeliness of digital twin models, while frequent migration of twin models over short periods incurs substantial costs. Therefore, we propose a dual-timescale bilevel learning approach, where the upper-layer learning optimizes twin model migration decisions on a long timescale to achieve forward-looking model migration, and the lower-layer learning optimizes data upload and resource allocation decisions on a short timescale to ensure the accuracy and timeliness of digital twin models. Then, we design a multi-agent selective parameter sharing approach based on spatiotemporal dependency correlations to accelerate model convergence and reduce communication costs among agents. Furthermore, through a rigorous theoretical analysis, we prove the convergence of the dual-timescale bilevel learning with broad applicability. Finally, numerical results demonstrate that our algorithm outperforms comparison algorithms in terms of convergence, AoI, and system cost, achieving at least a 21.30% reduction in AoI and a 14.58% reduction in system cost compared to the benchmark algorithms.

Deploying ultra-reliable and low-latency communication (URLLC) service function chains (SFCs) is imperative for applications demanding stringent latency and reliability performances. In these applications, ensuring uninterrupted service hinges on establishing fault-disjoint primary and backup service function paths (SFPs). However, existing techniques for deploying SFCs fall short in optimizing latency differentials between the primary and backup SFPs, posing risks of service disruptions in critical URLLC applications like remote surgery, smart factory, and unmanned vehicle systems. In this work, we investigate pioneering techniques to efficiently optimize the primary and backup SFP latencies while minimizing their differentials. We formally formulate the problem of ultra-reliable and low-latency SFC deployment (URLLC-SD) and show its NPhardness. We develop an innovative algorithm, the Yen-based SFP Identification in Layered Graph (YANG), which optimizes the equal-weight composite latency objectives with symmetric QoS/SLA for primary and backup SFPs at the expense of runtime complexity. Through extensive simulations, we demonstrate the YANG's superiority, surpassing state-of-the-art benchmarks. In particular, YANG achieves the highest acceptance rates under specific constraints on SFP latency and latency differentials. Furthermore, our analysis reveals interesting insights into the selection of good path candidates to optimize URLLC-SD.

The model compression techniques enable deploying compressed large language models (CMs) at network edge, facilitating convenient provision of AI-generated content (AIGC) services. To ensure timely delivery of these services, efficient placement of CMs across resource-constrained edge networks is essential. In this work, we investigate how to obtain latency-efficient CM placement across resource-constrained edge networks. With the objective of service latency optimization, we formulate the CM placement in resource-constrained network (CPRN) problem and establish its NP-hardness. We propose the Migration-agent-based Deep Reinforcement Learning (M-DRL) approach, which incorporates a specially designed migration agent tailored for such placement problems. To enhance training efficiency, we incorporate efficient heuristic placement results into the environment of M-DRL, developing our Heuristic-guided M-DRL (HM-DRL) approach. Our extensive simulation results demonstrate that HM-DRL outperforms an extended benchmark in service latency, while maintaining a low training overhead.

Cloud-based large language models (LLMs) have gained widespread adoption among human users. However, when the users shift to Internet-enabled machines, centralized LLM systems often suffer from high latency and fail to provide timely responses. Deploying LLMs over edge networks presents a promising alternative, yet maintaining an up-to-date knowledge base to address the dynamic and time-sensitive demands of diverse machine users remains a significant challenge. Consequently, there is a pressing need for fast and cost-efficient LLM deployment strategies tailored to edge environments. In this work, we address the problem of deploying mixture-of-experts (MoE) LLMs in a runtime- and cost-efficient manner. We formally define the Expert Model Deployment in Edge Networks (EMD-EN) problem, aiming to minimize deployment costs. Leveraging the inherent modularity of MoE, we propose a novel Neighbor-First Centrality (NFC) metric to facilitate the placement of model components across edge nodes and design the NFC-based Mixture of Expert layer Deployment (NFC-MoED) algorithm. Our results show that NFC-MoED substantially improves runtime efficiency and maintains near-optimal deployment costs compared to the brute-force benchmark.

To support edge intelligence, knowledge distillation (KD) is widely employed to compress large language models (LLMs) into smaller, domain-specific student models. However, due to the limited generalization capabilities of student models, they may fail to provide accurate responses across diverse domains. In such cases, the teacher model serves as a complementary component, handling queries that exceed the scope of student models. In line with the KD paradigm, this work investigates a collaborative deployment framework in which multiple student models are distributed across the network edge to serve the majority of client requests. In contrast, a centralized teacher model addresses more complex or ambiguous queries. To begin, we formally define the Student Model Placement in Edge Networks (SMP-EN) problem, aiming to minimize total access costs. We prove that SMP-EN is NP-hard, and to address this challenge, we introduce an Access Cost Measure (ACM) that quantifies the expected access costs. Building upon this measure, we propose the ACM-based Student Model Placement (ACM-SMP) algorithm to determine student model placement efficiently. Extensive simulations show that ACM-SMP significantly reduces the average expected client access cost compared to benchmarks.

With its rapid development, AIGC applications have expanded to diverse generative content, including text, images, audio, and videos. To enhance the AIGC's output quality, prompt chains are proposed to structure the generation process. Owing to the prompt's data processing nature, one compromised prompt engineering-enabled server (PES) may propagate vulnerabilities across networks, leading to unintended content generation, system risks, and potential user trust issues. To mitigate these risks, this work adopts a zero-trust (ZT) security framework to protect inter-server communications. We define the problem of prompt chain deployment in ZT-enabled networks(PCD-ZT), with the objective of minimizing total service costs, encompassing both deployment and ZT verification costs. To address this, we introduce the verification-cost-balance (VCB) factor that helps reduce ZT verifications to save the overall service cost and accordingly propose an algorithm called sub-prompt-chain brand and bound (SCBB). Extensive simulations demonstrate that our SCBB achieves overall service cost reductions of 4.67 % and 13.89 %, and cuts verification costs by 11.86 % and 30.50 %, compared to the benchmarks extended from the state-of-the-art.

With the widespread adoption of large language models (LLMs) like GPT, user experiences in various interactive applications have significantly improved. However, reports from OpenAI highlight that GPT clients are now facing high response delays and frequent interruptions, particularly during peak usage hours, due to limited computation resources. This challenge is expected to escalate as machines are interacting with GPT models at higher frequencies, with greater data volumes, and over longer lifecycles. A promising solution is to deploy LLMs across edge networks to efficiently distribute the huge resource demands. This work presents the very first efforts at exploring how to cost-effectively deploy expert models from a mixture of experts (MoE) LLM within edge networks. We introduce the expert models deployment in edge networks (EMD-EN) problem, focusing on optimizing deployment costs. To address this, we propose a novel least cost gain (LCG) measure for selecting appropriate physical nodes to host expert models and present a corresponding LCG-based expert models deployment (LCGEMD) algorithm. Extensive simulations show that our approach outperforms the benchmarks by an average of 17.31% and 36.98% in terms of deployment cost reduction.

Recent advances in large language models (LLMs) have empowered robots and drones with autonomous decision-making capabilities. Due to the stringent real-time requirements of these applications, LLM inference must be performed at the network edge. However, hosting high-precision LLMs on a single edge server is often infeasible, creating challenges in efficiently distributing LLM deployments across edge networks. This work addresses these challenges by formulating and solving the profit maximization problem for distributed LLM inference deployment. We first formally define the Profit-Centric Inference Chain Deployment (PC-InCD) problem. To solve PC-InCD, we introduce a novel Local Maximal Profit (LMP) factor that enables effective edge server selection for hosting LLM sub-modules, and we propose the LMP-based Inference Chain Deployment (LMP-InCD) algorithm. Extensive simulations demonstrate that LMP-InCD significantly outperforms benchmark methods in maximizing profit across diverse network conditions.

The increasing adoption of Electric Vehicles (EVs) is driving the need for secure, efficient, and intelligent charging systems. While EVs offer a sustainable alternative to conventional vehicles, challenges such as charging station availability, range anxiety, and the privacy risks associated with sharing sensitive data—like location and energy usage—remain significant barriers to broader adoption. To address these challenges, this paper introduces a novel privacy-preserving multi-source data-driven framework for intelligent EV charging optimization. The proposed system combines a hybrid optimization strategy incorporating an enhanced Hungarian Matching Algorithm for cost-efficient EV-to-charging station assignment, a Random Forest regression model for accurate EV range prediction using contextual data, and a Laplace mechanism-based differential privacy module to protect user location data. This unified framework not only improves charging efficiency and predictive accuracy but also provides formal privacy guarantees against inference attacks. Extensive experiments conducted on synthetic datasets demonstrate the framework’s effectiveness in reducing charging costs, enhancing range prediction accuracy, and preserving EV user privacy. The results suggest strong potential for real-world deployment in future intelligent transportation systems.

While being able to avoid single point failures, emerging decentralized security techniques are facing new challenges of reliability, robustness, and privacy preservation in blockchain-enabled Internet of Things (IoT) systems. To circumvent these issues, a zero-trust security scheme is proposed through distributed access control, enhanced authentication, dynamic authorization, and privacy preservation enabled by the consortium blockchain. The proposed scheme integrates three key components, i.e., a distributed recommendation mechanism, where multiple authorized nodes are utilized as referrers to efficiently confer their trust on a new public entity for enhanced authentication; an anonymous credential generation strategy, which is developed for the new entity to further protect its privacy from linking attacks; and an adaptive reputation update strategy, which is proposed for evaluating the nodes’ behaviors in the system for accountability and dynamic multiple-level authorization. The proposed scheme is implemented in a Hyperledge Fabric and the results show that it significantly enhances security and protects private information.

Website Fingerprinting (WF) aims to identify users’ visited websites from encrypted traffic traces, disabling the anonymity of encrypted communication like the Tor network. It is practical to use historically labeled (source) data, e.g., public datasets, to pre-train a WF model, and then collect few incoming (target) data to re-train this model within a low cost. Unfortunately, there is always a considerable difference of latent feature distributions between the source and target data (i.e., the cross-domain problem) and an inevitable bias of feature distribution caused by a limited volume of target data (i.e., the biased distribution problem). Although current Few-Shot Learning-based WF (FSWF) methods achieve satisfactory performance on the efficient establishment, they lack cross-domain transferability, and meanwhile, are unable to alleviate the distribution bias. In this paper, we first systematically analyze the cross-domain problem among different domains of traffics, revealing the ubiquity and dominant factors of it. To mitigate the cross-domain and biased distribution problems, we propose a Distribution Calibrated Website Fingerprinting (DCWF) method that incorporates a two-stage distribution calibration process and a tailored circle network. In the two-stage calibration process, we first devise a re-modeling mechanism capturing the information distribution of the target domain to extract representative features, and then design a calibration process to adjust the biased distribution of the target domain. Subsequently, a tailored circle network is proposed to reduce the noise caused by the calibration process. Finally, extensive experiments are conducted and the results demonstrate the superiority of our DCWF over comparisons under both close-world and open-world settings.

Semantic communication (SC) has emerged as a promising paradigm for upcoming intelligent applications, enabling mobile devices to collaboratively execute intelligent tasks with edge servers through computation offloading. However, few studies have addressed the problem of collaborative inference in SC networks. Traditional collaborative inference mechanisms may suffer performance decline in SC systems and are vulnerable to eavesdroppers. To address these issues, first, we present an encryptor that encrypts semantic information to avoid privacy leakage and a decryptor for restoration. Besides, we propose a novel SC-empowered edge computing framework enabling mobile devices to deploy a partial semantic encoder and offload the rest to edge servers. Based on this framework, we formulate the collaborative inference optimization problem, jointly optimizing delay, energy consumption, and privacy leakage. DNNPart is devised based on deep deterministic policy gradient to address the problem, which consists of a semantic attention mechanism that enables it to focus on important state variables, a hybrid action representation method that makes it adapt to mixed discrete and continuous action spaces, a dynamic model splitting algorithm that locates the optimal partition layer and adaptively splits the semantic coders. Integrated with these components, DNNPart iteratively optimizes the offloading strategy to find the optimal offloading strategy. Extensive simulations were conducted to verify the effectiveness of the proposed method by comparing it with baseline mechanisms.

Internet-connected devices generate service function chain (SFC) requests for reliability-sensitive applications such as smart factories and intelligent healthcare. To facilitate reliable SFC provisioning, one can employ dedicated SFC protection approaches to protect the primary service function path (SFP) by constructing a fault-disjointed backup SFP. Notably, the construction processes of the fault-disjointed primary and backup SFPs are interdependent, and the direct application of existing SFC embedding approaches to construct these SFPs by separate processes may not effectively optimize overall resource consumption. In this work, for the first time, we comprehensively study how to embed and protect an SFC through collaborative processes that have provable bounds. We formally define the problem of SFC embedding and protection (SFCEP), for which we develop the novel techniques of a backup SFP identifier (BSI) and resource-aware Bellman-Ford loop (RBL) to address the challenges posed by collaborative embedding and protection. On the basis of these techniques, we propose an efficient algorithm called optimal SFC embedding and protection (Opt-SEP). When the network resources are sufficient to accommodate an incoming SFC request, we prove that Opt-SEP can minimize the overall resource cost of creating a pair of fault-disjointed primary and backup SFPs. Moreover, for cases in which the network resources are limited, our extensive simulation results show that Opt-SEP significantly outperforms the benchmarks.

This paper studies a distributed semantic communication (SC) problem for multivariate time series forecasting tasks in edge environments, with heterogeneous clients considered. At the client side, a semantic encoder is composed of a number of federated blocks and this number is subject to local resource availability. Each federated block consists of a patch-wise attention module (PAM) and a federated adapter, extracting semantic information for efficient transmission across wireless channels. Based on the federated adapters, this paper proposes an SC-oriented heterogeneous federated learning architecture, named SC-FedAda. SC-FedAda adopts self-distillation to facilitate cross-client and cross-layer knowledge sharing, enabling efficient collaborative inference. At the edge server, semantic signals are fed into a channel decoder and then a semantic decoder. The semantic decoder consists of a PAM and a fully connected network for forecasting tasks. Simulation results demonstrate that SC-FedAda outperforms four state-of-the-art federated learning-based structures under three types of wireless channels, i.e. SC-FedAda achieves much lower forecasting loss on three widely-used time series datasets, particularly in low signal-to-noise ratio scenarios.

Mobile edge computing (MEC) decentralizes the computational and storage capabilities of the network to edge nodes, providing support for the dynamic deployment and rapid response of mobile services. However, the large-scale distributed deployment of edge nodes, their widespread geographic distribution, multi-dimensional and complexly dependent service characteristics, and the dynamically changing network environment pose challenges to energy-efficient service deployment. In this paper, we consider multi-dimensional features for service deployment, including dynamic traffic demand, geography information, service semantics, and service popularity, with the aim of improving the availability of edge services and reducing network energy consumption. Firstly, to handle the large volume of edge service data, we design a multi-dimensional feature extraction approach based on the Transformer model, which does not rely on the sequential order of data and can enhance computational efficiency of edge models through parallel processing. Then, to adapt to the dynamically changing edge network environment, we propose an Energy-Efficient Service Deployment algorithm (EESD) based on the improved Dueling Deep Q-Network, which makes service deployment and base station switching decisions in a learning-based manner. Finally, simulation results demonstrate that EESD outperforms comparison algorithms in terms of model convergence, system total cost, and energy consumption.

In the emerging applications of edge computing (e.g., unmanned factories and meta-verse), network requests are required to be securely and reliably delivered in the form of service function chains (SFCs). To enhance security, security-aware SFs are employed in the SFC, and this type of SFC is referred to as the security-aware SFC (S-SFC). For reliability, service providers can employ a dedicated backup SFC to protect the primary one. However, no existing works addressed the SFC deployment mechanisms that jointly consider SFC reliability and security. For this, here, we investigate the problem of jointly embedding and protecting a security-aware SFC. To efficiently compose, embed, and protect an S-SFC, we propose the S-SFC asymmetric protection concept, which allows the primary and backup SFCs not necessarily to follow an identical structure as the traditional SFC dedicated protection does. Next, we formulate the problem of S-SFC composing, embedding, and protection (S-SFCEP) and prove its NP-hardness. To tackle this problem, we formulate an efficient algorithm, namely, sub-chain-based S-SFC deployment (SCB-SD). Our extensive simulation results show that the proposed SCB-SD outperforms the state-of-the-art benchmarks by an average of 13.86% and 23.19%, respectively.

To provide dedicated protection in Network Function Virtualization (NFV), a reliability-aware Service Function Chain (SFC) can be deployed using two disjoint paths: a primary path and a backup path. In the event of network failures along the primary path, the working traffic is switched to the backup path to maintain service continuity. The process of accommodating reliability-aware SFCs is commonly referred to as SFC Deployment and Protection (SFCDP), which is proven to be NP-hard. In this work, we introduce a novel Network Service Deployment (NSD) transformer that can effectively incorporate and utilize fine-grained information regarding the network re-sources and the SFC requests. We develop a deep reinforcement learning framework based on NSD transformer (DRL-NSD) to effectively optimize the process of SFCDP. We conduct extensive simulations to validate the NSD transformer and compare the proposed NSD transformer with two benchmark neural network architectures. Our experimental results demonstrate that the NSD transformer outperforms the benchmarks across a variety of network topologies and network load settings.

Since the data samples on client devices are usually non-independent and non-identically distributed (non-IID), this will challenge the convergence of federated learning (FL) and reduce communication efficiency. This paper proposes FedQMIX, a node selection algorithm based on multi-agent reinforcement learning(MARL), to address these challenges. Firstly, we observe a connection between model weights and data distribution, and a clustering algorithm can group clients with similar data distribution into the same cluster. Secondly, we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward, penalizing the use of more communication rounds and thereby improving the communication efficiency of FL. Finally, experiments show that FedQMIX can reduce the number of communication rounds by 11% and 30% on the MNIST and CIFAR-10 datasets, respectively, compared to the baseline algorithm(Favor).

This paper addresses the virtual network (VN) mapping problems for the network profit optimization in space-division-multiplexing optical data center networks (SDM-ODCNs). We first define both link resource availability (LRA) and node resource availability (NRA) for the VN mapping optimization, by which an integer linear program (ILP) model and two VN mapping approaches are proposed to achieve the high network profit. Simulation results verify that our proposed LRA VN mapping approach achieves greatly close network performance to that by solving solutions of the integer linear program model and significantly outperforms its counterpart approaches. The improved network profits out of the VN mapping is as a result of well suppressed average crosstalk, rejection ratio of VNs, and spectrum fragmentation ratio in SDM-ODCNs.

Due to an increasing amount of users, service providers are paying more attentions on how to properly tackle the User Allocation (UA) problem. In the literature, few solutions to the UA problem took into account the heterogeneity of devices and the complexity of the network. Therefore, the paper takes these two factors into account and studies the UA problem in Non-Orthogonal Multiple Access (NOMA)-Based heterogeneous edge computing scenarios. To solve the above problem, we propose the Heuristic Ant colony Systems (HAS) algorithm, which aims to enhance the number of allocated users while reducing the overall deadline violation time. Extensive evaluations that employ a public dataset demonstrate the benefits of our proposed approach.

Frequent cyber-attacks force the service provider to employ security-aware service functions (SFs) to accommodate client network requests. Thanks to virtualization techniques' maturity, a security-aware SF can be provided by diverse vendors with various configurations, each of which needs various implementation cost and provides different security levels. When a client's network request comes, the multi-configuration SFs could compose various security-aware service function chains (S-SFCs) to flexibly satisfy the security requirement. In this paper, we investigate how to efficiently compose and embed an S-SFC to satisfy the client's security requirement. With the objective of cost optimization, we formulate the problem of security-aware service function chain deployment and prove its NP-hardness. We propose the technique of the security-cost-balance (SCB) factor to efficiently consider the capability of a physical node and the cost when the node is employed to satisfy the client's security requirement. Based on this technique, we develop an efficient algorithm called SCB-based S-SFC deployment (SCB-SD). The simulation results show that SCB-SD significantly outperforms the benchmarks directly extended from the state-of-the-art.

In network function virtualization (NFV), service providers can accommodate services from clients by deploying software-based functions, called service functions (SFs), on commodity servers. The deployed SFs are connected to SF forwarders for efficient management in these commodity servers. Many efforts have been put into protecting SFs from failures. However, little attention has been paid to protecting SF forwarders from failures. In this paper, we investigate how to efficiently allocate backup computing resources when facing SF and SF forwarder failures. To protect the deployed SFs with the minimum backup computing resources, we define a new SF forwarder off-site shared protection problem and approve its NP-hardness. An efficient heuristic algorithm is proposed and proved to be logarithmic approximate. Extensive simulation results show that the proposed algorithm significantly outperforms the approaches directly extended from the existing work.

Emerging machine learning techniques enable Internet-connected devices to generate service function chain (SFC) requests for reliability-sensitive applications. To facilitate reliable SFC provisioning, prior works have proposed the SFC dedicated protection approach for fog and cloud networks, which generally have abundant resources and connectivity. However, with limited resources and connectivity, it might be inefficient to directly employ these approaches at the network edge. In this work, we study how to efficiently embed and provide dedicated protection when accommodating SFCs at the network edge. We formally define the problem of SFC embedding with dedicated protection (SFCE-DP) to minimize the bandwidth resources usage at the network edge. Based on the proposed technique of augmenting path in a layered graph, we construct an efficient algorithm, called augmenting-path-based SFC embedding with dedicated protection (AP-SDP), to optimize SFCE-DP. When the network resources are limited, our results show that AP-SDP significantly outperforms the benchmark that is directly extended from the state-of-the-art.

In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of${k}$-heterogeneous-faults-tolerance and propose an augmented protection graph, called${k}$-connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called${k}$-heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques:${k}$-connected network slicing (KC-NS) and${k}$-connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when${k}$= 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.

The ultra-reliable and low latency communication (URLLC) service in 5G/6G will be delivered through a sequence of software-based network functions, also known as a service function chain (SFC). To satisfy the ultra-reliable requirement of the URLLC service, fault-tolerance in URLLC SFC processes is required. However, achieving deterministic fault-tolerance in URLLC SFC delivery is challenging as physical/virtual network failures and hardware/software failures have to be jointly consid-ered. In this work, we first introduce an augmented SF protection graph, called k-connected service function slicing (KC-SFS), which can facilitate the SF protection against multiple concurrent physical/virtual node and physical link failures. Based on the KC-SFS, we define a new problem called deterministic fault-tolerant service function slicing (DFT-SFC) and formulate it with a mathematical model. To solve DFT-SFC, we propose an efficient heuristic algorithm, called service function slice embedding (SFSE), which employs the k-connected network slicing technique (k-NST). Via thorough mathematical analysis, we prove that k-NST achieves 2-approximation. Meanwhile, our extensive experimental results show that the proposed SFSE guarantees deterministic fault-tolerance and outperforms the schemes directly extended from the stste -of - the art.

Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.

In network function virtualization (NFV), the customer may request a set of virtual network functions (VNFs) that the customer traffic will go through. To accommodate such requests, the service providers have to embed the requested VNFs onto the substrate network (SN) to form an actual traffic forwarding path called service function path (SFP). In the elastic optical network (EON), how to protect the running network services against VNF failures becomes an attractive research focus. Most existing work concentrates on the protection or restoration of the physical node or fiber link failures in the SN. Few research attention has been paid to the failure of the virtual machines running a VNF. In this paper, we study how to protect VNFs when a VNF failure occurs in an EON. We define a new VNF failure protection cover (VFPC) problem and mathematically formulate VFPC. We propose a protection cover list based VNF protection (PCL-VP) algorithm against any single VNF failure while satisfying the latency requirement. Extensive simulations and analysis show the effectiveness of the proposed algorithm.

With online social networks (OSNs), people are exposed to tons of fake information or misleading posts. Celebrities sometimes intentionally create misleading posts in OSNs to guide people for commercial or marketing purposes. The intentional phrases from such posts can affect the online rating and even lead to a frenzy shopping. That’s part of the reasons that the top social media influencers are targeted by merchants to help promote products. The Federal Trade Commission (FTC) requires that all sponsored posts must be clearly disclosed. However, many influencers do not follow the FTC rules. As a result, people may be misled by the undisclosed sponsorship. In this study, for the first time, we explore the credibility of posts on Instagram and analyze if an influencer complies with the FTC requirements. We build an effective Undisclosed Sponsored Post Detection (USPD) framework based on an ensemble of machine learning classifiers. The USPD framework consists of three main processes: (i) feature extraction, (ii) model construction and (iii) credibility and integrity analysis. Our analysis and experiments demonstrate that the proposed framework can achieve a high accuracy of 83% for undisclosed sponsored post detection. The proposed framework also takes advantages of the text, user and image features in OSN posts to effectively analyze how much an influencer can be trusted.

The ultra-fast speed and massive capacity in 5G networks push huge amounts of data to networks. With network function virtualization, these data will go through multiple service functions (SFs) and big data processing/analysis. As a result, the processing delay from such SFs and data processing/analysis can significantly impact the delivery of latency-sensitive services. To reduce the processing delay, network function parallelism techniques are introduced to allow multiple SFs running parallelly for the same request. In this work, we study how to apply network function parallelism into SF chaining and embedding to optimize the latency. When physical nodes have unlimited computing resource, we propose the mixed integer programming based parallelism-aware SFC optimization (MIP-PS) algorithm. Our analysis proves the proposed MIP-PS is integer-approximation. When physical nodes have limited computing resource, we propose the latency factor based parallelism-aware SFC optimization (LF-PS) algorithm. Our extensive simulations demonstrate that our proposed schemes outperform the approaches extended directly from the existing work.

Network function virtualization (NFV) is introduced to effectively deliver end-to-end network services for the emerging Internet of Things (IoT), multiaccess edge computing, and 5G communication techniques. In NFV, the network service request can be accommodated in the form of a service function chain (SFC). The SFC will have to reserve abundant resources, such as link bandwidth, service functions, and computation in the physical network to meet the demands of customers. Minimizing the cost from the resource reservation in NFV remains challenging, even though a few works in the literature proposed cost-optimization methodologies with assumptions to guarantee their correctness. In this article, we comprehensively investigate how to minimize the cost when delivering network services as SFCs with provable bounds and fewer assumptions. We formally define the problem of minimum cost service function chaining and embedding (MC-SFCE) and propose an algorithm, namely, cost factor-based SFCE optimization with shortcut (COFO-SC), for MC-SFCE. Novel mathematical analysis is provided to demonstrate the correctness of our approaches and related bounds. Our extensive simulations and analysis also show that the proposed COFO-SC outperforms the schemes directly extended from the existing work.

In network function virtualization (NFV), the client’s service requests will go through multiple service functions (SFs). The instances of the required SFs will be hosted on the geographically-distributed physical nodes in physical networks (PNs). The failure of any physical nodes or virtual nodes (i.e., SF instances) will impact the delivery of services and the client’s experience. It is essential for service providers to take the node failure and network reliability into account. Different from physical node failures that will affect the PN’s topology and connectivity, virtual node failures impact the services delivery of certain clients. As a result, applying traditional backup schemes that are designed for physical node failures may not efficiently provide protection for virtual node failures. In this work, we define and mathematically formulate a new latency-bounded off-site virtual node protection (LOVNP) problem in NFV. After proving the NP-hardness of the LOVNP problem, we introduce a novel shared protection technique called pairwise node protection to effectively facilitate the protection of node failures in NFV. Then, we propose an efficient heuristic algorithm called protection centrality based pairwise node protection (PC-PNP) to optimize the LOVNP problem and prove that PC-PNP has a logarithm-approximation boundary. Our extensive simulations and analysis show that the proposed algorithm significantly outperforms the algorithms that are extended from the existing work.

In Network Function Virtualization (NFV), to satisfy the Service Functions (SFs) requested by a customer, service providers will composite a Service Function Chain (SFC) and embed it onto the shared Substrate Network (SN). For many latency-sensitive and computing-intensive applications, the customer forwards data to the cloud/server and the cloud/server sends the results/models back, which may require different SFs to handle the forward and backward traffic. The SFC that requires different SFs in the forward and backward directions is referred to as hybrid SFC (h-SFC). In this paper, we, for the first time, comprehensively study how to optimize the latency in Hybrid SFC composition and Embedding (HSFCE). When each substrate node provides only one unique SF, we prove the NP-hardness of HSFCE and propose the first 2-approximation algorithm to jointly optimize the processes of h-SFC construction and embedding, which is called Eulerian Circuit based Hybrid SFP optimization (EC-HSFP). When a substrate node provides various SFs, we extend EC-HSFP and propose the efficient Betweenness Centrality based Hybrid SFP optimization (BC-HSFP) algorithm. Our extensive simulations and analysis show that EC-HSFP can hold the 2-approximation, while BC-HSFP outperforms the algorithms directly extended from the state-of-art techniques by an average of 20%.

The emerging multiaccess edge computing (MEC) architecture brings the needed computing resource to the network edge. Many 5G and Internet of Things (IoT) applications are latency sensitive and computation intensive in MEC systems. To flexibly provide and manage the network service requests in MEC systems, network function virtualization (NFV) can be employed to create a chain of service functions (SFs), namely, SF chain (SFC). Through SFC, the customer forwards user data to the edge server/cloud, and the edge server/cloud may return the processed results/models to the customer. When the forward and backward traffic is carrying different content, different SFs may be required for the forward and backward traffic, which requires a hybrid SFC (h-SFC). In this article, we study how to minimize the latency cost when embedding an h-SFC in MEC systems. We define a new problem called minimum latency hybrid SFC embedding (ML-HSFCE) and propose an algorithm, namely, optimal hybrid SFC embedding (Opt-HSFCE) to optimally embed a given h-SFC in MEC systems. Our extensive simulations and analysis show that the proposed Opt-HSFCE needs much less runtime compared with the brutal force algorithm and significantly outperforms the schemes that are directly extended from the existing techniques.

Network Function Virtualization (NFV) takes advantages of the emerging technologies in virtualization and automation to offer new ways in design, deployment, and management of networking services. In NFV, the proprietary hardware-based network functions are replaced by the software-based modules named as Virtual Network Functions (VNFs) or Service Functions (SFs). A network service request from the customer can be formed by multiple SFs. To satisfy a network service request, the service provider has to chain the SFs in the request into a Service Function Chain (SFC) and embed the constructed SFC onto the shared substrate network. In this paper, we comprehensively study how to composite and embed an SFC onto a shared substrate network with unique service function. We formulate this problem with the Integer Linear Programming (ILP) technique. We also propose an efficient heuristic algorithm with 2-approximation boundary, namely, Spanning Closed Walk based SFC Embedding (SCW-SFCE). Our extensive simulations and analysis show that the proposed approach can achieve near-optimal performance in a small network and outperform the Nearest Neighbour (NN) algorithm.

Network Function Virtualization (NFV) technology decouples network functions from proprietary hardware equipments. As a result, Internet Service Providers (ISPs) implement software-based network functions on generic highvolume substrate network devices. In NFV, a Service Function Chain (SFC) is defined as an ordered set of abstract network functions running on specific substrate nodes (e.g., servers). A challenging issue in NFV management and orchestration is how to optimize the Dependence-aware SFC Embedding in substrate Optical networks (D_SFCE_O). In this paper, we propose a novel algorithm, namely, Dependence-aware SFC embedding with Least-Used consecutive subcarriers (D_SFC_LU), which jointly optimizes SFC design, SFC mapping and spectrum allocation in optical networks. To minimize resource consumption, D_SFC_LU takes advantages of the proposed techniques: Impact Factor based Node Selection (IFNS), Chain Node Mapping (CNM) and Chain-Fit (CF) spectrum allocation. Our simulation and analysis demonstrate that D_SFC_LU can efficiently embed a network requests while minimizing the required substrate resource in optical networks.

In Network Function Virtualization (NFV), Service Function Chain (SFC) is composed of Virtual Network Function (VNF) nodes that are chained via VNF links. SFCs can be specified as unidirectional or bidirectional. A unidirectional SFC (u-SFC) demands the traffic being forwarded via the VNFs in one direction, while a bidirectional SFC (b-SFC) requires bidirectional traffic flows. In this paper, for the first time, we investigate the problem of how to efficiently deploy a hybrid SFC (h-SFC), whereas some VNF nodes are required to process bidirectional traffic while others only handle unidirectional traffic. We define a new problem called hybrid SFC Deployment (h-SFCD). When each substrate node provides one unique VNF, we prove the NP-hardness of the h-SFCD problem and propose an approximate algorithm, namely, 2-approximation Hybrid Service function chain Deployment in Unique function networks (2-HSD-U). Our experimental results show that the proposed 2-HSD-U algorithm significantly outperforms the heuristic algorithm based on the traditional Nearest-Neighbor technique.

Network virtualization in optical networks enables the decoupling of network services from the underlying hardware infrastructure to allow multiple Virtual Optical Requests (VORs) sharing the same Substrate/physical Optical Network (SON). The challenge of mapping VORs onto the shared SON lies on how to efficiently allocate physical resource for the VORs, which is referred to as Virtual Optical Network Embedding (VONE). Many recent research focus on the NP-Hard VONE optimization problem. In this paper, for the first time, we explore how to efficiently map a given VOR for a multicast service onto a shared SON while considering the fanout (splitting/forwarding) limitation of the physical optical switches. We propose a novel algorithm, namely, Centrality-based Degree Bounded Shortest Path Tree (C-DB-SPT) to minimize the resource usage while satisfying the degree limitation in the shared SON. The experimental results show that the C-DB-SPT algorithm outperforms the traditional greedy-based algorithms as much as by 35% in terms of the total bandwidth consumption.

With network virtualization over Elastic Optical Networks (EONs), network services are decoupled from the underlying hardware infrastructure to enable multiple Virtual Optical Requests (VORs) sharing the same Substrate/physical Optical Network (SON). The embedding process of VORs onto the shared SON while satisfying the computing resource and spectrum allocation constraints is referred to Virtual Optical Network Embedding (VONE), which is an NP-Hard problem. In this paper, for the first time, we investigate how to efficiently map a given VOR in the form of virtual optical multicast tree onto an SON. We propose a novel algorithm that is called Impact Factor based Virtual Optical Multicast Tree Embedding (IF-VOMTE) to minimize the resource usage and avoid redundant multicast transmission in the shared SON. The experimental results show that our algorithm outperforms the schemes based on traditional techniques such as Greedy Node Mapping (GNM-SP) and First-Fit Node Mapping (FFNM-SP) in terms of the total cost of bandwidth consumption and the reduction of redundant multicast transmission.

Network virtualization enables the decoupling of network services from the underlying hardware infrastructure to allow the same Substrate/physical Network (SN) shared by multiple Virtual Network (VN) requests. The process of mapping virtual nodes and links onto a shared SN while satisfying the computing and bandwidth constraints is referred to Virtual Network Embedding (VNE) as an NP-hard problem. In this paper, for the first time, we explore how to efficiently map a given Virtual Multicast Tree (VMT) request onto a substrate network. We propose a novel algorithm, namely, Virtual Multicast Tree Embedding based on dynamic Impact Factor (VMTE-IF) to minimize the required resource and redundant multicast transmission in the substrate network. The experimental results show that our algorithm outperforms the traditional greedy-based algorithms over 50% in terms of the cost of bandwidth consumption.