Multi-modal knowledge graphs (MMKGs) enrich the semantic expression capabilities of traditional knowledge graphs by incorporating diverse modal information, showcasing immense potential in various knowledge reasoning tasks. However, existing MMKGs encounter numerous challenges in the link prediction task (i.e., knowledge graph completion reasoning), primarily due to the complexity and diversity of modal information and the imbalance in quality. These challenges make the efficient fusion and enhancement of multi-modal information difficult to achieve. Most existing methods adopt simple concatenation or weighted fusion of modal features, but such approaches fail to fully capture the deep semantic interactions between modalities and perform poorly when confronted with modal noise or missing information. To address these issues, this paper proposes a novel framework model-Adaptive Fusion and Modality Information Enhancement(AFME). This framework consists of two parts: the Modal Information Fusion module (MoIFu) and the Modal Information Enhancement module (MoIEn). By introducing a relationship-driven denoising mechanism and a dynamic weight allocation mechanism, the framework achieves efficient adaptive fusion of multi-modal information. It employs a generative adversarial network (GAN) structure to enable global guidance of structural modalities over feature modalities and adopts a multi-layer self-attention mechanism to optimize both intra- and inter-modal features. Finally, it jointly optimizes the losses of the triple prediction task and the adversarial generation task. Experimental results demonstrate that the AFME framework significantly improves multi-modal feature utilization and knowledge reasoning capabilities on multiple benchmark datasets, validating its efficiency and superiority in complex multi-modal scenarios.

As multilingual artificial intelligence systems proliferate, achieving robust cross-lingual understanding remains an open challenge. Recent works have made progress on visual question answering (VQA) models by pretraining on large English image-text datasets. However, there is a language gap as most models are English-centric. Existing attempts at multilingual VQA rely on machine translation or multilingual model pretraining, but cannot effectively transfer rich cross-modal knowledge from English models. In this work, we propose the cross-lingual multimodal knowledge transfer (CMKT) framework to efficiently extend English VQA models to non-English languages via knowledge distillation. Specifically, we introduce a code-mixed cross-lingual mask modeling (CCM) method to establish representations for new languages using small image-text data. We also design a multimodal knowledge distillation (MMKD) method to transfer modal understanding from English models by imitating their sequence processing. Experiments on the xGQA benchmark demonstrate that CMKT can effectively improve zero-shot learning and few-shot learning in non-English languages. Our method reduces the data and computation needed to train multilingual VQA models from scratch. The knowledge transfer paradigm enables non-English languages to inherit and generalize the intricate visual-semantic relationships learned from English. The results also show that the proposed method outperforms previous state-of-the-art methods in the zero-shot setting on the xGQA dataset.

Large Multimodal Models (LMMs) excel in English multimedia tasks but face challenges in adapting to other languages due to linguistic diversity, limited non-English multimodal data, and high training costs. Existing approaches rely on machine-translated multimodal corpora or multilingual large language models, yet they demand substantial resources and achieve only modest zero-shot cross-lingual transfer performance, as shown in the IGLUE benchmark. In this work, we propose SMSA, a Syntax-aware Multimodal Semantic Adaptation approach, which efficiently extends vision-language models (VLMs) to multiple languages via a lightweight adaptation module. Instead of learning from scratch, SMSA transfers multimodal knowledge from English-trained models using two key components: (1) a Syntax-aware Adapter (SAA), which restructures multilingual text representations to align better with English syntax, reducing cross-lingual misalignment; (2) a Multimodal Semantic Distillation (MSD) method, which enables the model to mimic English sequence processing and retain multimodal associations across languages. This allows efficient adaptation to new languages while preserving the original model's strong multimodal capabilities. We extend an MoE-based VLM to 8 languages using a small translation dataset. Evaluations on the IGLUE benchmark show that SMSA achieves strong zero-shot transfer, outperforming some multilingual LMMs and demonstrating its effectiveness in cross-lingual vision-language adaptation.

In the realm of Knowledge-based Visual Question Answering (KB-VQA), the intricacy of the task lies in adeptly retrieving pertinent information from external sources and seamlessly aligning and amalgamating multimodal features. While numerous studies have effectively leveraged external knowledge to enrich factual connections among entities, there exists a tendency to overlook the significant reservoir of implicit information inherent in the visual-textual dimension. This oversight often results in suboptimal alignment and an undue reliance on the knowledge base. To address these challenges, this article introduces a novel strategy called SCAG. This approach aggregates the semantic co-occurring attention from diverse regions within images and various tokens within textual inputs using guidance weights to construct joint probabilistic representations grounded in the visual and textual dimensions, respectively. By employing this alignment strategy, the goal is to substantially mitigate information loss, reinforce inter-feature constraints within the model, reduce reliance on external knowledge sources, and enhance self-reasoning capabilities. The efficacy of our proposed model is comprehensively evaluated on the VQAv2 and OK-VQA datasets, with comparative analyses against multiple models conducted on the Ambiguous Knowledge (AK) dataset. Notably, our model exhibits a noteworthy 4.62% improvement over the state-of-the-art in addressing the knowledge dependency problem.

With the benefit of virtualization technology and an ascending trend towards more cloud applications, virtual cluster (VC) allocation concerning different requirements of application deployment is vital to cloud datacenters and edge networks. Most of the state-of-the-art approaches for VC allocation problems are probability-based heuristics, and none of them absolutely outperforms any other approach in any case. Thus, it is necessary to collect and select effective and efficient approaches to jointly solve diverse and customizable VC allocation problems. This paper presents VCA-Solver, a VC allocation solver that can draw on collectively feasible schemes from multiple effective approaches. We describe the architecture of VCA-Solver and present a preliminary evaluation with a prototype implementation.

Mobile edge networks enhance cloud applications by offering reduced delay for mobile terminals. However, due to the cumulative increase of mobile terminals and applications, edge networks fail to accommodate all applications and are difficult to guarantee the end-to-end delay demand, which is an important metric of QoS. One potential solution to address this challenge is to place virtual clusters (VCs) in mobile micro-clouds (MMCs) to enhance the capabilities of edge networks and the cloud. Therefore, in this paper, we build the optimization model of VC placement in edges for maximizing the benefit function, with constraints of 1). the satisfaction for end-to-end delay demand of each application; 2). the resource capacity of each MMC no more than the whole demands of those VCs placed in. We use Stochastic Network Calculus (SNC) to build the delay model more accurately, considering stochastic arrivals of flows and stochastic services of service nodes. Moreover, we use Martingale Theory to enhance the solvability of the SNC-based delay model, achieving tighter delay bound reliability.

In this paper, we propose a system framework of Pub/Sub-based multimodal data transmission to mobile terminals. For various sensors, we classify the data in different topics, but transmit them in the uniform data channel, with different transmission mechanisms. In this case, mobile terminals receive data via different mechanisms, the push notification and request-response connection. The proposed framework makes the data transmission more efficient and flexible.

Network Function Virtualization (NFV) is a promising paradigm that separates network functions from proprietary devices. Network service in NFV-enabled networks is achieved as a Service Function Chain (SFC), consisting of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within dynamic networks is a key challenge. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the concurrent migration of VNFIs based on the actual network situation. We first formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected services and to guarantee network load balancing after the migration simultaneously. To this end, we prove the NP-hardness of this problem and propose the Improved Hybrid Genetic Evolution (IHGE) algorithm to address it. Besides, to reduce the computation overhead of IHGE for large-scale networks, a multi-stage heuristic algorithm based on optimal order (MSH-OR) is designed. Finally, we perform a side-by-side comparison with prior algorithms. Extensive evaluation shows that the proposed approaches can effectively reduce the average delay for different scale networks while ensuring network load balancing.

Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

Nowadays, network softwareization is an emerging techno-economic transformation trend that significantly impacts how enterprises deploy their network services. As an essential technology in this trend, Network Function Virtualization (NFV) enables scalable and inexpensive network services by flexibly instantiating Virtualized Network Functions (VNFs) on commercial-off-the-shelf devices. In this paper, we focus on the VNF placement problem in NFV-enabled networks, aiming to maximize the number of accepted Service Function Chain Requests (SFCRs) while guaranteeing their delay requirements. To improve resource utilization efficiency, we take account of Fundamental Resource Overheads (FROs) and the shareability of VNF instances. We mathematically formulate the VNF placement problem and propose the Throughput Optimization and Delay Guarantee (TO-DG) heuristic solution, consisting of an affinity-based SFCR mapping algorithm and a VNF request adjustment algorithm. The evaluation results show that the performance of TO-DG is near to results derived by ILP solver for small scale problems. Moreover, TO-DG obtains higher network throughput than contrasting schemes in different scenarios and significantly improves network resource utilization.

Network Function Virtualization (NFV) enables cloud service providers (CSPs) to flexibly place their network functions on common off-the-shelf servers in the form of virtual network functions (VNF), without incurring costly capital and operating expenses (CAPEX/OPEX). In the NFV-enabled network, service function chains (SFCs) are responsible for accomplishing users' service requests by steering traffic through a set of VNFs in a specified order. Therefore, it is an important but intractable issue for CSPs to devise an optimal VNF placement scheme to enhance network performance and profits. In this paper, we focus on the VNF placement problem for mapping SFC requests (SFCRs) in NFV-enabled networks, considering the delay requirement of SFCRs. To improve resource utilization, we consider the basic resource overheads and sharability of VNF instances. Then we formulate the problem as an integer linear programming (ILP) model, with the purpose of total resource consumption minimization. Afterward, the novel SFCR mapping algorithm (SMA) and VNF request adjustment algorithm (VAA) are proposed to map SFCRs and optimize the placement of VNF requests. Simulation results show that our approach is near-optimal in terms of node resource consumption. Besides, it provides higher performance in terms of node resource consumption, average VNF utilization and the number of activated servers compared with the benchmarks.

Network function virtualization (NFV) and mobile edge computing (MEC) enable internet service providers (ISPs) to deploy service function chains (SFCs) to achieve the convenience and performance benefit without incurring high service delay, capital expenditures, and operating expenses. In MEC-NFV networks, network services are deployed in the form of service function chains (SFCs), each consisting of an ordered set of virtual network functions (VNFs). In this paper, we focus on the VNF placement problem in MEC-NFV enabled networks, aiming to optimize the throughput of SFC requests (SFCRs). First, we involve the sharing mechanism of VNF instances in the problem formulations, which can improve network resource utilization and save more node resources. Then we formulate the problem mathematically and propose a correlation-based mapping algorithm to map SFCRs in the network. Moreover, we design an adjustment algorithm to optimize the mapped SFCRs. Evaluation results show that our proposed solution efficiently improves the throughput of SFCRs compared with the benchmarks.

With greater numbers of cloud applications being deployed in virtual clusters (VCs) and running in datacenters, the energy consumption of datacenters is experiencing rapidly cumulative growth. Thus, it is a critical issue that how to allocate virtual machines (VMs) of the VC in physical machines (PMs) for energy savings as much as possible. Considering each PM/switch has a certain failure rate, VMs may not be executed when they meet with any PM/switch fault. So, a compact allocation scheme may benefit in low energy consumption but increase the risk of violating the availability of the VC. In this paper, we consider four optimization objectives about the VC and the datacenter, i.e., availability, energy consumption, average resource utilization, and resource load balance. Then we propose a multi-objective optimization model and raise an evolution algorithm to trade-off among these four optimization objectives. Finally, experimental results show the effectiveness and efficiency of our algorithm.

As more and more services are deployed in the cloud datacenter, network traffic is growing exponentially. Virtual machines (VMs) of a virtual cluster (VC) must be allocated on physical machines (PMs) in the datacenter with a certain topology. Each VM needs some resources to run various services. Apparently, allocating VMs in the datacenter as compactly as possible can reduce traffic consumption and avoid bandwidth-related bottlenecks. However, loose allocation scheme can reduce the loss expectation of VMs due to failure possibilities of PMs and switches, and the availability of the VC is increased thereby. To enhance availability and reduce network bandwidth usage, it is significant to determine the scheme of allocating VMs of the VC. In this paper, we first introduce four typical datacenter architectures with network topologies and corresponding cost matrices, and extend to generality. Then we propose a joint optimization function to measure the risk of VC and the core bandwidth usage with a global availability constraint. Subsequently, an evolutionary algorithm is raised to minimize the value of the constrained optimization function. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

With the increasing scale of cloud datacenters and rapid development of virtualization technologies, many cloud-based services have been deployed to meet requirements. Virtual machines (VMs) are placed on physical servers, and often provide virtual environment for cloud services. Therefore, virtual machines placement (VMP) problem has gradually attracted many attentions. It is meaningful that how to effectively and efficiently place VMs on servers to guarantee the service reliability and reduce the bandwidth consumption. In this paper, we first formulate VMP with a reliability model and a bandwidth consumption model, and analyse its complexity. Then we propose a VMP optimization approach to solve the problem and prove its effectiveness and efficiency. The core algorithm of our approach is an approximation algorithm to get VM partitions under the constraint of a specified reliability parameter. Then placement problem is transformed into matching problem between VM partitions with physical servers. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

Network Function Virtualization (NFV) brings great flexibility and scalability to the deployment of network services by decoupling network functions from dedicated devices, which has attracted more attention from both academia and industry. Network services in NFV are deployed in the form of Service Function Chain (SFC), which consists of multiple ordered Virtual Network Functions (VNFs). However, how to effectively place VNFs remains a problem to be solved. In this paper, we investigate joint correlation-aware VNF selection and placement problem. We first formulate the problem as an Integer Linear Programming (ILP) problem and propose a method based on self-learning matrix to partition VNF correlation. Then, we design a Joint Correlation-aware VNF Placement (JCVP) algorithm based on Dynamic Programming to transform the problem into several VNF mapping subproblems. Extensive simulation results show that compared with the previous algorithms our approach has better performance in link occupancy, SFC acceptance, and VNF utilization rate.

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges. A critical but difficult issue for the service and network providers is deciding where to instantiate a list of virtual network functions (VNFs), namely VNF placement problem. In this paper, we investigate the VNF placement, for the purpose of resource and network traffic consumption minimization. Moreover, we consider the arrival rates of users' requests for different types of service function chains (SFCs). This allows the placement scheme to adapt to users' time-varying requests and improve the network resource utilization. Then we formulate the VNF placement problem as a jointly constrained optimization problem. Afterwards, we propose an approach called joint optimization resource and traffic consumption (JORTC) with enhanced biogeography-based (EBBO) optimization algorithm to resolve the VNF placement problem. Finally, the evaluation results show the effectiveness of J-ORTC approach and performance advancement over the benchmarks.

As greater numbers of distributed cloud applications are required to deploy in virtual clusters (VCs) in the datacenter, the VC allocation problem is facing various QoS requirements and different resource optimizations. In this paper, we propose an SOA-based Optimizer, name as VCA-optimizer, for solving customizable VC allocation problems with different constraint conditions and different optimization objectives in the datacenter.

Mobile edge networks benefit cloud applications in particularly providing a shorter response latency for mobile terminals. However, the cumulative increase of mobile terminals and emerging cloud applications, poses new challenges for edge networks. To combat this issue, the promising idea of mobile micro-clouds (MMCs) is proposed to enhance edges and the cloud. In this paper, we investigate the problem of virtual cluster (VC) placement in MMCs, to minimize the average response latency with various requests among multiple cloud applications. Then a hybrid swarm intelligence approach is proposed to optimize VC placement scheme, for a trade-off between the average response latency and the overall VC placement cost. The preliminary evaluation results show the effectiveness and efficiency of our approach.

With the emergency of IoT, there are many IoT network slices with different network requirements. Most of the current IoT system are specific and non-programmable and therefore their slices are difficult to reuse. It is difficult to meet different QoS requirements especially in IoT system because there are plenty of IoT sensors in IoT system. In this paper, we propose a novel IoT network slicing creation system which based on two emerging SDN and NFV technologies. It provides an easily-operating service creation environment and a service execution environment based on micro service architecture. We implement an IoT muti-flow transmission scenario. After adding subservices and QoS policies into a business process at the design plane, the IoT scenario can run automatically at the execution plane. Experiment results on the scenario show that the numbers of packets per second of different flows are changing gradually depend on QoS policies.