Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Mobile edge computing (MEC) has revolutionized the way of teaching in universities. It enables more interactive and immersive experiences in the classroom, enhancing student engagement and learning outcomes. As an incentive mechanism based on social identity and contest theories, team competition has been adopted and shown its effectiveness in improving students’ participation and motivation in college classrooms. However, despite its potential benefit, there are still many unresolved issues: What type of students and teams benefit more from team competition? In what teaching context is team competition more effective? Which competition design methods better increase student academic performance? Mobile edge computing provides the ability to obtain the data of the teaching process and analyze the causal effect between team competition and students’ academic performance. In this paper, the authors first design a randomized field experiment among freshmen enrolled in college English courses. Then, the authors analyze the observation data collected from the online teaching platform, and predict individual treatment effects of academic performance in college English through linear and nonlinear machine learning models. Finally, by carefully investigating features of teams and individual students, the prediction error is reduced by up to 30%. In addition, through interpreting the predictive models, some valuable insights regarding the practice of team competition in college classrooms are discovered.

Deep learning-based semantic search (DLSS) aims to bridge the gap between experts and non-experts in search. Experts can create precise queries due to their prior knowledge, while non-experts struggle with specific terms and concepts, making their queries less precise. Cloud infrastructure offers a practical and scalable platform for data owners to upload their data, making it accessible to intended data users. However, the contemporary single-owner/single-user (S/S) approach to DLSS schemes falls short of effectively leveraging the inherent multi-user capabilities of cloud infrastructure. Furthermore, most of these schemes delegate the dissemination of secret keys to a single trust point within the mutual distrust scenario in cloud infrastructure. This paper proposes a Secure Semantic Search using Deep Learning in a Blockchain-Assisted Multi-User Setting (S3DBMS)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(S^3DBMS)$$\\end{document}. Specifically, the seamless integration of attribute-based encryption with transfer learning allows the construction of DLSS in multi-owner/multi-user (M/M) settings. Further, blockchain’s smart contract mechanism allows a multi-attribute authority consensus-based generation of user private keys and system-wide global parameters in a mutual distrust M/M scenario. Finally, our scheme achieves privacy requirements and offers improved security and accuracy.

In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research methods, such as Bayesian attack graphs, lack evidence correlation and real-time reflection of the network attack events, and high computational complexity for attack analysis. To solve these problems, this study proposes a Dynamic Uncertain Causal Attack Graph (DUCAG) model and a Causal Chain-based Risk Probability Calculation (CCRP) algorithm. The DUCAG model is constructed to represent the uncertain underlying causalities among network attack events, and the CCRP algorithm aims at dynamically updating the causality weights among different network attack events and attacker hypotheses based on alarm information and causal chain reasoning process. By causality simplification and causality reasoning methods, the CCRP efficiently predicts the attacker behaviors and potential attack likelihood under uncertain time-varying attack situations, and is robust to the incompleteness and redundancy in alarm information. Four experiments under different attack scenarios demonstrate that, the DUCAG model can effectively characterize and predict the complex and uncertain attack causalities, in a manner of high time efficiency. The proposed method has application significance on cloud computing platforms by dynamically evaluating network security status, predicting the future behaviors of attackers, and assisting in adjusting network defense strategies.

The emergence of mobile edge computing (MEC) has brought cloud services to nearby edge servers facilitating penetration of real-time and resource-consuming applications from smart mobile devices at a high rate. The problem of task offloading from mobile devices to the edge servers has been addressed in the state-of-the-art works by introducing collaboration among the MEC servers. However, their contributions are either limited by minimization of service latency or cost reduction. In this paper, we address the problem by developing a multi-objective optimization framework that jointly optimizes the latency, energy consumption, and resource usage cost. The formulated problem is proven to be an NP-hard one. Thus, we develop an evolutionary meta-heuristic solution for the offloading problem, namely WOLVERINE, based on a Binary Multi-objective Grey Wolf Optimization algorithm that achieves a feasible solution within polynomial time having computational complexity of O(M3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(M^3)$$\\end{document}, where M is an integer that determines the number of segments in each dimension of the objective space. Our experimental results depict that the developed WOLVERINE system achieves as high as 33.33%, 35%, and 40% performance improvements in terms of execution latency, energy, and resource cost, respectively compared to the state-of-the-art.

Abstract Artificial intelligence and blockchain technology have become indispensable in the era of the digital economy, particularly in the field of financial financing. However, when it comes to supply chain finance (SCF), existing models primarily focus on risk identification and credit evaluation, neglecting the critical aspects of trust transfer continuity and reliability within the chain. To address this issue, this paper proposes a blockchain-based transitive trust model for SCF, which ensures seamless trust transfer from core enterprises to bottom suppliers during financing enterprise credit evaluation. The model utilizes multi-layer metrics to calculate the comprehensive trust value of underlying suppliers, serving as the basis for credit delivery. Additionally, the model stores transitive signature receivable warrants on the blockchain and utilizes splittable delivery of warrants to underlying suppliers. The model’s rationality and correctness are verified through experimental analysis, with results demonstrating that the transitive trust model enhances Small and Medium-Sized Enterprises’ (SMEs) trust at the bottom of the supply chain, thus alleviating financing difficulties for SMEs.

Abstract Research has recently grown on multi-agent systems (MAS) and their coordination and secure cooperative control, for example in the field of edge-cloud computing. MAS offers robustness and flexibility compared to centralized systems by distributing control across decentralized agents, allowing the system to adapt and scale without overhaul. The collective behavior emerging from agent interactions can solve complex tasks beyond individual capabilities. However, controlling high-order nonlinear MAS with unknown dynamics raises challenges. This paper proposes an enhanced genetic algorithm strategy to enhance secure cooperative control performance. An efficient encoding method, adaptive decoding schemes, and heuristic initialization are introduced. These innovations enable compelling exploration of the solution space and accelerate convergence. Individual enhancement via load balancing, communication avoidance, and iterative refinement intensifies local search. Simulations demonstrate superior performance over conventional algorithms for complex control problems with uncertainty. The proposed method promises robust, efficient, and consistent solutions by adapting to find optimal points and exploiting promising areas in the space. This has implications for securely controlling real-world MAS across domains like robotics, power systems, and autonomous vehicles.

Modern data center topologies often take the form of a multi-rooted tree with rich parallel paths to provide high bandwidth. However, various path diversities caused by traffic dynamics, link failures and heterogeneous switching equipment widely exist in the production datacenter network. Therefore, the multi-path load balancer in data center should be robust to these diversities. Although prior fine-grained schemes such as RPS and Presto make the best use of available paths, However, they are prone to experiencing packet reordering problem under the asymmetric topology. The coarse-grained solutions such as ECMP and LetFlow effectively avoid packet reordering, but easily lead to under-utilization of multiple paths. To cope with these inefficiencies, we propose a load balancing mechanism called PDLB, which adaptively adjusts flowcell granularity according to path diversity. PDLB increases flowcell granularity to alleviate packet reordering under large degrees of topology asymmetry, while reducing flowcell granularity to obtain high link utilization under small degrees of topology asymmetry. PDLB is only deployed on the sender without any modification on switch. We evaluate PDLB through large-scale NS2 simulations. The experimental results show that PDLB reduces the average flow completion time by up to ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}8-53% over the state-of-the-art load balancing schemes.

Recently, the development of Low Earth Orbit (LEO) satellites and the advancement of the Mobile Edge Computing (MEC) paradigm have driven the emergence of the Satellite Mobile Edge Computing (Sat-MEC). Sat-MEC has been developed to support communication and task computation for Internet of Things (IoT) Mobile Devices (IMDs) in the absence of terrestrial networks. However, due to the heterogeneity of tasks and Sat-MEC servers, it is still a great challenge to efficiently schedule tasks in Sat-MEC servers. Here, we propose a scheduling algorithm based on the Deep Reinforcement Learning (DRL) method in the Sat-MEC architecture to minimize the average task processing time. We consider multiple factors, including the cooperation between LEO satellites, the concurrency and heterogeneity of tasks, the dynamics of LEO satellites, the heterogeneity of the computational capacity of Sat-MEC servers, and the heterogeneity of the initial queue for task computation. Further, we use the self-attention mechanism to act as a Q-network to extract high-dimensional dynamic information of tasks and Sat-MEC servers. In this work, we model the Sat-MEC environment simulation at the application level and propose a DRL-based task scheduling algorithm. The simulation results confirm the effectiveness of our proposed scheduling algorithm, which reduces the average task processing time by 22.1%, 30.6%, and 41.3%, compared to the genetic algorithm(GA), the greedy algorithm, and the random algorithm, respectively.

With the speedy advancement and accelerated popularization of 5G networks, the provision and request of services through mobile smart terminals have become a hot topic in the development of mobile service computing. In this scenario, an efficient and reasonable edge server deployment solution can effectively reduce the deployment cost and communication latency of mobile smart terminals, while significantly improving investment efficiency and resource utilization. Focusing on the issue of edge server placement in mobile service computing environment, this paper proposes an edge server deployment method based on optimal benefit quantity and genetic algorithm. This method is firstly, based on a channel selection strategy for optimal communication impact benefits, it calculates the quantity of edge servers which can achieve optimal benefit. Then, the issue of edge server deployment is converted to a dual-objective optimization problem under three constraints to find the best locations to deploy edge servers, according to balancing the workload of edge servers and minimizing the communication delay among clients and edge servers. Finally, the genetic algorithm is utilized to iteratively optimize for finding the optimal resolution of edge server deployment. A series of experiments are performed on the Mobile Communication Base Station Data Set of Shanghai Telecom, and the experimental results verify that beneath the limit of the optimal benefit quantity of edge servers, the proposed method outperforms MIP, K-means, ESPHA, Top-K, and Random in terms of effectively reducing communication delays and balancing workloads.