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.

Satellite communication has shown great potential for providing ubiquitous connectivity and broadband mobile communications, owing to its advantages of wide coverage, large system capacity, and high transmission rate. However, the increasing complexity of the electromagnetic scenarios poses an unprecedented threat to the security and reliability of satellite covert communication. In this paper, a dynamic spectrum control (DSC) transmission scheme for enhancing the covertness of satellite communications is proposed. The scheme generates a sequence family based on chaotic mapping, controls data decisions and switching processing. With the assistance of this sequence family, the authorized satellite user can unpredictably and dynamically occupy frequency slots for transmitting information. Then, the closed-form expressions of Bit Error Rate (BER) and detection probability is derived. Numerical results demonstrate that the proposed scheme outperforms the benchmark scheme in terms of the overall system performance. Besides, the influences of key parameters in the proposed scheme on the covertness of the system are further analyzed.

Data Center Network (DCN) flexibility is critical for providing adaptive and dynamic bandwidth while optimizing network resources to manage variable traffic patterns generated by heterogeneous applications. To provide flexible bandwidth, this work proposes a machine learning approach with a new Scalable and Reconfigurable Optical DCN (SROdcn) architecture that maintains dynamic and non-uniform network traffic according to the scale of the high-performance optical interconnected DCN. Our main device is the Fiber Optical Switch (FOS), which offers competitive wavelength resolution. We propose a new top-of-rack (ToR) switch that utilizes Wavelength Selective Switches (WSS) to investigate Software-Defined Networking (SDN) with machine learning-enabled flow prediction for reconfigurable optical Data Center Networks (DCNs). Our architecture provides highly scalable and flexible bandwidth allocation. Results from Mininet experimental simulations demonstrate that under the management of an SDN controller, machine learning traffic flow prediction and graph connectivity allow each optical bandwidth to be automatically reconfigured according to variable traffic patterns. The average server-to-server packet delay performance of the reconfigurable SROdcn improves by 42.33% compared to inflexible interconnects. Furthermore, the network performance of flexible SROdcn servers shows up to a 49.67% latency improvement over the Passive Optical Data Center Architecture (PODCA), a 16.87% latency improvement over the optical OPSquare DCN, and up to a 71.13% latency improvement over the fat-tree network. Additionally, our optimized Unsupervised Machine Learning (ML-UnS) method for SROdcn outperforms Supervised Machine Learning (ML-S) and Deep Learning (DL).

Satellite networks have long been regarded as a vital component of space communication systems, which provide integrated satellite-terrestrial broadband access in seamless coverage and cost-effective manner. The inter-satellite routing design for low earth orbit (LEO) satellite constellations is critical for achieving low-latency and high-reliability communication in the space communication systems. However, the inherent dynamic nature of LEO satellites, coupled with the variability in inter-satellite connectivity, imposes significant challenges for routing efficiency and network dependability. Existing routing schemes cannot handle such topological fluctuations due to their insensitivity to real-time network changes, thus suffering from performance degradations in highly dynamic space environments. This paper presents Iris, an intelligent reliable routing scheme for inter-satellite communication, aiming at increasing efficiency and reliability of the packet transmission process. Specifically, we propose a comprehensive deep reinforcement learning (DRL) framework that learns a policy to select routing paths automatically under the emerging software-defined satellite networking (SDSN) architecture. To strengthen fault-tolerance in fluctuating environments, we train an agent in an incremental manner by gradually increasing scenario complexity. Simulation results indicate that our solution significantly outperforms baselines and exhibits advances in adaptability and reliability, especially under dynamic environments with frequent topology changes.

Recent efforts have explored adaptive routing via deep reinforcement learning (DRL) techniques without handcrafted parameter engineering. Intrinsically, routing decision-making is essentially a process used to find a subgraph in a graph-structured network. However, previous works seldom took topological relationships into consideration when providing adaptive routing algorithms, causing them to suffer from suboptimal routes in dynamic network environments involving both varying traffic loads and burst traffic. In this paper, we presentGrace, a novel graph embedding-based Deep Reinforcement Learning framework tailored for distributed routing algorithm optimization within the Software-Defined Networking (SDN) paradigm. Specifically,Graceleverages graph embedding to translate graph-structured entities into low-dimensional vectors, thereby enabling multiple DRL agents to learn optimal routing paths under dynamic network environments. Unfortunately, training multiple agents encounters inherent challenges in complicated and dynamic network scenarios. In response, we design an adaptive incremental training method forGracethat makes the model adapt to task complexity in a gradual manner, while speeding up its retraining efforts when environments change. To further accelerate convergence, we integrate intrinsic curiosity intoGraceto tackle large environments with sparse rewards. Extensive experiments conducted on two real-world topologies demonstrate the rationality and effectiveness ofGrace, and the results show throughput improvements of up to 40.1% compared to other state-of-the-art DRL routing algorithms under bursty traffic conditions.

The explosive growth of cloud services has led to the widespread construction of large-scale data centers to meet diverse and multifaceted cloud computing demands. However, this expansion has resulted in substantial energy consumption. Virtual machine placement (VMP) has been extensively studied as a means to provide flexible and scalable cloud services while optimizing energy efficiency. Yet, the increasing complexity and diversity of applications have posted VMP suffering from waste of resources and bottlenecks due to unbalanced utilization of multi-dimensional resources. To address these issues, this article proposes a bi-objective optimization model for VMP that jointly optimizes power consumption and multi-dimensional resource utilization. Solving this large-scale bi-objective model presents a significant challenge in balancing performance and computational complexity. To tackle this, an enhanced decomposition-based multi-objective evolutionary algorithm (MOEA/D) based on$\varepsilon$-domination, termed$\varepsilon$-IMOEA/D-M2M is designed to provide solutions for the proposed optimization. Compared with both heuristics and evolutionary algorithms, performance evaluations demonstrate that our proposed VMP algorithm effectively reduces power consumption and balances multidimensional resource utilization while significantly decreasing running time compared to both heuristic and traditional evolutionary algorithms.

With the increasing scale of satellite networks, the cost of global traffic measurement is considerable. Estimating global network traffic data from partial traffic measurements becomes a promising solution. Most of existing methods have limitations in capturing high-dimensional spatio-temporal features and understanding highly dynamic network topology, which may lead to estimation errors. This paper proposes TopoTen, a topology-driven method for accurate traffic data estimation in Low Earth Orbit (LEO) mega-constellation networks. In TopoTen, SatRank module is created to encode input tensor in order to extract inherent topological features. While we introduce graph embedding block to process complex graph structural information. Additionally, we design a novel adaptive sparse spatio-temporal attention mechanism that enhances the model’s sensitivity to details and patterns by paying more attention to specific local regions of the input tensor. This mechanism improves TopoTen’s capacity to capture nonlinear spatio-temporal features. Experiments on small, medium, and large satellite network datasets show that TopoTen significantly outperforms baseline approaches, especially for large-scale Starlink dataset. It offers notable advantages in error reduction, especially for low sampling rates compared to mathematical baselines or for large-scale datasets compared to neural network based baselines.

The emerging space-terrestrial integrated networks (STIN) are envisioned to provide seamless connectivity for global coverage. As a promising key component for STIN, the Low Earth Orbiting (LEO) satellite network still faces significant technological challenges in routing for ensuring both reachability and efficiency, due to frequent topology changes and uneven load distribution. Load-balancing routing is a feasible solution to improve efficiency by alleviating regional overload, however, it may encounter either slow convergences or local perceptions. In this paper, we propose a Neighbor Load Rank (NLR) based load-balancing routing for LEO satellite networks, where potential congestion at the queue buffer of a satellite node is characterized by load scores of its neighboring satellites, so as to reduce the perspective limitation of local load-balancing routing. To accelerate routing convergence and reduce computational complexity, we design region delineation, boundary penalty and directional incentive strategies to obtain the approximate minimum hop count path. Meanwhile, we employ the topology-stabilizing model (TSM) to convert the frequent satellite-ground interconnection changes into traffic fluctuations. Simulations demonstrate that NLR can maintain low-latency capacity with lower transmission overhead and effectively balance the overloaded traffic.

Low Earth Orbit (LEO) satellites are gradually developing towards to a large scale, in order to achieve a desired vision of ubiquitous connectivity and broadband access at anytime and anywhere. However, the increasing scale and highly dynamic nature of LEO constellation pose challenges to network management on its flexibility and scalability. Clustering is introduced as an effective approach to manage LEO satellite networks in a flexible manner. Unfortunately, satellite clusters encounter instability and high communication load due to frequent topology changes and traffic growth. In this paper, we design LEO satellites clustering models that jointly optimize cluster reliability and network communication load in the GEO/LEO network architecture. The coalition game framework is introduced to obtain a stable cluster structure by adopting an automated and centralized approach. A coalition formation algorithm based on the optimization of reliability and communication load is developed for the clustering problem. Finally, numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed grouping and clustering scheme.

Traffic classification is a fundamental tool for network management, measurement and security. As the new services with diversified QoS requirements are evolving, traffic classification plays a more significant role in ensuring end-to-end performance guarantees. Several efforts have introduced deep learning (DL) to train traffic classifiers without manual features, however, these classifiers depend heavily on numerous samples and their quality. Indeed, it is unrealistic to obtain sufficient and representative samples in the underlying real network. In this paper, we propose a highly accurate traffic classification model by an Ensemble Learning framework, where Convolutional Recurrent Neural Networks are integrated into the Bagging to train the classifier using only small-scale datasets. Specifically, ensemble learning employs a combinatorial design of three base models to build more accurate and robust learning models so that absorbing features opens up the possibility of training classifiers on small sample sets. We conduct comprehensive experiments with a real-world dataset encompassing 20 applications. Extensive experiment results demonstrate that even with a mere 10% of training samples, our proposed model attains a classification accuracy of 93.94% and a classification precision of 94.33%, outperforming multiple other cutting-edge methods.

The operations and maintenance of satellite networks heavily depend on traffic measurements. Due to the large-scale and highly dynamic nature of satellite networks, global measurement encounters significant challenges in terms of complexity and overhead. Estimating global network traffic data from partial traffic measurements is a promising solution. However, the majority of current estimation methods concentrate on low-rank linear decomposition, which is unable to accurately estimate. The reason lies in its inability to capture the intricate nonlinear spatio-temporal relationship found in large-scale, highly dynamic traffic data. This paper proposes Satformer, an accurate and robust method for estimating traffic data in satellite networks. In Satformer, we innovatively incorporate an adaptive sparse spatio-temporal attention mechanism. In the mechanism, more attention is paid to specific local regions of the input tensor to improve the model's sensitivity on details and patterns. This method enhances its capability to capture nonlinear spatio-temporal relationships. Experiments on small, medium, and large-scale satellite networks datasets demonstrate that Satformer outperforms mathematical and neural baseline methods notably. It provides substantial improvements in reducing errors and maintaining robustness, especially for larger networks. The approach shows promise for deployment in actual systems.

Flowlet switching has been proven to be an effective technology for fine-grained load balancing in data center networks. However, flowlet detection based on static flowlet timeout values, lacks accuracy and effectiveness in complex network environments. In this paper, we propose a new deep reinforcement learning approach, called DRLet, to dynamically detect flowlets. DRLet offers two advantages: first, it provides dynamic flowlet timeout values to detect bursts into fine-grained flowlets; second, flowlet timeout values are automatically configured by the deep reinforcement learning agent, which only requires simple and measurable network states, instead of any prior knowledge, to achieve the pre-defined goal. With our approach, the flowlet timeout value dynamically matches the network load scenario, ensuring the accuracy and effectiveness of flowlet detection while suppressing packet reordering. Our results show that DRLet achieves superior performance compared to existing schemes based on static flowlet timeout values in both baseline and asymmetric topologies.

Fine granularity can greatly enhance load balancing opportunities, but packet reordering is still a challenge. In this paper, we propose EDFLet, a flowlet switching mechanism that uses deadlines to achieve in-order flowlet-level load balancing. We assign a deadline value to each packet of a flowlet based on its burst interval, which ensures that an earlier flowlet completes transmission before the next flowlet of the same flow. We also apply Earliest Deadline First scheduling at the switch, which guarantees that packets that packets meet their deadlines and arrive in order at the receiver. Our experimental results show that EDFLet performs better than existing methods in both symmetric and asymmetric topologies.

The low earth orbit (LEO) satellite constellation is regarded as an effective complement to the terrestrial communication system due to its seamless coverage and ultra-low latency. Unfortunately, the highly dynamic traffic volume, as well as the inherent nature of dynamic topology changes caused by frequent link handover and uncertain hardware failures, pose severe challenges in the design of reliable routing. However, most existing reliable routing approaches with distributed schemes only focus on information exchange between adjacent nodes, which makes them fail to perceive real-time global network changes and make optimal decisions. In this paper, we propose a software defined networking (SDN) based intelligent satellite routing (SISR) method to increase the adaptivity and reliability during the packet transmission process. With the facilitation of SDN, we manage the network in a hierarchical and centralized paradigm, and further implement a more refined form of reinforcement learning (RL) to enhance the fault-tolerant ability of satellite network routing. Experimental results show that our solution can reduce latency and packet loss ratio by more than 42% and 29% compared to baselines.

Load balancing in data center networks is crucial to effectively utilize network resources and enhance Quality of Service (QoS). Especially, the flowlet-level load balancing has been proven efficient in reducing latency and increasing throughput simultaneously. However, most existing work relying on empirical static timeout encounters performance degradation in dynamic network scenarios, due to a mismatch between the static timeout and changing traffic conditions. To address this problem, we propose a Deep Reinforcement Learning-Based Traffic-Aware Load Balancing scheme (DRL-TAL), which uses deep reinforcement learning (DRL) to update the flowlet timeout adaptively. The agent using a deep deterministic policy gradient (DDPG) algorithm continuously senses network throughput and generates the timeout threshold dynamically for the next time slot. The flowlet granularity is deployed for elephant flows to achieve a balance between throughput and disorder, where the timeout value relies on the threshold generated by the agent. Furthermore, the mice flow gets forwarded under packet granularity by selecting the port with the smallest queue length to ensure a shorter flow completion time. The results demonstrate that DRL-TAL performs impressively well in the symmetric topology, with no packet loss and minimal disorder under high load compared to the state-of-the-art schemes. Moreover, it significantly reduces flow completion time by up to 45% compared to Conga in the asymmetric topology.

Forwarding path selection for data traffic is one of the most fundamental operations in computer networks, whose performance drastically impacts both transmission efficiency and reliability in network domains. Although deep reinforcement learning (DRL) has attracted considerable attention for path selection instead of hand-tuned heuristics, few works have considered how to exploit graph-structured information in networks to improve routing and forwarding efficiency. In fact, generating routes is essentially a process for finding a subgraph in a graph-structured network. To this end, this paper proposes an effective and novel graph embedding-based DRL framework for adaptive path selection (termed GRL-PS), aiming at reducing end-to-end (E2E) latency and promoting network throughput while maintaining stability in dynamically changing environments. Specifically, graph representation learning (GRL) is deployed as an effective enabler for the DRL agent to learn the relational knowledge of interacting entities for route decisions in networks. However, training such an agent in a dynamically changing environment encounters a knowledge acquisition bottleneck, since the DRL agent is always forced to learn every task from scratch. To improve the adaptation of behaviors and acquire skills beyond what the source policy can teach, we introduce potential-based reward shaping as a means of knowledge transfer to guide the agent in unfamiliar conditions with sparse rewards. Experimental results show that compared with baseline methods, our solution can achieve nearly-optimal performance with both latency and throughput, especially in large-scale dynamic networks.

With the development of edge-cloud computing technologies, distributed data centers (DCs) have been extensively deployed across the global Internet. Since different users/applications have heterogeneous requirements on specific types of ICT resources in distributed DCs, how to optimize such heterogeneous resources under dynamic and even uncertain environments becomes a challenging issue. Traditional approaches are not able to provide effective solutions for multi-dimensional resource allocation that involves the balanced utilization across different resource types in distributed DC environments. This paper presents a reinforcement learning based approach for multi-dimensional resource allocation (termed as NESRL-MRM) that is able to achieve balanced utilization and availability of resources in dynamic environments. To train NESRL-MRM’s agent with sufficiently quick wall-clock time but without the loss of exploration diversity in the search space, a natural evolution strategy (NES) is employed to approximate the gradient of the reward function. To realistically evaluate the performance of NESRL-MRM, our simulation evaluations are based on real-world workload traces from Amazon EC2 and Google datacenters. Our results show that NESRL-MRM is able to achieve significant improvement over the existing approaches in balancing the utilization of multi-dimensional DC resources, which leads to substantially reduced blocking probability of future incoming workload demands.

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider’s and users’ perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider’s cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

No abstract available.

Due to its excellent ability in learning features from large scale raw data, deep learning (DL) has attracted much attention for encrypted traffic classification. However, most DL-based traffic classifiers usually rely on enormous labeled samples. Motivated by this, we investigate a self-supervised traffic classifier (FLAG) without sacrifice of identification accuracy, only depending on small labeled traffic samples and highly available unlabeled traffic samples. Specifically, focusing on local short-term characteristics of traffic, we design a preprocessing algorithm, termed as N-phrase Extration, to convert unlabeled raw traffic dataset into sequences of high-frequency phrases as input of Bidirectional Encoder. On account of their significance, potential timing characteristics from input sequences are mined by Bidirectional Encoder and embedded into robust representations with distributed vectors to enhance classifier’s performance significantly. Our comprehensive experiments indicate FLAG can achieve 98.65% in 100% of dataset and 98.07% in 10% of dataset in terms of true positive rate in UNB ISCX VPN-nonVPN dataset, which are better than p-FP, FS-Net and Deep Packet.

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.

Network virtualization facilitates the deployment of diversified services and flexible resource management in elastic optical networks (EONs). However, due to the explosive growth of traffic, considerable energy consumption and spectrum usage have restricted the sustainable development of cloud services. This paper addresses joint energy and spectrum efficient problem for virtual optical network embedding (VONE) over EONs. We propose a heuristic algorithm to improve energy and spectrum efficiency while keeping a high acceptance rate. With consideration of factors influencing energy and spectrum efficiency, a feasible shortest path is preferred; meanwhile, an appropriate modulation format is dynamically selected according to transmission distance and the trade-off between energy and spectrum consumption. To improve the acceptance rate of virtual network requests, a dual mapping is employed to reinforce the embedding process by multi-dimensional resources integrated mapping. The simulation results show that the proposed algorithm can achieve a joint energy and spectrum efficiency with a much lower blocking probability compared with the baseline approach.

The servers in the data center networks have multi-dimensional physical resources, and there is a lot of diversity in resource consumption among tasks. When virtual machines carrying different user requests are deployed on the same server at the same time, it is very likely that there is an imbalanced usage of multi-dimensional resources, resulting in the waste of physical resources. In this paper, we focus on virtual machine placement in data centers aiming to balance multi-dimensional resource usage and maximize the service rate. To solve such a bi-objective optimization problem, we present a joint bin-packing heuristic and genetic algorithm to reduce the time complexity while obtaining an approximate optimal solution.

With the boom of Internet of Things (IoT), an increasing amount of data from IoT applications is moved to geo-distributed data centers (DCs) for data analysis. Massive compute-demanding applications call for a more flexible and efficient resource allocation for uncertain and heterogeneous traffic in geo-distributed multi-DC systems. Virtual network embedding, a major part of network virtualization, facilitates to provide different kinds of businesses or services by resource sharing. Moreover, due to their elasticity, elastic optical networks are viewed as a very promising solution to support inter-DC networks. This paper focuses on the effectiveness and spectrum fragmentation problem for virtual optical network embedding in elastic optical inter-DC networks by employing multidimensional resources and a topological attribute. In the node mapping, betweenness of a physical node is considered together with multidimensional resource carrying capacity (MRCC) to identify proper matching. Specifically, to reduce the influence of a spectrum fragment, the available spectrum continuity degree is coupled with the computing capacity of a physical node as the MRCC. In the link mapping, a tightest-matching factor is employed for the selection of paths to accommodate virtual links. Compared with baseline algorithms except for the integer linear programming (ILP) solution, analytical and numerous experiments show that our solution reduces the blocking probability by 30% on average, balances the load by 15% on average and improves spectral efficiency significantly. Moreover, our proposal has a slightly lower spectral efficiency but a better blocking performance and a much better link load balance than that of the ILP formulation.