Network Optimization Problem Research Articles

Intelligent Segment Routing: Toward Load Balancing with Limited Control Overheads

Segment routing has been a novel architecture for traffic engineering in recent years. However, segment routing brings control overheads, i.e., additional packets headers should be inserted. The overheads can greatly reduce the forwarding efficiency for a large network, when segment headers become too long. To achieve the best of two targets, we propose the intelligent routing scheme for traffic engineering (IRTE), which can achieve load balancing with limited control overheads. To achieve optimal performance, we first formulate the problem as a mapping problem that maps different flows to key diversion points. Second, we prove the problem is nondeterministic polynomial (NP)-hard by reducing it to a k-dense subgraph problem. To solve this problem, we develop an ant colony optimization algorithm as improved ant colony optimization (IACO), which is widely used in network optimization problems. We also design the load balancing algorithm with diversion routing (LBA-DR), and analyze its theoretical performance. Finally, we evaluate the IRTE in different real-world topologies, and the results show that the IRTE outperforms traditional algorithms, e.g., the maximum bandwidth is 24.6% lower than that of traditional algorithms when evaluating on BellCanada topology.

Dynamical Primal-Dual Nesterov Accelerated Method and Its Application to Network Optimization

This article develops a continuous-time primal-dual accelerated method with an increasing damping coefficient for a class of convex optimization problems with affine equality constraints. This article analyzes critical values for parameters in the proposed method and prove that the rate of convergence in terms of the duality gap function is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\frac{1}{t^2})$</tex-math></inline-formula> by choosing suitable parameters. As far as we know, this is the first continuous-time primal-dual accelerated method that can obtain the optimal rate. Then, this article applies the proposed method to two network optimization problems, a distributed optimization problem with consensus constraints and a distributed extended monotropic optimization problem, and obtains two variant distributed algorithms. Finally, numerical simulations are given to demonstrate the efficacy of the proposed method.

Preface

The 2022 2nd International Conference on Computer Technology, Information Engineering and Electron Materials (CTIEEM 2022) was successfully held in Guangzhou, China from November 25th-27th, 2022 (virtual form). The conference is a leading academic conference and has the potential to bring new horizons and insights to the researchers and practitioners in the fields related to computer technology, information engineering and electron materials.CTIEEM 2022 created an academic platform for academic sharing and scientific innovation, brought together experts, scholars, and engineering and technical personnel from all over the world to present their research results and exchange ideas, promoted the industrial cooperation of academic achievements, and facilitated future business or research contacts among all the participants.The scope of the conference covered all areas of research in computer technology, information engineering and electron materials, including Wireless Sensor Network, Array Signal Processing Technology, Intelligent and Optimal Control System, Electrical Engineering Materials, Magnetic Material, etc. The conference brought together about 50 participants, primarily from China, but also from Malaysia, Korea, and other countries.A three-day program of the conference was divided into three sessions, including keynote speeches, oral presentations, and academic investigation. Each keynote speaker was given 30-40 minutes for keynote speeches and every presenter was given 10-15 minutes. Among them, Prof. Qin Xin from University of the Faroe Islands, Denmark is one of our keynote speakers and performed a wonderful keynote speech for us. His researches mainly focus on the design and analysis of sequential, parallel and distributed algorithms for various communication and optimization problems in wireless communication networks, as well as cryptography and digital currencies including quantum money. Moreover, he also investigates the combinatorial optimization problems with applications in Bioinformatics, Data Mining and Space Research and has produced more than 111 peer reviewed scientific papers, which are mainly published in leading international conferences and journals, such as ICALP, ACM PODC, IEEE MASS, ISAAC, IEEE ICC, etc. Currently, Prof. Qin Xin is serving on Management Committee Board of Denmark for several EU ICT projects. The insightful speeches of each keynote speakers triggered interesting discussions and a good number of informal talks between all the participants.We would like to thank all the participants in the 2022 2nd International Conference on Computer Technology, Information Engineering and Electron Materials. Besides, our special acknowledgement goes to the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. We are sure that the proceedings of CTIEEM 2022 will serve as an important research source for references, which will contribute to not only scientific and engineering progress but also other new products and processes.The Committee of CTIEEM 2022List of Committee member are available in this pdf

A Two-Stage Swarm Optimizer With Local Search for Water Distribution Network Optimization.

Evolutionary computation (EC) algorithms have been successfully applied to the small-scale water distribution network (WDN) optimization problem. However, due to the city expansion, the network scale grows at a fast speed so that the efficacy of many current EC algorithms degrades rapidly. To solve the large-scale WDN optimization problem effectively, a two-stage swarm optimizer with local search (TSOL) is proposed in this article. To address the issues caused by the large-scale and multimodal characteristics of the problem, the proposed algorithm divides the optimization process into an exploration stage and an exploitation stage. It first finds a promising region of the search space in the exploration stage. Then, it searches thoroughly in the promising region to obtain the final solution in the exploitation stage. To search effectively the huge search space, we propose an improved level-based learning optimizer and use it in both the exploration and exploitation stages. Two new local search algorithms are proposed to further improve the quality of the solution. Experiments on both synthetic benchmark networks and a real-world network show that the proposed algorithm has outperformed the state-of-the-art metaheuristic algorithms.

A Review of Emergency Medical Rescue Network Optimization Problems

This paper mainly reviews the research on the optimization of emergency medical treatment network, which mainly includes the location of emergency medical facilities and the transportation of the wounded, as well as the integration of the two, and finds that although many scholars have paid attention to the research of related issues, they mainly focus on the optimization of emergency medical treatment network in the context of major natural disasters such as earthquakes and typhoons, and there are few related studies on the optimization of emergency medical treatment network under infectious health emergencies.

Resource Allocation in Macrocell–Femtocells via Combined Rain and Whale Optimization Algorithm

Femtocell (FC) dense exploitation imposes a novel challenge for the resource allotment in a 2-tier network, like: (1) dynamic resources allotment among the 2 tiers by considering the off-load traffic from macrocell (MC), (2) permitting access to users when ensuring QoS for FC subscribers, and (3) suitable power setting, which discovers cooperation among the overall bandwidth allotted to FCs and system performances. This research work examines the problems existing on cross-tier interfering as well as resource allocation alleviation in “full-duplex (FD) Orthogonal Frequency Division Multiple Access (OFDMA) oriented Heterogeneous Networks (HetNets), which involves macrocell with underlying femtocells”. Here, three primary contributions are concerned: a single objective issue together with power allocation, price allocation & subcarrier allocation of MC-FC networks. In addition, this research establishes a new hybrid algorithm termed Hybrid Whale and Rain Optimization (HW-RO) for solving the optimization problem in MC-FC Networks. Finally, the superiority of the suggested technique is evaluated in comparison to various existing approaches.

Trustworthy Federated Learning via Blockchain

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this article, we shall propose a decentralized blockchain-based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying a practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning (DRL)-based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by the DRL-based algorithm compared with the baseline algorithms.

Scalable Multi-Agent Reinforcement Learning for Dynamic Coordinated Multipoint Clustering

Reinforcement learning (RL) is a widely investigated intelligent algorithm and proved to be useful in the wireless communication area. However, for optimization problems in large-scale multi-cell networks whose dimension increases exponentially, it is unrealistic to employ a conventional centralized RL algorithm and make decisions for the entire network. Multi-agent RL, which allows distribute decision-making, is expected to solve the scalability problem but with performance issues due to the unknown global information, i.e., non-stationary environment. In this paper, we propose a parameter-sharing multi-agent RL for grouping decisions of coordinated multi-point in a large-scale network, where agents jointly serve users to enhance the cell-edge service. By sharing information via parameters, our theoretical and simulation results show that parameter sharing can largely benefit the multi-agent algorithm with convergence proof and convergence speed analysis. To reduce the effect of biased local heterogeneous experience, we also propose a transfer learning method for the parameter sharing process, whose performance of transfer learning algorithms is verified by the simulation results.

Dynamic Channel Assignment for Downlink and Uplink Decoupling in Wireless Networks

The demand for wireless communication capacity continues to increase with the extensive usage of smartphones, tablets, and devices that are related to the Internet of Things (IoT). However, devices and base stations are diversified, and base stations of various sizes are mixed. In existing cellular networks, the transmission powers of the base stations are different. If the downlink received power from the base station that belongs to the device is maximum, the uplink received power from the device at the base station is not always the maximum. This study maximizes the power that is received from the device through downlink-uplink decoupling (DUDe). DUDe can improve the spectral efficiency by selecting the downlink base station and the uplink base station independently in a network with base stations with different transmission powers. This study focuses on two technologies, DUDe and the dynamic channel assignment (DCA). It proposes an association algorithm to solve the dynamic combinational optimization problem for uplink and downlink cellular networks separately using DUDe. When a user device arrives, it first connects to the base station that has the maximum capacity at that time. Subsequently, by using the base station assignment at that time as an individual, the proposed method performs a more optimal base station assignment with DCA by using a genetic algorithm. The computer simulations demonstrate that the proposed method can achieve up to a 140 % higher spectral efficiency than the existing DUDe in the fixed channel assignment (FCA).

Mitigating Trade-Off in Unlicensed Network Optimization Through Machine Learning and Context Awareness

Unlicensed cellular networks are being deployed worldwide by cellular operators to meet the rising data demands. However, the unlicensed band has existing incumbents such as Wi-Fi and radar systems. This creates a highly dynamic environment, making harmonious unlicensed coexistence difficult. Consequently, conventional optimization techniques are not sufficient to offer latency-critical applications and services. A data-driven hybrid optimization approach is necessary for optimal network performance with low convergence times. However, a largely unexplored problem in dense unlicensed network optimization is the accuracy-speed trade-off, that is, achieving high accuracy in optimization objectives with minimal time costs. This work seeks to address this problem through a hybrid optimization approach that combines machine learning and network optimization. It investigates the use of more precise higher-order network feature relationships (NFRs) in optimization formulations and the consequent trade-off that arises between the increase in convergence time (Speed) and the nearness to optimal results (Accuracy). In addition, it demonstrates the relevance of context awareness of network conditions and the traffic environment to mitigate the trade-off. To that end, a context-aware network feature relationship-based optimization (CANEFRO) approach is proposed and validated through decision matrix analysis. The experiments were carried out on a coexistence testbed consisting of both unlicensed LTE standards (LTE-U & LAA) and two Wi-Fi standards (802.11n/ac) on multiple channel bandwidths. In addition, LTE-U & LAA are contrasted on signaling and user data traffic data models and resource block allocation performance. More importantly, CANEFRO demonstrates the impact of the network context on the degree of feature relationship ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{nd}$ </tex-math></inline-formula> & <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3^{rd}$ </tex-math></inline-formula> degree polynomials), objective of optimization (SINR and Capacity), and the network use case (Accuracy vs. Speed). CANEFRO is also used to contrast LTE-U & LAA optimization performance. In particular, the decision matrix analysis demonstrates a higher decision score for LAA by as much as 42% compared to LTE-U.

Deep Learning-Based Autonomous UAV-BSs for NGWNs: Overview and a Novel Architecture

To address the ever-growing connectivity demand in communications, the adoption of ingenious solutions, such as utilization of unmanned aerial vehicles (UAVs) as mobile base stations, is imperative. In general, the location of a UAV base station (UAV-BS) is determined by optimization algorithms, which have high computationally complexities and are hard to run on UAVs due to energy consumption and time constraints. In this article, we overview the UAV-BS trajectory optimization problem for next generation wireless networks and show that a convolutional neural network (CNN) model can be trained to infer the location of a UAV-BS in real time. To this end, we create a framework to determine the UAV-BS locations considering the deployment of mobile users (MUs) to generate labels by using the data obtained from an optimization algorithm. Performance evaluations reveal that once the CNN model is trained with the given labels and locations of MUs, the proposed approach is, indeed, capable of approximating the results given by the adopted optimization algorithm with high fidelity, outperforming reinforcement learning-based approaches in resource-constrained settings. We also explore future research challenges and highlight key issues.

Cost-Aware DU Placement and Flow Routing in 5G Packet xHaul Networks

Packet-switched xHaul networks based on Ethernet technology are considered a promising solution for assuring convergent, cost-effective transport of diverse radio data traffic flows in dense 5G radio access networks (RANs). A challenging optimization problem in such networks is the placement of distributed processing units (DUs), which realize a subset of virtualized baseband processing functions on general-purpose processors at selected processing pool (PP) facilities. The DU placement involves the problem of routing of related fronthaul and midhaul data flows between network nodes. In this work, we focus on developing optimization methods for joint placement of DUs and routing of flows with the goal to minimize the overall cost of PPs activation and processing in the network, which we refer to as the PPC-DUP-FR problem. We account for limited processing and transmission resources as well as for stringent latency requirements of data flows in 5G RAN. The latency constraint makes the problem particularly difficult in a packet-switched xHaul network since it involves the non-linear and dynamic estimation of the latencies caused by buffering of packets in the switches. The latency model that we apply in this work is based on worst-case calculations with improved latency estimations that skip from processing the co-routed, but non-affecting flows. We use a mixed-integer programming (MIP) approach to formulate and solve the PPC-DUP-FR optimization problem. Moreover, we develop a heuristic method that provides optimized solutions to larger PPC-DUP-FR problem instances, which are too complex for the MIP method. Numerical experiments performed in different network scenarios indicate on the effectiveness of the heuristic in solving the PPC-DUP-FR problem. In particular, the heuristic achieves up to 63% better results than MIP (at the MIP optimality gap equal to 76%) in a medium-size mesh network, in which the MIP problem is unsolvable for higher traffic demands within reasonable runtime limits. In larger networks, MIP is able to provide some results only for the PPC-DUP-FR problem instances with very low traffic demands, whereas the solutions generated by the heuristic are at least 83% better than the ones achieved with MIP. Also, the analysis performed shows a significant impact of the PP cost factors considered and of the level of cost differentiation of PP nodes on the overall PP cost in the network. Finally, simulation results of a case-study packet xHaul network confirm the correctness of the latency model used.

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight into network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.

A generic parallel optimization framework for solving hard problems in optical networks

Many optimization problems in optical networks are either NP-hard or take a long time to solve. In this paper, we develop a generic parallel computing framework which either solves these problems completely or at least improves the quality of the solution. To verify the effectiveness of the proposed framework, we build a miniaturized parallel computing system and test our approach based on different heuristic and meta-heuristic algorithms for the bin-packing problems, mixed integer linear programming (MILP) models, and distributed machine learning. Experimental studies show that the proposed parallel computing framework based on the miniaturized parallel computing system is effective in so far as it either significantly reduces the computing time or improves the quality of the solution as compared to when only a single machine is used.

Digital Twin-Based Zero-Touch Management for IoT

The rapid development of the Internet of Things (IoT) requires network automation, to improve management efficiency and reduce manual operations. Zero-touch network is a promising technology for empowering network management automation by creating virtualized networks for software-based solutions. However, the traditional software-defined network (SDN) technology is not suitable for IoT devices, due to its massive, heterogeneous, and distributed characteristics. In this paper, we introduce digital twin technology (DT) into the IoT, and propose a DT modeling method through ontology and knowledge graph technologies, which maps IoT elements in the digital space and provides the advantages of centralized control, device abstraction, and flexible control of management. Then, referring to the conceptual architecture of a zero-touch network, a DT-based zero-touch management framework suitable for IoT is established. Finally, aiming at specific device management and network optimization problems in the IoT, a zero-touch management scheme with digital twin technology as the core and intention as the driver is proposed, and the effectiveness of the proposed method is demonstrated using an example.

Centralized and Collective Neurodynamic Optimization Approaches for Sparse Signal Reconstruction via L₁-Minimization.

This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the L1 -minimization problem. First, two centralized neurodynamic approaches are designed based on the augmented Lagrange method and the Lagrange method with derivative feedback and projection operator. Then, the optimality and global convergence of them are derived. In addition, considering that the collective neurodynamic approaches have the function of information protection and distributed information processing, first, under mild conditions, we transform the L1 -minimization problem into two network optimization problems. Later, two collective neurodynamic approaches based on the above centralized neurodynamic approaches and multiagent consensus theory are proposed to address the obtained network optimization problems. As far as we know, this is the first attempt to use the collective neurodynamic approaches to deal with the L1 -minimization problem in a distributed manner. Finally, several comparative experiments on sparse signal and image reconstruction demonstrate that our proposed centralized and collective neurodynamic approaches are efficient and effective.

End-to-End Service Auction: A General Double Auction Mechanism for Edge Computing Services

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

Multi-agent deep reinforcement learning for online request scheduling in edge cooperation networks

Edge computing as a complementary paradigm of cloud computing has gained more attention by providing mobile users with diversified services at the network edge. However, the increasingly complex mobile applications put a heavier load on edge networks. It is challenging to provide concurrency requests with high-quality service processing, especially when the edge networks are dynamically changing. To address the above issues, this paper investigates the online concurrent user requests scheduling optimization problem in edge cooperation networks. We model it as an online multi-stage decision-making problem, where requests are divided into a group of independent and logically related sub-tasks. We proposed a centralized training distributed execution based multi-agent deep reinforcement learning technique to realize the implicit cooperation scheduling decision-making policy learning among edge nodes. At the centralized training stage of the proposed mechanism, a value-decomposition-based policy learning technique is adopted to improve the long-term system performance, while at the distributed execution stage, only local environment status information is needed for each edge node to make the request scheduling decision. Extensive experiments are conducted, and simulation results demonstrate that the proposed mechanism outperforms other request scheduling mechanisms in reducing the long-term average system delay and energy consumption while improving the throughput rate of the system.

Formulation of Mindfulness States as a Network Optimization Problem and an Attempt to Identify Key Brain Pathways Using Digital Annealer

Formulation of Mindfulness States as a Network Optimization Problem and an Attempt to Identify Key Brain Pathways Using Digital Annealer

A NOMA-Enabled Framework for Relay Deployment and Network Optimization in Double-Layer Airborne Access VANETs

A non-orthogonal multiple access (NOMA)-enabled double-layer airborne access vehicular ad hoc networks (DLAA-VANETs) architecture is designed in this paper, which consists of a high-altitude platform (HAP), multiple unmanned aerial vehicles (UAVs) and vehicles. For the designed DLAA-VANETs, we investigate the UAV deployment and network optimization problems. In particular, a UAV deployment scheme based on particle swarm optimization is presented. Then, the NOMA technique is introduced into the designed architecture, which can improve the transmission rate. Afterward, we take the information security into account and formulate a downlink total transmission rate maximization problem by optimizing UAV height and subcarrier allocation. For tackling this non-convex problem, we decouple this downlink total transmission rate maximization problem as two subproblems, where UAV height and subcarrier allocation problems are solved in turn. Moreover, the transmission performance of the designed DLAA-VANETs is analyzed, based on which the security outage probability (SOP) is derived. Finally, simulation results demonstrate that the presented UAV deployment scheme can maximize the relay coverage ratio. In addition, the proposed can achieve a higher downlink total transmission rate in comparison with the current works.