Segment Routing over IPv6 (SRv6) gives operators explicit path control and alleviates network congestion, making it a compelling technique for traffic engineering (TE). Yet two practical hurdles slow adoption. First, a one-shot upgrade of every traditional device is prohibitively expensive, so operators must prioritize which devices to upgrade. Second, the Segment Routing Header (SRH) increases packet size; if TE algorithms ignore this overhead, they will underestimate link load and may cause congestion in practice. We address both challenges with DRL-TE, an algorithm that couples deep reinforcement learning (DRL) with a lightweight local search (LS) step to minimize the network’s maximum link utilization (MLU). DRL-TE first identifies the smallest set of critical devices whose upgrade yields the largest drop in MLU, enabling hybrid IP/SRv6 networks to approach optimal performance with minimal investment. It then computes SRH-aware routes, and the DRL agent, augmented by a fast LS refinement, rapidly reduces MLU even under traffic variation. Experiments on an 11-node hardware testbed and three larger simulated topologies show that upgrading about 30% of devices allows DRL-TE to match fully upgraded networks and reduce MLU by up to 34% compared with existing algorithms. DRL-TE also maintains high performance under link failures and traffic variations, offering a cost-effective and robust path toward incremental SRv6 deployment.

In this article, we propose a constrained optimization approach to portfolio selection by maximizing nine risk-adjusted return metrics, subject to second-order stochastic dominance (SSD) constraints. The SSD constraints ensure that the portfolio returns are no less than an amplified proportion of returns from a benchmark in the sense of SSD. Because the number of SSD constraints is extremely large, the resulting constrained optimization problems are computationally challenging. To reduce computational complexity, we develop an efficient algorithm to solve the problems iteratively by incrementally adding SSD constraints. We experimentally demonstrate the superiority of the proposed approaches to several baselines in terms of out-of-sample performance criteria based on financial data from major world stock markets.

In this article, the classic portfolio selection problem is reformulated as nine convex optimization problems to maximize nine risk-adjusted performance indexes based on nine different risk measures in Markowitz's return-risk framework. The exact convex reformulations facilitate a decision maker to optimize portfolios efficiently by maximizing one of the nine risk-adjusted performance criteria using widely available convex optimization problem solvers, without compromising the portfolio optimality. The superior performances of the proposed approaches to the state-of-the-art methods, in terms of out-of-sample risk-adjusted returns, annualized returns, and portfolio sparsity, are demonstrated through extensive experimentation on 13 datasets from major world stock markets.

Index tracking is a primary passive investment strategy. Many existing methods, such as cardinality-constrained and regularized regressions, need to prespecify parameters to generate sparse portfolios to track indices, which complicates the tracking procedure and may compromise tracking performance. This article addresses index tracking and enhanced index tracking via Bayesian learning and collaborative neurodynamic optimization. Specifically, we formulate a sparse Bayesian regression problem for index tracking. Furthermore, we reformulate the problem for enhanced index tracking by adding constraints based on a second-order stochastic domination rule. To overcome the nonconvexity of the objective function in the formulated problems, we propose a sparse Bayesian regression algorithm based on multiple recurrent neural networks in the collaborative neurodynamic optimization framework. We demonstrate the superiority of the proposed methods to mainstream baselines in terms of predictability, consistency, sparsity, and profitability via experimentation on the data from seven major stock markets.

As a primary passive investment strategy, index tracking replicates the performance of a specific financial market index by minimizing tracking errors. Most existing index tracking methods are developed based on the assumption that all historical data are equally important. As a result, the importance of different historical data may be overlooked. This article addresses index tracking via temporally weighted least-squares regression. The weight for each time period except for the latest one is defined as the reciprocal of the largest absolute residual of the returns between the index currently and all the selected stocks in the subsequent periods. The weight for the latest period is inferred from the weights in the preceding periods via Gaussian process regression. The tracking accuracy and consistency of the proposed approach are demonstrated via experimentation on historical data from seven major stock markets.

As a ubiquitous deployment paradigm, integrating microservice architecture (MSA) into edge networks promises to enhance the flexibility and scalability of services. However, it also presents significant challenges stemming from dispersed node locations and intricate network topologies. In this paper, we have proposed a topology-aware MSA characterized by a three-tier network traffic model encompassing the service, microservices, and edge node layers. This model meticulously characterizes the complex dependencies between edge network topologies and microservices, mapping microservice deployment onto link traffic to accurately estimate communication delay. Building upon this model, we have formulated a weighted sum communication delay optimization problem considering different types of services. Then, a novel topology-aware and individual-adaptive microservices deployment (TAIA-MD) scheme is proposed to solve the problem efficiently, which accurately senses the network topology and incorporates an individual-adaptive mechanism in a genetic algorithm to accelerate the convergence and avoid local optima. Extensive simulations show that, compared to the existing deployment schemes, TAIA-MD improves the communication delay performance by approximately 30% to 60% and effectively enhances the overall network performance. Furthermore, we implement the TAIA-MD scheme on a practical microservice physical platform. The experimental results demonstrate that TAIA-MD achieves superior robustness in withstanding link failures and network fluctuations.

Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance. To address this issue, we propose a novel network-aware service reliability model that effectively captures the correlation between network state changes and reliability. Based on this model, we formulate the microservice placement problem as an integer nonlinear programming problem, aiming to maximize service reliability. Subsequently, a service reliability-aware placement (SRP) algorithm is proposed to solve the problem efficiently. To reduce bandwidth consumption, we further discuss the microservice placement problem with the shared backup path mechanism and propose a placement algorithm based on the SRP algorithm using shared path reliability calculation, known as the SRP-S algorithm. Extensive simulations demonstrate that the SRP algorithm reduces service failures by up to 22% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 64% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 11% compared to the SRP algorithm with the shared backup mechanism.

In network virtualization environments, with random arrival and departure of virtual network requests, there exist some resources (i.e., link resources and node resources) isolated from others in substrate networks. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by avoiding resource fragmentation in substrate networks. First and most importantly, we define a new metric called resource fragmentation degree (RFD) to quantitatively measure the status of resource fragmentation at substrate nodes and links. The basic idea of RFD is that the resource availability of a node (or a link) is determined by the residual link and node resources around the node (or the link). Based on the definition of RFD, we formulate the virtual network embedding (VNE) problem as a mixed integer programming problem with consideration of the cost of resource fragmentation. Then, an online VNE algorithm with consideration of RFD (VNE-RFD) is proposed to solve the problem, which is performed according to the current resource status of substrate networks and virtual network requests. To reduce accumulated fragmented resources produced by dynamic arrival and departure of virtual network requests, a heuristic virtual network reconfiguration algorithm based on RFD (VNR-RFD) is proposed. Simulation results show that VNE-RFD and VNR-RFD can effectively reduce fragmented resources and thus embed more virtual networks into substrate networks.

In network function virtualization enabled networks with dynamic traffic, virtual network function (VNF) migration has been considered as an effective way to improve quality of service as well as resource utilization. However, due to time-varying network traffic, designing a fast and accurate VNF migration algorithm is still a great challenge. To address this issue, in this paper, we exploit the temporal convolutional network (TCN) to predict traffic flow for VNF migration decision in a fast and accurate manner. Based on the predicted results, we define a metric, i.e., migration index, to represent the load trend of each node in the network. A fast and efficient heuristic VNF migration algorithm is then proposed based on the migration index, with the goal to minimize the total migration cost in a time period. Extensive simulations are carried out to validate the effectiveness of TCN for traffic prediction. The results demonstrate that the proposed VNF migration algorithm can reduce the total migration cost up to 20% compared with existing algorithms.

As an essential group in knowledge innovation, researchers are encouraged to exchange ideas with each other for further brainstorm through advanced communication technology. However, efficient online knowledge sharing among researchers is still limited. Although past literature proposes a series of motivators of online knowledge sharing, the differences in the effects of motivators remain in dispute. Thus, it is time to understand how motivators influence each other and inspire scientists to share knowledge and promote virtual communities. Based on the self-determination theory, this study proposes a model with several factors and analyze 301 Chinese researchers' data in an online WeChat cross-disciplinary research community by adopting SmartPls 2.0 and SPSS 22. The results reveal the effects of several antecedents and mediating effects of altruism and knowledge sharing behavior and report the differences of results among different demographic groups. This study enriches the literature in knowledge sharing on social media and proposes further research points to researchers and useful advice to practitioners.

For video streaming, Adaptive BitRate (ABR) algorithms are usually used to improve end-to-end user’s Quality of Experience (QoE). Many of the state-of-the-art ABR algorithms are based on simplified models, leading to conservative predictions of real situations. To optimize the QoE in complex end-to-end transmission environments, ABR algorithms based on Deep Reinforcement Learning (DRL) has shown a great improvement compared to traditional algorithms. However, the slow convergence of existing DRL-based ABR algorithms limits the QoE performance under dynamic video streaming environments. In this paper, we propose Fastconv, a novel DRL-based ABR algorithm that has a fast convergence speed to ensure a satisfactory QoE performance. Our work can be mainly divided into two parts. First, we preprocess the input data with large fluctuation in order to obtain the steady input and reduce the indeterminacy of convergence. Second, in order to reduce the structural complexity of the neural network itself and the number of parameters, we propose a neural network architecture based on multiplexed convolution kernel. Experiment results based on a real traced mobile dataset have demonstrated that Fastconv outperforms both the traditional and DRL-based ABR algorithms in terms of QoE.