Signaling storms threaten cellular core networks when synchronized reconnection attempts from massive numbers of devices trigger cascading, metastable overloads. Existing defenses rely on manual, static configurations of local overload controls, which ignore serial dependencies among heterogeneous network elements. We present Spillway, a digital-twin-driven system that automates global signaling-flood mitigation. Spillway introduces a hierarchical defense architecture that enforces altruistic throttling, allowing upstream nodes to shed load before downstream bottlenecks collapse. To evaluate candidate configurations, Spillway uses CN-DES, a domain-specific discrete-event simulator with a vectorized kernel. By aggregating users that share protocol states, CN-DES decouples simulation cost from user count and simulates regional-scale storms involving tens of millions of users in minutes, achieving a 60× speedup over traditional simulation while preserving fidelity. Spillway then uses heteroscedastic evolutionary Bayesian optimization to search a large, non-convex parameter space. We report on a five-year deployment in the world's largest 5G Standalone network. During real incidents, including application anomalies and RAN failures, networks using Spillway-optimized configurations experienced substantially fewer user fallbacks than predicted under legacy configurations; post-incident analysis confirms that pre-deployed parameters kept all network elements within safe operating bounds.

With the widespread use of distributed machine learning (DML), many IT companies have established networks dedicated to DML. Different communication architectures of DML have different traffic patterns and different requirements on network performance, which is closely related to network topology . However, traditional network topologies usually pursue general goals and are agnostic to the special communication pattern of the applications. The mismatch between network topology and the applications will directly affect the training performance. Although some studies have analyzed the effect of topology on training performance, the topologies and communication architectures involved are not comprehensive, and it is still not known which topology is appropriate for which communication architecture. This survey investigates typical topologies and analyzes whether they meet the requirements of three commonly used communication architectures (i.e., Parameter Server (PS), Tree and Ring architectures) of DML. Specifically, the topology requirements of each communication architecture and two common topology requirements (i.e., high scalability and fault tolerance) for DML are studied firstly. Next, whether these topologies meet the topology requirements is analyzed. Then, this paper discusses potential technologies and approaches to construct the appropriate scheme for each topology requirement, and then presents DMLNet , a novel network topology that suits the three communication architectures. Finally, several potential directions for future research are outlined.

LEACH-like clustering protocols focus mainly on the low-power, low-rate, and low-wakeup network applications, and work in a multiround clustering strategy that causes frequent handovers of cluster heads (CHs), thus less support for real-time services that require stable cluster topologies. Besides, these protocols are faced with respective drawbacks, such as suboptimality of selected heads, costly node-base station (BS) energy overheads, lack of runtime cluster maintenance, etc. This article proposes RANCE, a randomly centralized and on-demand clustering protocol, aiming at prolonging nodes’ clustered time to support internodes collaboration while being energy efficient in mobile ad hoc networks. First, RANCE designs a randomly centralized CH selection mechanism in which every node in the local wireless network is eligible to initiate the centralized CH selection, so that the self-organizing characteristics of mobile ad hoc nodes can be utilized for head selection optimization. Second, taking into account the wireless volatility caused by changes of topology, obstacles, signal strength, etc., the fine-grained cluster relationships maintenance is provided by means of multilevel aliveness and adaptive bidirectional heartbeat packets. Third, RANCE works in an event-driven and on-demand manner instead of a time-triggered manner in LEACH-like protocols, to reduce the impact on continuous services caused by frequent CH handovers among all nodes. Simulation results show that RANCE provides longer clustered time (over 99% of nodes’ lifetime in networks more than 100 nodes) and good clustering scalability with high consistency at minimum energy cost, and exhibits good potentials in mobile wireless environments that are infrastructureless/poor for continuous missions.

Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.

The economic, educational and vocational growth is the key factor to the unity and development of ethnic areas and population in China. Ethnic universities dedicated to minority nationalities play a key role in cultivating ethnic graduates for such a purpose. This paper proposes a Semantic Web approach for the collaborative analysis and visualization of ethnic education and vocation based on ethnic university-centric statistics. Semantic Web techniques are adopted for the modeling, storage and query of ethnic education and vocation data. A modified PageRank algorithm is derived to evaluate ethnic entrepreneurship and innovation achievements. Then, hidden relationships between educational and vocational statistics are mined by means of SPARQL query language. Finally, the collaborative analysis is visualized in a Baidu Map based Web frontend. Experiment results and demonstration of our system exhibit its applicability in analyzing and rendering ethnic education and vocation statistics.

The traditional total variation denoising method is prone to spurious edges under high noise conditions. This paper proposed a cooperative filtering method, which combines discrete wavelet transform (DWT) and total variation (TV). The total variation method is used to obtain the denoised image, and then the discrete wavelet transform is performed. The denoised image undergoes wavelet decomposition and reconstruction, could reduce the edge effect and improve the image quality. The experimental results show that the proposed method could solve the noise suppression of variational problems, at the same time, it could provide better peak signal to noise ratio and some other parameters.

Semantic Web approaches are often used for Web service description, modeling, semantics discovery, capabilities matching, etc. However, as the primary querying tool for Semantic Web, SPARQL is yet to be deeply explored to support Semantic Web service composition. Therefore the description, modeling and composition of Semantic Web services are usually two-tier. This paper extends SPARQL to support path query, so that SPARQL is used in our service composition framework which finds the top-k shortest data flows that satisfy the user constraints. Experiment results on real-world Web service datasets exhibit its applicable performance compared with service compositions using other SPARQL engines/extensions.

The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc. This paper proposes a K-means clustering algorithm based on the Binary Correlation Quantum Behaved Particle Swarm Optimization (BC-QPSO) to relieve the above shortcomings. Convergence is guaranteed in this improved K-means algorithm with probability 1 by means of the powerful global searching ability offered by BC-QPSO. The swarm fitness variance determines the transition between BC-QPSO and K-means. The experiment results on clustering analysis show that the improved K-means clustering algorithm outperforms the traditional algorithm with regard to remote sensing imaging precision.

During the execution period of composite Web services, the conventional centralized orchestration (CO) tends to incur various drawbacks such as unnecessary traffic, inappropriate dependencies, etc. Decentralized orchestration is advocated to solve these drawbacks but results in some other ones if the pure decentralized orchestration (PDO) is adopted such as high cost for component Web service proxy deployment, weak execution monitoring, etc. In this paper, by deriving the concept of affinity for component service relationship modeling and employing the novel service granule facility, we propose the decentralized orchestration with local centralized orchestration (DOLCO) to cope with the problems encountered by PDO. Experiments show that DOLCO can serve as the complement or alternative of decentralized orchestration.

This paper focuses on important issues pertinent to future networks, i.e. future network application trend, Network Model, and NGN development strategies. The authors challenge the orthodox OSI/RM and Internet architectural representation with a point of view that it does not explain modern network