With the rapid increase of data, the scale of cloud is gradually expanding, forming a wide-area cloud platform consisting of multiple data centers distributed across different locations. For industry professionals, ma...
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Semantic communication has emerged as a promising paradigm through the transmission of semantic features to improve spectrum efficiency. This paper proposes an end-to-end semantic communication system for multi-task i...
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Low earth orbit (LEO) satellite network is promising to meet the low latency, extensive coverage, and flexible networking requirements for future 6G communications. To realize efficient data transmission in dynamic LE...
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With the advancement of communication and computer technology, network architectures have become increasingly complex, accompanied by the continual emergence of novel services. This increase in sophistication surpasse...
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With the advancement of communication and computer technology, network architectures have become increasingly complex, accompanied by the continual emergence of novel services. This increase in sophistication surpasses the abilities of manual maintenance and traditional network management solutions, thereby elevating the potential risk of network failures. This article provides a comprehensive review of significant network incidents that have occurred over the past decade and outlines the evolution of network self-healing. Building on these findings, we propose an effective knowledge-driven self-healing pattern (KSHP) suitable for a broad spectrum of network failure scenarios, designed to alleviate the challenges of automation and scalability in autonomous networks. In our case study, we implemented a self-healing agent following the KSHP schema to mitigate network service failures caused by link congestion. The experimental results validate the efficacy and practicality of KSHP in network fault scenarios. Furthermore, future directions are discussed and analyzed in detail. The insights presented in this article provide a basic pattern for the knowledge-driven self-healing framework, facilitating the achievement of Level 5 autonomous networks.
With the increasing complexity of networks, network telemetry becomes a critical part of network management. However, existing network telemetry systems still suffer from excessive control overhead, forwarding overhea...
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With the increasing complexity of networks, network telemetry becomes a critical part of network management. However, existing network telemetry systems still suffer from excessive control overhead, forwarding overhead, and latency. In this paper, we propose INT-LLPP, a novel in-band network-wide telemetry system with low-latency and low-overhead path planning. The network telemetry architecture of INT-LLPP is unique in that it only requires a set of probes to collect telemetry items for multiple service flows. Moreover, the proposed Probe Path Generation (PPG) algorithm optimizes the probe paths to reduce the forwarding overhead and achieve full network coverage. To balance the telemetry latency and control overhead, we propose an efficient algorithm called the Simulated Annealing Maximum Latency Setting (SAMLS) algorithm, which controls the length of the probe paths. Simulation results show that INT-LLPP can reduce network telemetry control overhead by over 50% and reduce forwarding overhead by 5% to 10%. Moreover, INT-LLPP can lower telemetry latency by 30% to 40%.
Segment Routing (SR) is a flexible and efficient source-routing technology. It can forward traffic along arbitrary paths in the network and has good scalability without the maintenance of routing information at interm...
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Air-space-ground integrated network (ASGIN) is considered as one of the candidate technologies for the sixth generation (6G) mobile communication due to its ubiquitous connectivity and wide coverage. And accurate clut...
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In recent years, the rapid development of virtual reality (VR) technology has brought the concept of the metaverse from science fiction to reality. VR devices have made significant progress in the consumer market, wit...
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Human trajectory forecasting is a crucial challenge in the social security and autonomous driving due to the inherent uncertainty in human actions and intentions. To leverage the potential motion patterns of human beh...
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Discriminative canonical correlation analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists of two pa...
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Discriminative canonical correlation analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists of two parts: (i) mean-centering that subtracts the sample mean from the sample and (ii) solving the generalized eigenvalue problem. The cost of DCCA is expensive when dealing with a large number of high-dimensional samples. To solve this problem, here we propose a quantum DCCA algorithm. Specifically, we devise an efficient method to compute the mean of all samples and then use block-Hamiltonian simulation and quantum phase estimation to solve the generalized eigenvalue problem. Our algorithm achieves a polynomial speedup in the dimension of samples under certain conditions over its classical counterpart.
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