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- Chen, Ling, et al.
(author)
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Reliable and efficient RAR-based distributed model training in computing power network
- 2024
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In: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 16:5, s. 527-540
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Journal article (peer-reviewed)abstract
- The computing power network (CPN) is a novel network technology that integrates computing power from the cloud, edge, and terminals using IP/optical cross-layer networks for distributed computing. CPNs can provide an effective solution for distributed model training (DMT). As a bandwidth optimization architecture based on data parallelism, ring all-reduce (RAR) is widely used in DMT. However, any node or link failure on the ring can interrupt or block the requests deployed on the ring. Meanwhile, due to the resource competition of batch RAR-based DMT requests, inappropriate scheduling strategies will also lead to low training efficiency or congestion. As far as we know, there is currently no research that considers the survivability of rings in scheduling strategies for RAR-based DMT. To fill this gap, we propose a scheduling scheme for RAR-based DMT requests in CPNs to optimize the allocation of computing and wavelength resources considering the time dimension while ensuring reliability. In practical scenarios, service providers may focus on different performance metrics. We formulate an integer linear programming (ILP) model and a RAR-based DMT deployment algorithm (RDDA) to solve this problem considering four optimization objectives under the premise of the minimum blocking rate: minimum computing resource consumption, minimum wavelength resource consumption, minimum training time, and maximum reliability. Simulation results demonstrate that our model satisfies the reliability requirements while achieving corresponding optimal performance for DMT requests under four optimization objectives.
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| 2. |
- Zhang, Boxin, et al.
(author)
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Physical layer encryption-based secure slicing in 5G RAN with hybrid-trusted links
- 2024
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In: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 16:8, s. 800-813
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Journal article (peer-reviewed)abstract
- In a 5G radio access network (RAN), network slicing enables dividing a single RAN infrastructure into multiple logical networks, efficiently accommodating services with diverse requirements. Although RAN slicing can help improve resource efficiency and reduce network costs, it is accompanied by various security risks. One of the security threats in RAN slicing is potential eavesdropping, resulting in the leakage of sensitive data within slices. Encryption technologies have been developed to address the eavesdropping problem at different layers in optical networks. We focus on physical layer encryption since it has been demonstrated beneficial in line-speed processing, low latency, and small encryption overhead. The problem of utilizing physical layer encryption technologies to achieve secure RAN slices remains unexplored since physical layer encryption introduces additional hardware costs. In this paper, we study how to realize secure RAN slicing based on physical layer encryption in a metro aggregation network that consists of hybrid-trusted links (i.e., links with different risks for eavesdropping). We propose an integer linear programming (ILP) model and an auxiliary graph-based heuristic for small-scale and large-scale networks, respectively. The objective is to maximize the number of deployed slices and minimize the total cost of secure slice deployment, which includes the costs of servers, line cards (LCs), encryption cards (ECs), and bandwidth resources. To evaluate the benefit of encryption, we compare it with a detour solution, which protects slices by routing through trusted links (i.e., where no additional hardware for encryption is deployed). Simulation results show that the encryption-based solution exhibits a lower cost than the benchmark when the same number of slices are deployed, and it can reduce the blocking ratio by up to 8.5% as slice requests increase. In addition, the average latency of slices is also reduced by up to 14.6%. (c) 2024 Optica Publishing Group
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