- Award ID(s):
- 1854049
- NSF-PAR ID:
- 10310312
- Date Published:
- Journal Name:
- Information Science and Applications. Lecture Notes in Electrical Engineering
- Volume:
- 739
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the k-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called minMaxBWUtilHops, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio.more » « less
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Abstract Dynamic or temporal networks enable representation of time-varying edges between nodes. Conventional adjacency-based data structures used for storing networks such as adjacency lists were designed without incorporating time and can thus quickly retrieve all edges between two sets of nodes (a
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null (Ed.)
Abstract Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple mobile 3D printing robots work together cooperatively to print the desired part. At the core of C3DP lies the chunk-based printing strategy. This strategy splits the desired part into smaller chunks, and then the chunks are assigned and scheduled to be printed by individual printing robots. In our previous work, we presented various hardware and software components of C3DP, such as mobile 3D printers, chunk-based slicing, scheduling, and simulation. In this study, we present a fully integrated and functional C3DP platform with all necessary components, including chunker, slicer, scheduler, printing robots, build floor, and outline how they work in unison from a system-level perspective. To realize C3DP, new developments of both hardware and software are presented, including new chunking approaches, scalable scheduler for multiple robots, SCARA-based printing robots, a mobile platform for transporting printing robots, modular floor tiles, and a charging station for the mobile platform. Finally, we demonstrate the capability of the system using two case studies. In these demonstrations, a CAD model of a part is fed to the chunker, divided into smaller chunks, passed to the scheduler, and assigned and scheduled to be printed by the scheduler with a given number of robots. The slicer generates G-code for each of the chunks and combines G-code into one file for each robot. The simulator then uses the G-code generated by the slicer to generate animations for visualization purposes.
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