Routing and Timeslot Scheduling for SPN Fine-Granularity Slices

Abstract

The integration of 5G and vertical industries promotes the development of the energy Ethernet while putting forward fine granularity, flexibility, high reliability, and deterministic low-latency service requirements for the smart grid and the ubiquitous power Internet of Things (UPIoT). As the bearer architecture supporting the next-generation optical transmission network, the Slicing Packet Network (SPN) slice granularity decreases from 5 Gbps to 10 Mbps fine granularity and the frame period of 5 Gbps large-granularity slices is short, so the non-deterministic delay caused by timeslot conflicts has a negligible impact on the end-to-end delay, and the timeslot scheduling is unnecessary. However, due to the reduction in timeslot granularity and the change in frame structure in 10 Mbps slices, the scheduling of conflicting timeslots and the complex device computing management problems need to be solved urgently. In this paper, we establish a model of routing embedded timeslot scheduling for the routing of fine-granularity slices and timeslot scheduling problems in SPN-based FlexE interfaces, for which we propose a deterministic timeslot allocation mechanism supporting end-to-end low-latency transmission. According to the timeslot symmetry, the mechanism can reduce the space of feasible solutions through ant colony optimization and unidirectional neighborhood search (ACO-UNS), so as to efficiently solve the scheduling of conflicting timeslots and provide end-to-end delay guarantee for delay-sensitive services. Finally, we make a comparison between the ACO-UNS algorithm and the timeslot random dispatching algorithm (ACO-RD); the results show that, relative to the ACO-RD, the reduction in the proposed ACO-UNS is 98.721% for the end-to-end delay of fine-granularity slices.

Traffic-Driven Proactive Slot Orchestration in an Optical Network with Fine-Granularity Flexible Ethernet Interfaces

“…In this paper, we used two evaluation indicators to compare the performance of different service traffic prediction models. They are the R-Square (R 2 ) and Normalized Mean Squared Error (NMSE), and their definition are as follows in (18) and (19).…”

“…Extensive simulations confirm the effectiveness of these CLR strategies, highlighting the potential of FlexE and elastic optical networks for efficient restoration in scenarios involving FlexE switch outages. Recently, Gu, R., et al [19] established a model of routing embedded timeslot scheduling for the routing of fine-granularity slices and timeslot scheduling problems in SPN-based FlexE interfaces, for which a deterministic timeslot allocation mechanism supporting end-to-end low-latency transmission is proposed.…”