Sl-edge

Abstract

Network slicing of multi-access edge computing (MEC) resources is expected to be a pivotal technology to the success of 5G networks and beyond. The key challenge that sets MEC slicing apart from traditional resource allocation problems is that edge nodes depend on tightly-intertwined and strictly-constrained networking, computation and storage resources. Therefore, instantiating MEC slices without incurring in resource over-provisioning is hardly addressable with existing slicing algorithms. The main innovation of this paper is SlEDGE , a unified MEC slicing framework that allows network operators to instantiate heterogeneous slice services (e.g., video streaming, caching, 5G network access) on edge devices. We first describe the architecture and operations of SlEDGE , and then show that the problem of optimally instantiating joint network-MEC slices is NP-hard. Thus, we propose near-optimal algorithms that leverage key similarities among edge nodes and resource virtualization to instantiate heterogeneous slices 7.5x faster and within 25% of the optimum. We first assess the performance of our algorithms through extensive numerical analysis, and show that SlEDGE instantiates slices 6x more efficiently then state-ofthe-art MEC slicing algorithms. Furthermore, experimental results on a 24-radio testbed with 9 smartphones demonstrate that SlEDGE provides simultaneously highly-efficient slicing of joint LTE connectivity, video streaming over WiFi, and ffmpeg video transcoding.

Auto-scaling edge cloud for network slicing

“…In this sense, autoscaling computational resources would interplay with the dynamic scaling of wireless network resources by deploying a coordinator agent. We refer to 3GPP (2018), Yan et al (2019), D'Oro et al (2020), and Liu et al (2020 for an insightful discussion on the design of coordinator, autoscaling network resources, and the global slicing control.…”

Architecting 5G RAN slicing for location aware vehicle to infrastructure communications: The Autonomous Tram use case

“…In this case, an offline reinforcement learning approach is proposed which allocates radio resources to different slices with the target of maximizing the resource utilization while ensuring the availability of resources. More generally, the resource allocation problem for RAN slicing is broadly studied in the literature (e.g., see [18][19][20][21][22]) and the various solutions achieve a different trade-off between isolation and optimized resource utilization. In [23] the resource allocation problem among different slices is formulated with the aim of minimizing the inter-slice interference in a cellular environment.…”