  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 Sl-EDGE, 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 Sl-EDGE, 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 0.25 of the optimum. We first assess the performance of our algorithms
through extensive numerical analysis, and show that Sl-EDGE instantiates slices
6x more efficiently then state-of-the-art MEC slicing algorithms. Furthermore,
experimental results on a 24-radio testbed with 9 smartphones demonstrate that
Sl-EDGE provides at once highly-efficient slicing of joint LTE connectivity,
video streaming over WiFi, and ffmpeg video transcoding.
