2 research outputs found
OKpi: All-KPI Network Slicing Through Efficient Resource Allocation
Networks can now process data as well as transporting it; it follows that
they can support multiple services, each requiring different key performance
indicators (KPIs). Because of the former, it is critical to efficiently
allocate network and computing resources to provide the required services, and,
because of the latter, such decisions must jointly consider all KPIs targeted
by a service. Accounting for newly introduced KPIs (e.g., availability and
reliability) requires tailored models and solution strategies, and has been
conspicuously neglected by existing works, which are instead built around
traditional metrics like throughput and latency. We fill this gap by presenting
a novel methodology and resource allocation scheme, named OKpi, which enables
high-quality selection of radio points of access as well as VNF (Virtual
Network Function) placement and data routing, with polynomial computational
complexity. OKpi accounts for all relevant KPIs required by each service, and
for any available resource from the fog to the cloud. We prove several
important properties of OKpi and evaluate its performance in two real-world
scenarios, finding it to closely match the optimum
Edge-powered Assisted Driving For Connected Cars
Assisted driving for connected cars is one of the main applications that
5G-and-beyond networks shall support. In this work, we propose an assisted
driving system leveraging the synergy between connected vehicles and the edge
of the network infrastructure, in order to envision global traffic policies
that can effectively drive local decisions. Local decisions concern individual
vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when;
global decisions, instead, involve whole traffic flows. Such decisions are made
at different time scales by different entities, which are integrated within an
edge-based architecture and can share information. In particular, we leverage a
queuing-based model and formulate an optimization problem to make global
decisions on traffic flows. To cope with the problem complexity, we then
develop an iterative, linear-time complexity algorithm called Bottleneck
Hunting (BH). We show the performance of our solution using a realistic
simulation framework, integrating a Python engine with ns-3 and SUMO, and
considering two relevant services, namely, lane change assistance and
navigation, in a real-world scenario. Results demonstrate that our solution
leads to a reduction of the vehicles' travel times by 66 in the case of lane
change assistance and by 20 for navigation, compared to traditional,
local-coordination approaches.Comment: arXiv admin note: text overlap with arXiv:2008.0933