OKpi: All-KPI Network Slicing Through Efficient Resource Allocation

Abstract

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 realworld scenarios, finding it to closely match the optimum.

Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

“…Each service is associated with an SLA, which specifies the requirements in terms of KPIs [17], and with a maximum amount of resources, e.g., for which the user is willing to pay the network provider. We consider latency as the most relevant KPI, although our model could be extended to others, like throughput and energy consumption.…”

“…• We first formalize the DMP, and show that even finding a feasible solution to the problem -regardless of its cost -is NP-hard. • We take latency as the main Key Performance Indicator (KPI) [17], as specified by the Service Level Agreement (SLA), and show how to calculate the minimal amount of CPU resources required for placing every service chain on any datacenter, while satisfying the latency requirements. • We develop a placement algorithm that, leveraging some bounded amount of resource augmentation, is guaranteed to provide a feasible solution whenever such a solution exists for the case with no resource augmentation.…”

A 5G-Based eHealth Monitoring and Emergency Response System: Experience and Lessons Learned

“…In case of an emergency, the Central eServer requests to the operator (by means of a query to the 5GT-VS) the instantiation of an edge emergency service (Edge-NS in Figure 2) connected to the monitoring NFV-NS close enough to the emergency location. A placement algorithm (in the 5GT-SO) [26]- [28] is in charge of deciding the placement of the Edge eServer based on (i) location constraints, (ii) information regarding the availability of local computation resources, and (iii) latency constraints of the AR application. The placement algorithm only computes the ideal Edge eServer location over the operator's local resources.…”

Cotorra

“…OKpi [6] is a service orchestration algorithm that decides the deployment of VFs and VLs to meet latency constraints. Additionally, OKpi also considers coverage constraints and tells to a robot or end user to which RU it has to attach to experience low latencies.…”