Optimal and probabilistic resource and capability analysis for network slice as a service

Network Slice as a Service is one of the key concepts of the fifth generation of mobile networks (5G). 5G supports new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. In addition, safety and security critical use cases move into focus. These use cases involve diverging requirements, e.g. network reliability, latency and throughput. Network virtualization and end-to-end mobile network slicing are seen as key enablers to handle those differing requirements and providing mobile network services for the various 5G use cases and between different tenants. Network slices are isolated, virtualized, end-to-end networks optimized for specific use cases. But still they share a common physical network infrastructure. Through logical separation of the network slices on a common end-to-end mobile network infrastructure, an efficient usage of the underlying physical network infrastructure provided by multiple Mobile Service Providers (MSPs) in enabled. Due to the dynamic lifecycle of network slices there is a strong demand for efficient algorithms for the so-called Network Slice Embedding (NSE) problem. Efficient and reliable resource provisioning for Network Slicing as a Service, requires resource allocation based on a mapping of virtual network slice elements on the serving physical mobile network infrastructure. In this thesis, first of all, a formal Network Slice Instance Admission (NSIA) process is presented, based on the 3GPP standardization. This process allows to give fast feedback to a network operator or tenant on the feasibility of embedding incoming Network Slice Instance Requests (NSI-Rs). In addition, corresponding services for NSIA and feasibility checking services are defined in the context of the ETSI ZSM Reference Architecture Framework. In the main part of this work, a mathematical model for solving the NSE Problem formalized as a standardized Linear Program (LP) is presented. The presented solution provides a nearly optimal embedding. This includes the optimal subset of Network Slice Instances (NSIs) to be selected for embedding, in terms of network slice revenue and costs, and the optimal allocation of associated network slice applications, functions, services and communication links on the 5G end-to-end mobile network infrastructure. It can be used to solve the online as well as the offline NSIA problem automatically in different variants. In particular, low latency network slices require deployment of their services and applications, including Network Functions (NFs) close to the user, i.e., at the edge of the mobile network. Since the users of those services might be widely distributed and mobile, multiple instances of the same application are required to be available on numerous distributed edge clouds. A holistic approach for tackling the problem of NSE with edge computing is provided by our so-called Multiple Application Instantiation (MAI) variant of the NSE LP solution. It is capable of determining the optimal number of application instances and their optimal deployment locations on the edge clouds, even for multiple User Equipment (UE) connectivity scenarios. In addition to that multi-path, also referred to as path-splitting, scenarios with a latency sensitive objective function, which guarantees the optimal network utilization as well as minimum latency in the network slice communication, is included. Resource uncertainty, as well as reuse and overbooking of resources guaranteed by Service Level Agreements (SLAs) are discussed in this work. There is a consensus that over-provisioning of mobile communication bands is economically infeasible and certain risk of network overload is accepted for the majority of the 5G use cases. A probabilistic variant of the NSE problem with an uncertainty-aware objective function and a resource availability confidence analysis are presented. The evaluation shows the advantages and the suitability of the different variants of the NSE formalization, as well as its scalability and computational limits in a practical implementation

An efficient model for mobile network slice embedding under resource uncertainty

The fifth generation (5G) of mobile networks will support several new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. End-to-end network slicing is a key-feature of 5G since it allows to share and at the same time isolate resources between several different use cases as well as between tenants by providing logical network. The virtual separation of the network slices on a common end-to-end mobile network infrastructure enables an efficient usage of the underlying network resources and provides means for security and safety related isolation of the defined logical networks. A much-discussed challenge is the reuse or overbooking of resources guaranteed by contract. However, there is a consensus that over-provisioning of mobile communication bands is economically infeasible and a certain risk of network overload is acceptable for the majority of the 5G use cases. In this paper, an efficient model for mobile network slice embedding is presented which enables an informed decision on network slice admission. This is based on the guaranteed end-to-end mobile network resources that have to be provided on the one hand and the capacities and capabilities of the underlying network infrastructure on the other hand. The network slice embedding problem is solved in form of a Mixed Integer Linear Program with an uncertainty-aware objective function. Subsequently, the confidence in the availability of each resource is analyzed

Outflows and mass accretion in collapsing dense cores with misaligned rotation axis and magnetic field

Outflows and jets are intimately related to the formation of stars, and play an important role in redistributing mass, energy and angular momentum within the dense core and parent cloud. The interplay between magnetic field and rotation is responsible for launching these outflows, whose formation has been generally carried out for idealized systems where the angle $\alpha$ between the rotation axis and large-scale magnetic field is zero. Here we explore, through three-dimensional ideal magneto-hydrodynamic simulations, the effects of a non-zero $\alpha$ on the formation of outflows during the collapse of dense pre-stellar cores. We find that mass ejection is less efficient for increasing angle $\alpha$, and that outflows are essentially suppressed for $\alpha\sim90^{\circ}$. An important consequence is a corresponding increase of the mass accreted onto the adiabatic (first) core. In addition, mean flow velocities tend to increase with $\alpha$, and misaligned configurations produce clumpy, heterogeneous outflows that undergo precession, and are more prone to instabilities.Comment: Accepted for publication in MNRAS letters, 6 pages 4 figure

A ring accelerator? Unusual jet dynamics in the IceCube candidate PKS 1502+106

On 2019/07/30.86853 UT, IceCube detected a high-energy astrophysical neutrino candidate. The Flat Spectrum Radio Quasar PKS 1502+106 is located within the 50 percent uncertainty region of the event. Our analysis of 15 GHz Very Long Baseline Array (VLBA) and astrometric 8 GHz VLBA data, in a time span prior and after the IceCube event, reveals evidence for a radio ring structure which develops with time. Several arc-structures evolve perpendicular to the jet ridge line. We find evidence for precession of a curved jet based on kinematic modelling and a periodicity analysis. An outflowing broad line region (BLR) based on the C IV line emission (Sloan Digital Sky Survey, SDSS) is found. We attribute the atypical ring to an interaction of the precessing jet with the outflowing material. We discuss our findings in the context of a spine-sheath scenario where the ring reveals the sheath and its interaction with the surroundings (narrow line region, NLR, clouds). We find that the radio emission is correlated with the $\gamma$-ray emission, with radio lagging the $\gamma$-rays. Based on the $\gamma$-ray variability timescale, we constrain the $\gamma$-ray emission zone to the BLR (30-200 $r_{\rm g}$) and within the jet launching region. We discuss that the outflowing BLR provides the external radiation field for $\gamma$-ray production via external Compton scattering. The neutrino is most likely produced by proton-proton interaction in the blazar zone (beyond the BLR), enabled by episodic encounters of the jet with dense clouds, i.e. some molecular cloud in the NLR.Comment: 35 pages, 33 figures, 3 tables; accepted by the MNRAS Main Journa

Brief Report: Sensorimotor Gating in Idiopathic Autism and Autism Associated with Fragile X Syndrome

Prepulse inhibition (PPI) may useful for exploring the proposed shared neurobiology between idiopathic autism and autism caused by FXS. We compared PPI in four groups: typically developing controls (n = 18), FXS and autism (FXS+A; n = 15), FXS without autism spectrum disorder (FXS−A; n = 17), and idiopathic autism (IA; n = 15). Relative to controls, the FXS+A (p < 0.002) and FXS−A (p < 0.003) groups had impaired PPI. The FXS+A (p < 0.01) and FXS−A (p < 0.03) groups had lower PPI than the IA group. Prolonged startle latency was seen in the IA group. The differing PPI profiles seen in the FXS+A and IA indicates these groups may not share a common neurobiological abnormality of sensorimotor gating

SON function performance prediction in a cognitive SON management system

As a reply to the increasing demand for fast mobile network connections the concept of Self-Organising Networks (SONs) has been developed, reducing the need for humans to execute Operation, Administration and Maintenance (OAM) tasks for mobile networks. However, a SON contains functions which are provided by different vendors as black boxes, making it hard to predict the performance of the network, especially under untested configurations. Since Mobile Network Operators (MNOs) have to fulfil rising mobile network performance demands while reducing costs at the same time, it is crucial to gain a better understanding of the network behaviour to allow a costneutral performance improvement while simultaneously reducing the risk of network misconfiguration and service disturbance. In this paper an approach is introduced to enhance SON Management models with cognitive Machine Learning (ML) methods. Therefore, the simulated behaviour of three different SON Functions is analysed and described by a Linear Regression (LR) Model. In a second step, performance data of network cells are analysed for similarities using k-Means Clustering. The findings of these two steps are then combined by fitting the models onto smaller clusters of cells. Finally, the utility of these models for predicting the performance of the network is evaluated and the different stages of refinement are compared with each other