RELSAD: A Python package for reliability assessment of modern distribution systems

publishedVersio

Dependability of the NFV Orchestrator: State of the Art and Research Challenges

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The introduction of network function virtualisation (NFV) represents a significant change in networking technology, which may create new opportunities in terms of cost efficiency, operations, and service provisioning. Although not explicitly stated as an objective, the dependability of the services provided using this technology should be at least as good as conventional solutions. Logical centralisation, off-the-shelf computing platforms, and increased system complexity represent new dependability challenges relative to the state of the art. The core function of the network, with respect to failure and service management, is orchestration. The failure and misoperation of the NFV orchestrator (NFVO) will have huge network-wide consequences. At the same time, NFVO is vulnerable to overload and design faults. Thus, the objective of this paper is to give a tutorial on the dependability challenges of the NFVO, and to give insight into the required future research. This paper provides necessary background information, reviews the available literature, outlines the proposed solutions, and identifies some design and research problems that must be addressed.acceptedVersio

Estimating Available Bandwidth on Access Links by Means of Stratified Probing

This paper presents a novel approach for estimation of available bandwidth on access links using stratified probing. The main challenges of performing such estimations in this network part is related to the bursty nature of cross-traffic and the related uncertainty regarding appropriate time period for producing sample estimates. Under the fluid flow traffic model assumption, these problems would not be present – but for the access network part this assumption does not hold. The method suggested in this paper is based on a four-phase approach. In the first phase a traffic profile for the cross-traffic is established, with focus on detecting periodic behavior and duration of respectively burst and idle sub-periods. In the second and third phase, the active probing is split into strata and synchronized according to the burst/idle sub periods. In the final phase, the actual probing and estimation of available bandwidth takes place. The method is analyzed by means of experiments in a controlled lab environment, using adaptive video streaming as the service with a periodical behavior. The empirical results are considered quite promising both in terms of accuracy and the low degree of intrusiveness facilitated by the stratified approach.<p>Copyright © 2012-2014 Engineering and Technology Publishing, All Rights Reserved</p

Traffic modeling for aggregated periodic IoT data

The Internet of Things (IoT) is emerging in the telecommunication sector, and will bring a very large number of devices that connect to the Internet in the near future. The expected growth in such IoT nodes necessitates appropriate traffic models in order to evaluate their impact on different aspects of networking, e.g., on signaling load in the networks, or on processing load of the data in a cloud. In this paper we analyze the characteristics of aggregated periodic IoT data based on related work, and compare them with a Poisson process as approximation for the superposed traffic as assumed in standardization. Such an approximation is crucial in order to investigate the scalability of an IoT network, as it may be impossible in practice to measure or to simulate large-scale IoT deployments. The accuracy and applicability of the Poisson process is investigated for the use case “IoT cloud”. The results show that the Poisson process may induce large errors depending on the performance metric of interest. This error must be considered by standardization and requires more sophisticated traffic models. As key contributions, we provide realistic traffic models for periodic IoT data, introduce performance metrics for quantifying the bias, and derive reference values as to when the Poisson process can be assumed for aggregated periodic IoT data.submittedVersion© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Non-markovian survivability assessment model for infrastructure wireless networks

Network design and operation of a mobile network infrastructure, especially its access points, need to consider survivability as a fundamental requirement. Quantifiable approaches to survivability analysis of such infrastructures are crucial. Most existing analytical models analyze the networks transient behaviors by applying homogeneous continuous-time Markov chain (CTMC). However, the distributions for transitions between states during a failure recovery are not exponential in many real cases. To address this problem, we first propose to use a non-Markovian model to characterize the transient behavior of the phased recovery of the network after a failure. Then, based on the proposed model, we conduct survivability analysis of the network. Moreover, numerical results are presented to validate the phase type (PH) approximation used in the proposed model. A case study illustrates the effects of different model parameters on the network's survivability. These results shed new insights not only on survivability analysis, e.g. the non-Markovian phased recovery model, but also on survivability provisioning, e.g. how the model parameters affect the network's survivability, of such a network against failure events

Survivability analysis of a two-tier infrastructure-based wireless network

Network design and operation of a mobile network infrastructure, especially its base stations, need to consider survivability as a fundamental requirement. Quantifiable approaches to survivability analysis of such infrastructures are crucial. The objective of this paper is to propose a model for quantification of the survivability of a two-tier infrastructure-based wireless network subject to massive failures, caused by e.g. natural disasters, common mode hardware and software failures, and security attacks. We use a Markov modelling approach to analyze the transient behavior of the recovery phases of a two-tier infrastructure-based wireless network. In order to take location information of base stations into consideration, the spatial average network performance is estimated by means of a stochastic geometry based approach. In order to avoid state space explosion while addressing large networks, an approximate product-form analysis approach is also presented, where the two base stations tiers are decoupled such that their survivability analysis can be studied independently. The assumptions used in the proposed models, including Poisson point process and product-form decomposition, are validated on real data. Numerical experiments are also performed to investigate the approximation accuracy and computational efficiency of the product-form analysis approach, as well as to examine the effects of different parameters on the network’s survivability. The results show that the approximate product-form approach has reasonably good accuracy and is suitable for analysis of large size networks

A Framework for Spatial and Temporal Evaluation of Network Disaster Recovery

The support of vital societal functions requires a reliable communication network, especially in the presence of crises and disastrous events. Disasters caused by natural factors including earthquakes, fires, floods or hurricanes can disable network elements such as links and nodes and cause widespread disruption in end users connectivity to network services. Effects of disasters can vary over space and time due to disaster escalation and propagation. Network recovery from disasters requires understanding of both the spatial properties of the hazard at hand, and their temporal evolution. While the former has already been addressed in the literature, existing models and measures are unable to capture the temporal aspects of disaster recovery.This paper proposes a framework for spatial and temporal evaluation of network disaster recovery. It allows for modelling random spatial patterns of disasters in a geographical grid. The temporal aspects captured in our framework include changes due to the progression of a potentially shape-changing disaster across the affected area, as well as to the recovery actions of adaptive network reconfiguration and topology reconstruction undertaken by the network operator. The framework applicability is demonstrated on a content delivery network use case example, where we capture the evolving network performance in terms of the average shortest path length between the peers and the content replicas hosted by servers. By providing insights into the spatial and temporal effects of both disaster escalation and remediation measures, our proposed framework lays down the groundwork for flexible disaster modelling and recovery sequence optimization

Traffic modeling for aggregated periodic IoT data

The Internet of Things (IoT) is emerging in the telecommunication sector, and will bring a very large number of devices that connect to the Internet in the near future. The expected growth in such IoT nodes necessitates appropriate traffic models in order to evaluate their impact on different aspects of networking, e.g., on signaling load in the networks, or on processing load of the data in a cloud. In this paper we analyze the characteristics of aggregated periodic IoT data based on related work, and compare them with a Poisson process as approximation for the superposed traffic as assumed in standardization. Such an approximation is crucial in order to investigate the scalability of an IoT network, as it may be impossible in practice to measure or to simulate large-scale IoT deployments. The accuracy and applicability of the Poisson process is investigated for the use case “IoT cloud”. The results show that the Poisson process may induce large errors depending on the performance metric of interest. This error must be considered by standardization and requires more sophisticated traffic models. As key contributions, we provide realistic traffic models for periodic IoT data, introduce performance metrics for quantifying the bias, and derive reference values as to when the Poisson process can be assumed for aggregated periodic IoT data

Including Failure Correlation in Availability Modeling of a Software-Defined Backbone Network

Software-defined networking (SDN) promises to improve the programmability and flexibility of networks, but also brings new challenges that need to be explored. The main objective of this paper is to include failure correlation in a quantitative assessment of the properties of SDN backbone networks to determine whether they can provide similar availability as the traditional IP backbone networks. To achieve this goal, this paper has formalized a two-level availability model that captures the global network connectivity without neglecting the essential details and which includes a failure correlation assessment. This paper proposes a modular and systematic approach for characterizing the principal minimal-cut sets in both SDN and traditional networks, and stochastic activity network models for characterizing the single network elements. To demonstrate the feasibility of the model, an extensive sensitivity analysis has been carried out on a national backbone network

Implementing the Availability Model of a Software-Defined Backbone Network in Möbius

Software-defined networking (SDN) promises to im-prove the programmability and flexibility of networks, but it may bring also new challenges that need to be explored. One open issue is the quantitative assessment of the properties of SDN backbone networks to determine whether they can provide similar availability to the traditional IP backbone networks. To achieve this goal, a two-level availability model that is able to capture the global network connectivity without neglecting the essential details and which includes a failure correlation assessment should be considered. The two-level availability modelis composed by a structural model and the dynamic models of the principal minimal-cut sets of the network. The purpose ofthis technical report is to extensively present the implementation on M ̈obius of the Stochastic Activity Network (SAN) availability model of the network elements and the principal minimal-cut sets of a SDN backbone network and the corresponding traditional backbone network