Low-level Configuration Research Articles

Technological Transformation of Telco Operators towards Seamless IoT Edge-Cloud Continuum.

This article investigates and discusses challenges in the telecommunication field from multiple perspectives, both academic and industry sides are catered for, surveying the main points of technological transformation toward edge-cloud continuum from the view of a telco operator to show the complete picture, including the evolution of cloud-native computing, Software-Defined Networking (SDN), and network automation platforms. The cultural shift in software development and management with DevOps enabled the development of significant technologies in the telecommunication world, including network equipment, application development, and system orchestration. The effect of the aforementioned cultural shift to the application area, especially from the IoT point of view, is investigated. The enormous change in service diversity and delivery capabilities to mass devices are also discussed. During the last two decades, desktop and server virtualization has played an active role in the Information Technology (IT) world. With the use of OpenFlow, SDN, and Network Functions Virtualization (NFV), the network revolution has got underway. The shift from monolithic application development and deployment to micro-services changed the whole picture. On the other hand, the data centers evolved in several generations where the control plane cannot cope with all the networks without an intelligent decision-making process, benefiting from the AI/ML techniques. AI also enables operators to forecast demand more accurately, anticipate network load, and adjust capacity and throughput automatically. Going one step further, zero-touch networking and service management (ZSM) is proposed to get high-level human intents to generate a low-level configuration for network elements with validated results, minimizing the ratio of faults caused by human intervention. Harmonizing all signs of progress in different communication technologies enabled the use of edge computing successfully. Low-powered (from both energy and processing perspectives) IoT networks have disrupted the customer and end-point demands within the sector, as such paved the path towards devising the edge computing concept, which finalized the whole picture of the edge-cloud continuum.

Creation and validation of systems for product and process configuration based on data analysis

In the course of increasing individualization of customer demand, configurable products are gaining importance. Nowadays, variant-specific bills of materials and routings for configurable products are created with the help of rule-based configuration systems, so-called low-level configuration systems. The rules and generic structures on which such configuration systems are based are created manually today. This is challenging because it can be difficult and sometimes impossible to directly transfer expert knowledge into those systems. Furthermore documents that have already been created by experts in the past such as bills of material and routings contain relevant information as well which may be exploited to compose configuration systems. However, in the literature, there are no approaches yet to systematically transfer expert knowledge into configuration systems or to consider existing documents. In addition, the creation of such configuration systems is prone to error due to their complexity. Although there are already numerous approaches to the formal testing of configuration systems, approaches based on data analysis to support the validation of such systems have not yet been considered. Therefore, in this paper an approach is presented to automatically create low-level configuration systems by means of exemplary variant-specific bill of materials and routings using machine learning. The super bill of materials and the super routing as well as the dependencies between the product characteristics and the components respectively the operations are learned. Furthermore, it is shown how errors in the input data as well as errors in the resulting low-level configuration system can be detected by means of anomaly detection.

Diagnostic Analysis of Multimodel Rainstorm Forecast for Cases Based on MODE Method

It is very important to analyze the performance of the model rainstorm forecast for understanding the intensity and position deviation of the model precipitation and improving the forecast ability. This paper uses classical scoring and the MODE (Method for Object-Based Diagnostic Evaluation) method to evaluate the forecast performance of different products. The forecast and observation data used in the article mainly include CMA (China Meteorological Administration) multi-source merged precipitation analysis, the precipitation forecast of ECMWF (European Centre for Medium-Range Weather Forecasts), CMA-Meso (Mesoscale model forecast of CMA) and SCMOC (National Meteorological Center grid precipitation forecast guidance product) data. At the same time, the possible correction method of heavy rainfall area is explored by using the high and low-level circulation configuration of the ECMWF model. The main conclusions are as follows: ① MODE spatial verification shows that the number, intensity, area and location of ECMWF rainstorm precipitation objects match the observed precipitation best, which is obviously better than SCMOC and CMA-Meso precipitation forecasts. There are significantly fewer SCMOC rainstorm precipitation objects, and the area of each single precipitation object is significantly larger, which often fails to report the small area objects of convective precipitation. ② The reason for the high TS score of SCMOC is that it reduces the number of small area rainstorm objects and avoids the “double punishment” phenomenon caused by position forecast error, which leads to the failure of SCMOC in local rainstorm forecasts. ③ Analyzing the relationship between the circulation situation of the ECMWF model and the location of rainstorm forecasts by the model, it is found that the location of the rainstorm area is consistent with the upper circulation system, especially with the strong rising area of the vertical velocity of 700 hPa and the high value area of the specific humidity of 850 hPa. When the rainstorm area coincides with the upper air system but is not consistent with the ground convergence area and the high value area of velocity potential, the rainstorm location often has a large deviation. The location of the surface convergence area can be used as a reference to improve the performance of the rainstorm forecast.

Real-Time Tracking of Object Melting Based on Enhanced DeepLab v3+ Network.

In order to reveal the dissolution behavior of iron tailings in blast furnace slag, the main component of iron tailings, SiO2, was used for research. Aiming at the problem of information loss and inaccurate extraction of tracking molten SiO2 particles in high temperature, a method based on the improved DeepLab v3+ network was proposed to track, segment, and extract small object particles in real time. First, by improving the decoding layer of the DeepLab v3+ network, construct dense ASPP (atrous spatial pyramid pooling) modules with different dilation rates to optimize feature extraction, increase the shallow convolution of the backbone network, and merge it into the upper convolution decoding part to increase detailed capture. Secondly, integrate the lightweight network MobileNet v3 to reduce network parameters, further speed up image detection, and reduce the memory usage to achieve real-time image segmentation and adapt to low-level configuration hardware. Finally, improve the expression of the loss function for the binary classification model of small object in this paper, combining the advantages of the Dice Loss binary classification segmentation and the Focal Loss balance of positive and negative samples, solving the problem of unbalanced dataset caused by the small proportion of positive samples. Experimental results show that MIoU (mean intersection over union) of the proposed model for small object segmentation is 6% higher than that of the original model, the overall MIoU is increased by 3%, and the execution time and memory consumption are only half of the original model, which can be well applied to real-time tracking and segmentation of small particles.

Quingo: A Programming Framework for Heterogeneous Quantum-Classical Computing with NISQ Features

The increasing control complexity of Noisy Intermediate-Scale Quantum (NISQ) systems underlines the necessity of integrating quantum hardware with quantum software. While mapping heterogeneous quantum-classical computing (HQCC) algorithms to NISQ hardware for execution, we observed a few dissatisfactions in quantum programming languages (QPLs), including difficult mapping to hardware, limited expressiveness, and counter-intuitive code. In addition, noisy qubits require repeatedly performed quantum experiments, which explicitly operate low-level configurations, such as pulses and timing of operations. This requirement is beyond the scope or capability of most existing QPLs. We summarize three execution models to depict the quantum-classical interaction of existing QPLs. Based on the refined HQCC model, we propose the Quingo framework to integrate and manage quantum-classical software and hardware to provide the programmability over HQCC applications and map them to NISQ hardware. We propose a six-phase quantum program life-cycle model matching the refined HQCC model, which is implemented by a runtime system. We also propose the Quingo programming language, an external domain-specific language highlighting timer-based timing control and opaque operation definition, which can be used to describe quantum experiments. We believe the Quingo framework could contribute to the clarification of key techniques in the design of future HQCC systems.

Availability and service disruption of network services: From high-level requirements to low-level configuration constraints

In the context of Network Function Virtualization (NFV), a Network Service (NS) is defined as a composition of Network Functions (NFs), which can be Virtual Network Functions (VNFs) or Physical Network Functions (PNFs). The availability of an NS is among its important characteristics. Moreover, when there is a service outage there is often a service disruption. We define the Service Disruption Time for an NS functionality as the amount of time for which the service state is lost due to service outages for a given period. The Service Data Disruption for an NS functionality is defined as the maximum amount of data lost due to a service outage. To have a comprehensive design solution considering service availability and service disruption, we propose a novel method which enhances an NS design by mapping service-level availability and disruption requirements to constraints on low-level configuration parameters, and calculates the number of required standby instances for each VNF so that the requirements can be met. Our method guarantees the availability and disruption requirements at any scaling level of the NS while minimizing the resource cost. To reduce the complexity of the complete search for the optimal configuration we also propose a heuristic algorithm and perform experiments to compare it with the complete search.

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

NORA: An Approach for Transforming Network Management Policies into Automated Planning Problems.

Realizing autonomic management control loops is pivotal for achieving self-driving networks. Some studies have recently evidence the feasibility of using Automated Planning (AP) to carry out these loops. However, in practice, the use of AP is complicated since network administrators, who are non-experts in Artificial Intelligence, need to define network management policies as AP-goals and combine them with the network status and network management tasks to obtain AP-problems. AP planners use these problems to build up autonomic solutions formed by primitive tasks that modify the initial network state to achieve management goals. Although recent approaches have investigated transforming network management policies expressed in specific languages into low-level configuration rules, transforming these policies expressed in natural language into AP-goals and, subsequently, build up AP-based autonomic management loops remains unexplored. This paper introduces a novel approach, called NORA, to automatically generate AP-problems by translating Goal Policies expressed in natural language into AP-goals and combining them with both the network status and the network management tasks. NORA uses Natural Language Processing as the translation technique and templates as the combination technique to avoid network administrators to learn policy languages or AP-notations. We used a dataset containing Goal Policies to evaluate the NORA’s prototype. The results show that NORA achieves high precision and spends a short-time on generating AP-problems, which evinces NORA aids to overcome barriers to using AP in autonomic network management scenarios.

Assessment of NVSHMEM for High Performance Computing

High Performance Computing has been a driving force behind important tasks such as scientific discovery and deep learning. It tends to achieve performance through greater concurrency and heterogeneity, where the underlying complexity of richer topologies is managed through software abstraction. In this paper, we present our assessment of NVSHMEM, an experimental programming library that supports the Partitioned Global Address Space programming model for NVIDIA GPU clusters. NVSHMEM offers several concrete advantages. One is that it reduces overheads and software complexity by allowing communication and computation to be interleaved vs. separating them into different phases. Another is that it implements the OpenSHMEM specification to provide efficient fine-grained one-sided communication, streamlining away overheads due to tag matching, wildcards, and unexpected messages which have compounding effect with increasing concurrency. It also offers ease of use by abstracting away low-level configuration operations that are required to enable low-overhead communication and direct loads and stores across processes. We evaluated NVSHMEM in terms of usability, functionality, and scalability by running two math kernels, matrix multiplication and Jacobi solver, and one full application, Horovod, on the 27,648-GPU Summit supercomputer. Our exercise of NVSHMEM at scale contributed to making NVSHMEM more robust and preparing it for production release.

Evolution and Testing of NB-IoT Solutions

NB-IoT is the first 3GPP technology that has been specifically developed for IoT. In this paper, we will introduce the most important NB-IoT features included in Release 14, Release 15, and Release 16 and the need for testing of NB-IoT solutions. This paper is focused on demonstrating the potential of the new experimentation features included into the TRIANGLE testbed to support testing and benchmarking of Narrowband Internet of Thing (NB-IoT) solutions. TRIANGLE provides an experimentation framework that offers a high level of abstraction using a framework that allows for defining and executing experiments in a very straightforward way. The experimentation framework developed as part of the TRIANGLE project optionally keeps experimenters far from the low level configuration of the infrastructure and provides scenarios which reproduce realistic conditions.

Assisting reachability verification of network configurations updates with NUV

Configuring a network is always difficult and error-prone because of low-level configuration languages and complex routing mechanisms. Most network outages occur when network the configuration is updated. Thus, it is important to proactively verify network configurations. Unfortunately, there are two practical obstacles that inhibit the performance of existing configuration verification tools: (i) Lack of knowledge. Network users often do not know which input verification queries need to be verified. (ii) Limited scalability. Even the state-of-the-art tool (i.e., Minesweeper [1]) takes approximately 500 seconds to complete a single reachability query for a network with tens of routers. Considering the total number of queries that must be verified, real networks often make such techniques impractical. In this paper, we propose NUV, a framework for performing network configuration update verification. NUV outputs verification queries for only the endpoints whose forwarding behavior has changed under the updated network configuration. Based on the network forwarding model, NUV infers the potential traffic impact and eliminates endpoints whose forwarding behavior is equivalent in both the original network configuration and the updated configuration. The NUV outputs can be used as input to a rich collection of existing configuration verification tools. Evaluations on a series of benchmark networks show that NUV can solve the lack of knowledge problem, and it enables query verification to execute at speeds orders of magnitude faster than existing tools; thus, it also improves verification scalability.

A comparative study for bandwidth on demand using ONOS reactive and intent forwarding

<span>Telco operators need to tailor their networks to be agile, efficient and able manage the operational cost at moderate level. Furthermore, the norm of network volatility practiced by many enterprises open to a new challenge in managing an increasing traffic over the same WAN. For an enterprise, turning toward Bandwidth-on-Demand (BoD) approach promises to deliver extra network capacity when in demand without the complexity of running separate network. However, they need to manually transform these high-level policies into low-level configuration command and thus error-prone. This study was to address the effects of SDN (software-defined network) on the BoD performance towards the differences between the reactive and intent forwarding based on the assessment matrix. The testbed was performed under Mininet simulator which interacted with ONOS (Open Network Operating System). Mesh type topology with a predetermine parameter matrix was used using random topology generator. The expected outcome from this research was to show the effect on the BoD performance under two type of forwarding; reactive and intent based on the assessment matrix. This report contributed towards determining the effectiveness of SDN architecture on BoD performance and to improve future benchmarking on SDN-BoD testbed.</span>

Cooperative driving strategies of connected vehicles for stabilizing traffic flow

It is foreseeable that connected vehicles (CVs) and regular vehicles (RVs) will be operated together on roads in the upcoming decades. The appearance of CVs provides us an opportunity to improve traffic efficiency by guiding drivers to properly steer their CVs. To take advantage of this opportunity, this paper proposes two cooperative driving strategies for CVs that move in heterogeneous RV and CV traffic, namely, CDS-L and CDS-G. CDS-L is a local strategy that considers only the traffic surrounding the subject CVs. CDS-G is a global strategy that considers the conditions of all CVs on a road. The basis of the strategies is to incorporate the spatial and temporal averages of the driving status of surrounding CVs. A parameter that is a function of the traffic density is introduced to balance traffic efficiency and fluctuations. Simulation results demonstrate that the proposed strategies well suppress traffic fluctuations and thus promote traffic efficiency. The proposed strategies are dedicated to traffic congestion mitigation by using low-level configuration CVs.

SDN Controller Placement in IoT Networks: An Optimized Submodularity-Based Approach.

Software-Defined Networking (SDN) has opened a promising and potential approach for future networks, which mostly requires the low-level configuration to implement different controls. With the high advantages of SDN by decomposing the network control plane from the data plane, SDN has become a crucial platform to implement Internet of Things (IoT) services. However, a static SDN controller placement cannot obtain an efficient solution in distributed and dynamic IoT networks. In this paper, we investigate an optimization framework under a well-known theory, namely submodularity optimization, to formulate and address different aspects of the controller placement problem in a distributed network, specifically in an IoT scenario. Concretely, we develop a framework that deals with a series of controller placement problems from basic to complicated use cases. Corresponding to each use case, we provide discussion and a heuristic algorithm based on the submodularity concept. Finally, we present extensive simulations conducted on our framework. The simulation results show that our proposed algorithms can outperform considered baseline methods in terms of execution time, the number of controllers, and network latency.

Don't mind the gap: Bridging network-wide objectives and device-level configurations

We reflect on the historical context that lead to Propane, a high-level language and compiler to help network operators bridge the gap between network-wide routing objectives and low-level configurations of devices that run complex, distributed protocols. We also highlight the primary contributions that Propane made to the networking literature and describe ongoing challenges. We conclude with an important lesson learned from the experience.

Admixr-R package for reproducible analyses using ADMIXTOOLS.

SummaryWe present a new R package admixr, which provides a convenient interface for performing reproducible population genetic analyses (f3, D, f4, f4-ratio, qpWave and qpAdm), as implemented by command-line programs in the ADMIXTOOLS software suite. In a traditional ADMIXTOOLS workflow, the user must first generate a set of text configuration files tailored to each individual analysis, often using a combination of shell scripting and manual text editing. The non-tabular output files then need to be parsed to extract values of interest prior to further analyses. Our package simplifies this process by automating all low-level configuration and parsing steps, making analyses as simple as running a single R command. Furthermore, we provide a set of R functions for processing, filtering and manipulating datasets in the EIGENSTRAT format. By unifying all steps of the workflow under a single R framework, this package enables the automation of analytic pipelines, significantly improving the reproducibility of population genetic studies.Availability and implementationThe source code of the R package is available under the MIT license. Installation instructions, reference manual and a tutorial can be found on the package website at https://bioinf.eva.mpg.de/admixr.Supplementary information Supplementary data are available at Bioinformatics online.

Software-Defined WAN via Open APIs

Cloud applications require the ability to customize bandwidth and network policies for desired wide area network (WAN) connections. Unfortunately, currently widely used virtual private network is difficult to achieve such customization where sophisticated manual configurations and operator expertise are required. Software-defined network (SDN) creates the opportunities to provide this ability. In this paper, we design Grace, a SDN-based system to provide diverse connections with flexible bandwidth and customized policies, implementing WAN as a service. Our contributions can be listed as follows: 1) We introduce open APIs for customers by abstracting WAN connections based on connection types, bandwidth, latency sensitivity, and policy-related information; 2) We develop an effective conflict detection algorithm considering both resource reservation and safety guarantee; 3) We propose a linear programming -based bandwidth algorithm for latency-sensitive connections by dynamic scheduling of time and bandwidth, and design a pricing scheme for various connection demands to address the case that network cannot fulfill all the connection requests. A prototype implementation and extensive evaluations show that Grace provides the ability to customize WAN connections without policy conflicts, allocates required bandwidth optimally, translates them into low-level configurations for underlying network devices, and successfully deploys WAN in a short time.

Tempo

Multi-tenant database systems have a component called the Resource Manager, or RM that is responsible for allocating resources to tenants. RMs today do not provide direct support for performance objectives such as: "Average job response time of tenant A must be less than two minutes", or "No more than 5% of tenant B's jobs can miss the deadline of 1 hour." Thus, DBAs have to tinker with the RM's low-level configuration settings to meet such objectives. We propose a framework called Tempo that brings simplicity , self-tuning , and robustness to existing RMs. Tempo provides a simple interface for DBAs to specify performance objectives declaratively, and optimizes the RM configuration settings to meet these objectives. Tempo has a solid theoretical foundation which gives key robustness guarantees. We report experiments done on Tempo using production traces of data-processing workloads from companies such as Facebook and Cloudera. These experiments demonstrate significant improvements in meeting desired performance objectives over RM configuration settings specified by human experts.

Simulating Carsharing Operations through Agent-based Modelling: An Application to the City of Lisbon, Portugal

The increasing use of automobiles has had significant negative impacts on urban life: pollution, excessive energy use and time lost in traffic. The quick rise of auto externalities has policy makers facing the hard challenge of balancing demand for mobility on the one hand, and assuring sustainable urban life on the other. One strategy that can aid reducing these externalities is carsharing. Carsharing typically involves a fleet of vehicles in stations around a city, which clients may use on an hourly payment basis. Classical round-trip systems only address a niche market of urban trips such as shopping and errands, and few companies have risked the one-way carsharing option in the past due to vehicle stocks imbalance. Currently these systems are gaining new attention with important car builders investing in providing the one-way market in many cities in Europe and the United States. Nevertheless, there is still great uncertainty on the financial and economic viability of this type of carsharing. This results from a lack of realistic modeling tools that allow testing several operational parameters of this transportation alternative. In this paper we present a very detailed and realistic model to assess the potential of one way carsharing systems done through the use of agent based simulation. The simulation incorporates a stochastic demand model discretized in time and space and a detailed road network. It aims to assess the economic performance of the system both from the users’ perspective and the carsharing operator. The performance is a function of several planning and operational decisions which are included in the model: fleet size; station or free parking areas location decision; pricing policies. Some lower level configurations are also analyzed, such as an information system, vehicle reservation and maintenance operations scheduling. This paper focuses on the analysis of the spatial distribution and fleet size impacts on the demand for this transport option. This model was developed for the city of Lisbon, but its flexible architecture allows it to be adapted to different cities. Therefore this may become a tool that government authorities can use to rigorously estimate carsharing impacts and at the same time help private companies to manage their systems better.

An Adaptive Policy-Based Approach for Static and Dynamic Policy Conflict Detection: An Implementation of Location-Based System

The new paradigm in mobile services lies in providing a location-based user experience and here lies the tremendous scope for expansion of location-based services (LBS). One of the key approaches that have been recognized as an enabler of such a view is policy-based management, which has been mostly acknowledged as a methodology that provides adaptability and support to automatically assign network resources, control Quality of Service and security, by considering administratively specified rules. Whilst current research focuses mostly on rules for low-level device configuration, significant challenges remain to be addressed in order to provide dynamic adaptation of policy in response to changes and ensure that policies will lead to a feasible implementation within the managed environment. In this paper, the emphasis is on building the described environment using today's existing technologies. We use our policy-based framework PobMC to combine mobile (location-based) services, ubiquitous and autonomic computing concepts. The goal is to bring the kind of information rich environment, which is currently limited to computer screens, to the real world. This is achieved by constructing an architecture based on existing wireless network technologies and Web applications. Location information of destinations and users is computed by the received signal strength indicator (RSSI) values of the Wi-Fi sensors, to provide adaptive services over wireless Web. The users use their mobile devices to access these location-based services.