QAware: A Cross-Layer Approach to MPTCP Scheduling

Multipath TCP (MPTCP) allows applications to transparently use all available network interfaces by creating a TCP subflow per interface. One critical component of MPTCP is the scheduler that decides which subflow to use for each packet. Existing schedulers typically use estimates of end-to-end path properties, such as delay and bandwidth, for making the scheduling decisions. In this paper, we show that these scheduling decisions can be significantly improved by incorporating readily available local information from the device driver queues in the decision-making process. We propose QAware, a novel cross-layer approach for MPTCP scheduling. QAware combines end-to-end delay estimates with local queue buffer occupancy information and allows for a better and faster adaptation to the network conditions. This results in more efficient use of the available resources and considerable gains in aggregate throughput. We present the design of QAware and evaluate its performance through simulations, and also through real experiments, comparing it to existing schedulers. Our results show that QAware performs significantly better than other available approaches for various use-cases and applications.Comment: in Proceedings of IFIP Networking 2018, 2018 available at: https://files.ifi.uzh.ch/stiller/IFIP%20Networking%202018-Proceedings.pd

Dynamic Server Selection by Using a Client Side Composite DNS-Metric

Dynamic Server Selection (DSS) is a new DNS method for the optimal server selection of a multiple available network service. The method allows dynamic selection of a server on the client side based on the information of the server load and its network topological distance from the client. The server selection is based on the calculations of a composite DNS-metric in which servers, whose IP addresses are sent in a DNS response, are ranked from the optimal to the least suitable. Calculation parameters are server response time, which the client measures for each server independently, and the server load, which is specified by the server administrator. The DSS method has the lowest overall network service response time in comparison with the other four observed methods (Geographical, Hops, Random and RTT) which, in measurements done in a real time environment, have longer response time from 8.5% to 26.8% compared to DSS

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Coordinated Multicast/Unicast Transmission on 5G: A Novel Approach for Linear Broadcasting

Linear broadcasting services, with a scheduled programming, constitute a paramount tel-ecommunication service for today’s society. Although the existing technology is mature, current linear broadcast systems have serious limitations when providing service to moving users or users placed in areas with complex orography and poor signal quality. To over-come these limitations, 3GPP 5G standard has included a work item to support 5G mul-ticast/broadcast services for future Release 17. This paper investigates the integration of point-to-point (unicast) communication with cellular multicast/broadcast on 5G technology to extend the current support of linear broadcasting services. This integration relies on the use mobile edge computing (MEC) at the 5G base station (gNB) to host a dynamic adap-tive streaming over HTTP (DASH) server that is coordinated with the multicast transmis-sion to complement the broadcast service. This approach join the reliability of point-to-point communications, with dedicated resources for each user, with the spectrum efficiency of multi-cast communications, where a set of users share common resources. The coopera-tion between those unicast and multicast schemes allows those users whose coverage is not good enough, to complete the linear broadcast flow through the point-to-point transmission via MEC. The benefits of such approach have been assessed with simulations in a realistic scenario that considers a vehicle moving across a sparsely populated region in southern Spain. Results reveals that throughput and bitrate playback (reproduction rate) are greatly improved when unicast/multicast integration is enabled since the number of stalling events is reduced significantly.This work has been partially supported by Radio Televisión Española through Impulsa Visión RTVE grant and by the Universidad de Málaga. We are grateful to Pere Vila, Esteban Mayoral Campos, Adolfo Muñoz Berrón and Miguel Ángel Bona San Vicente for their support and collaboration during the development of the project. Funding for open access charge: Universidad de Málaga / CBU

QoE on media deliveriy in 5G environments

231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor