Evaluation of Active Queue Management (AQM) Models in Low Latency Networks

Abstract: Low latency networks require the modification of the actual queuing management in order to avoid large queuing delay. Nowadays, TCP’s congestion control maximizes the throughput of the link providing benefits to large flow packets. However, nodes’ buffers may get fully filled, which would produce large time delays and packet dropping situations, named as bufferbloat problem. For actual time-sensitive applications demand, such as VoIP, online gaming or financial trading, these queueing times cause bad quality of service being directly noticed in user’s utilization. This work studies the different alternatives for active queue management (AQM) in the nodes links, optimizing the latency of the small flow packets and, therefore, providing better quality for low latency networks in congestion scenarios. AQM models are simulated in a dumbbell topology with ns3 software, which shows the diverse latency values (measured in RTT) according to network situations and the algorithm that has been installed. In detail, RED, CoDel, PIE, and FQ_CoDel algorithms are studied, plus the modification of the TCP sender’s congestion control with Alternative Backoff with ECN (ABE) algorithm. The simulations will display the best queueing times for the implementation that mixes FQ_CoDel with ABE, the one which maximizes the throughput reducing the latency of the packets. Thus, the modification of queueing management with FQ_CoDel and the implementation of ABE in the sender will solve the bufferbloat problem offering the required quality for low latency networks.Resumen Las redes de baja latencia requieren la modificación de la actual gestión de las colas con el fin de eludir los extensos tiempos de retardo. Hoy en d´ıa, el control de congestión de TCP maximiza el rendimiento (throughput) del enlace otorgando beneficio a los grandes flujos de datos, sin embargo, los buffers son plenamente cargados generando altos tiempos de retardo y fases de retirada de paquetes, llamada a esta situación el problema de Bufferbloat. Par las aplicaciones contempor´aneas como las llamadas VoIP, los juegos on-line o los intercambios financieros; estos tiempos de cola generan una mala calidad de servicio detectada directamente por los usuarios finales. Este trabajo estudia las diferentes alternativas de la gestión activa de colas (AQM), optimizando la latencia de los peque˜nos flujos y, por lo tanto, brindando una mejor calidad para las redes de baja latencia en situaciones de congestión. Los modelos AQM han sido evaluados en una topolog´ıa ’dumbbell’ mediante el simulador ns3, entregando resultados de latencia (medidos en RTT) de acuerdo con la situación del enlace y el algoritmo instalado en la cola. Concretamente, los algoritmos estudiados han sido RED, CoDel, PIE y FQ_CoDel; adem´as de la modificación del control de congestión TCP del emisor denominada ABE (Alternative Backoff with ECN). Las simulaciones que mejor resultados ofrecen son las que implementan combinación de FQ_CoDel con el algoritmo ABE, maximizando el rendimiento y reduciendo la latencia de los paquetes. Por lo tanto, la modificación con FQ_CoDel en las colas y la de ABE en el emisor ofrecen una solución al problema del Bufferbloat altamente solicitada por las redes de baja latencia

Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool. The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction

Toward Optimal Resource Allocation of Virtualized Network Functions for Hierarchical Datacenters

Telecommunications service providers (TSPs) previously provided network functions to end users with dedicated hardware, but they are resorting to virtualized infrastructure for reducing costs and increasing flexibility in resource allocation. A representative case is the Central Office Re-architected as Datacenter (CORD) project from AT&T, which aims to deploy virtualized network functions (VNFs) to over 4000 central offices (COs) across the U.S. However, there is a wide spectrum of options for deploying VNFs over the COs, varying from highly distributed to highly centralized manners. The former benefits end users with short response time but has its inherent limitation on utilizing geographically dispersed resources, while the latter allows resources to be better utilized at a cost of longer response time. In this work, we model the TSP's virtualized infrastructure as hierarchical datacenters, namely hierarchical CORD, and provide a resource allocation solution to strike the optimal balance between the two extreme options. Our evaluations reveal that in general, the 3-tier architecture incurs the least cost in case of deploying VNFs under moderate or loose delay constraints. Furthermore, the margin of improvement on the resource allocation cost increases inversely with the overall system utilization rate. Our results also suggest that as heavy request load overwhelms the network infrastructure, the relevant VNFs shall be migrated to lower-tier edge datacenters or to some nearby datacenters with superior network capacity. The evaluations also demonstrate that the proposed model allows highly adaptive VNF deployment in the hierarchical architecture under various conditions.This work was supported in part by H2020 Collaborative Europe/Taiwan Research Project 5G-CORAL under Grant 761586, and in part by the Ministry of Science and Technology, Taiwan, under Grant MOST-106-2218-E-009-018 and Grant MOST-106-2221-E-194-021-MY3

Design of a parallel vector access unit for SDRAM memory systems

Journal ArticleParallel Vector Access is a technique that exploits the regularity of vector or stream accesses to perform them efficiently in parallel on a multi-bank memory system. The performance of applications that have vector accesses may be improved using a memory controller that performs scatter/gather operations so that only the vector or stream elements that are accessed by the application are transmitted across the system bus. These scatter/gather operations can be speeded up by broadcasting vector operations to all banks of memory in parallel, each of which implements an algorithm to determine which elements of the requested vector they contain. This thesis presents the mathematical foundations behind one such algorithm for controller are investigated. The the performance of such a memory controller on vector kernels is studied by gate level simulation and the results analyzed. Because of the parallel approach, the PVA is able to load elements up to 32.8 times faster than a conventional memory system and 3.3 times faster than a pipelined vector unit, without hurting normal cache line fill performance

Programmation sûre de plates-formes embarquées de type multi/pluri-cœurs

The purpose of this document is to describe an overview of my work on the topic of "programming mutli/many-core COTS in the context of aeronautics" and to propose future research work.L’objectif de ce document est de décrire une synthèse des travaux que j’ai menés autour du thème de "la programmation sûre de plates-formes embarquées" et de proposer des perspectives de recherche pour les années à venir

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems