772 research outputs found
Edge/Fog Computing Technologies for IoT Infrastructure
The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies
Network Service Orchestration: A Survey
Business models of network service providers are undergoing an evolving
transformation fueled by vertical customer demands and technological advances
such as 5G, Software Defined Networking~(SDN), and Network Function
Virtualization~(NFV). Emerging scenarios call for agile network services
consuming network, storage, and compute resources across heterogeneous
infrastructures and administrative domains. Coordinating resource control and
service creation across interconnected domains and diverse technologies becomes
a grand challenge. Research and development efforts are being devoted to
enabling orchestration processes to automate, coordinate, and manage the
deployment and operation of network services. In this survey, we delve into the
topic of Network Service Orchestration~(NSO) by reviewing the historical
background, relevant research projects, enabling technologies, and
standardization activities. We define key concepts and propose a taxonomy of
NSO approaches and solutions to pave the way towards a common understanding of
the various ongoing efforts around the realization of diverse NSO application
scenarios. Based on the analysis of the state of affairs, we present a series
of open challenges and research opportunities, altogether contributing to a
timely and comprehensive survey on the vibrant and strategic topic of network
service orchestration.Comment: Accepted for publication at Computer Communications Journa
Corpus for development of routing algorithms in opportunistic networks
We have designed a collection of scenarios, a corpus, for its use in the study and development of routing algorithms for opportunistic networks. To obtain these scenarios, we have followed a methodology based on characterizing the space and choosing the best exemplary items in such a way that the corpus as a whole was representative of all possible scenarios. Until now, research in this area was using some sets of non-standard network traces that made it difficult to evaluate algorithms and perform fair comparisons between them. These developments were hard to assess in an objective way, and were prone to introduce unintentional biases that directly affected the quality of the research. Our contribution is more than a collection of scenarios; our corpus provides a fine collection of network behaviors that suit the development of routing algorithms, specifically in evaluating and comparing them. If the scientific community embraces this corpus, the community will have a global-agreed methodology where the validity of results would not be limited to specific scenarios or network conditions, thus avoiding self-produced evaluation setups, availability problems and selection bias, and saving time. New research in the area will be able to validate the routing algorithms already published. It will also be possible to identify the scenarios better suit specific purposes, and results will be easily verified. The corpus is available free to download and use
AI gym for Networks
5G Networks are delivering better services and connecting more devices, but at the same
time are becoming more complex.
Problems like resource management and control optimization are increasingly dynamic
and difficult to model making it very hard to use traditional model-based optimization
techniques. Artificial Intelligence (AI) explores techniques such as Deep Reinforcement
Learning (DRL), which uses the interaction between the agent and the environment to
learn what action to take to obtain the best possible result.
Researchers usually need to create and develop a simulation environment for their
scenario of interest to be able to experiment with DRL algorithms. This takes a large
amount of time from the research process, while the lack of a common environment
makes it difficult to compare algorithms.
The proposed solution aims to fill this gap by creating a tool that facilitates the setting
up of DRL training environments for network scenarios. The developed tool uses three
open source software, the Containernet to simulate the connections between devices, the
Ryu Controller as the Software Defined Network Controller, and OpenAI Gym which
is responsible for setting up the communication between the environment and the DRL
agent.
With the project developed during the thesis, the users will be capable of creating
more scenarios in a short period, opening space to set up different environments, solving
various problems as well as providing a common environment where other Agents can
be compared.
The developed software is used to compare the performance of several DRL agents in
two different network control problems: routing and network slice admission control. A
novel DRL based solution is used in the case of network slice admission control that jointly
optimizes the admission and the placement of traffic of a network slice in the physical
resources.As redes 5G oferecem melhores serviços e conectam mais dispositivos, fazendo com que
se tornem mais complexas e difíceis de gerir.
Problemas como a gestão de recursos e a otimização de controlo são cada vez mais
dinâmicos e difíceis de modelar, o que torna difícil usar soluções de optimização basea-
das em modelos tradicionais. A Inteligência Artificial (IA) explora técnicas como Deep
Reinforcement Learning que utiliza a interação entre o agente e o ambiente para aprender
qual a ação a ter para obter o melhor resultado possível.
Normalmente, os investigadores precisam de criar e desenvolver um ambiente de
simulação para poder estudar os algoritmos DRL e a sua interação com o cenário de
interesse. A criação de ambientes a partir do zero retira tempo indispensável para a
pesquisa em si, e a falta de ambientes de treino comuns torna difícil a comparação dos
algoritmos.
A solução proposta foca-se em preencher esta lacuna criando uma ferramenta que
facilite a configuração de ambientes de treino DRL para cenários de rede. A ferramenta
desenvolvida utiliza três softwares open source, o Containernet para simular as conexões
entre os dispositivos, o Ryu Controller como Software Defined Network Controller e o
OpenAI Gym que é responsável por configurar a comunicação entre o ambiente e o agente
DRL.
Através do projeto desenvolvido, os utilizadores serão capazes de criar mais cenários
em um curto período, abrindo espaço para configurar diferentes ambientes e resolver
diferentes problemas, bem como fornecer um ambiente comum onde diferentes Agentes
podem ser comparados.
O software desenvolvido foi usado para comparar o desempenho de vários agentes
DRL em dois problemas diferentes de controlo de rede, nomeadamente, roteamento e
controlo de admissão de slices na rede. Uma solução baseada em DRL é usada no caso
do controlo de admissão de slices na rede que otimiza conjuntamente a admissão e a
colocação de tráfego de uma slice na rede nos recursos físicos da mesma
Proceedings Work-In-Progress Session of the 13th Real-Time and Embedded Technology and Applications Symposium
The Work-In-Progress session of the 13th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u2707) presents papers describing contributions both to state of the art and state of the practice in the broad field of real-time and embedded systems. The 17 accepted papers were selected from 19 submissions. This proceedings is also available as Washington University in St. Louis Technical Report WUCSE-2007-17, at http://www.cse.seas.wustl.edu/Research/FileDownload.asp?733. Special thanks go to the General Chairs – Steve Goddard and Steve Liu and Program Chairs - Scott Brandt and Frank Mueller for their support and guidance
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