100,660 research outputs found
Cloud Network Slicing: A systematic mapping study from scientific publications
Cloud Network Slicing is a new research area that brings together cloud
computing and network slicing in an end-to-end environment. In this context,
understanding the existing scientific contributions and gaps is crucial to
driving new research in this field. This article presents a complete
quantitative analysis of scientific publications on the Cloud Network Slicing,
based on a systematic mapping study. The results indicate the situation of the
last ten years in the research area, presenting data such as industry
involvement, most cited articles, most active researchers, publications over
the years, main places of publication, as well as well-developed areas and
gaps. Future guidelines for scientific research are also discussed
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Reconfigurable Hardware Accelerators: Opportunities, Trends, and Challenges
With the emerging big data applications of Machine Learning, Speech
Recognition, Artificial Intelligence, and DNA Sequencing in recent years,
computer architecture research communities are facing the explosive scale of
various data explosion. To achieve high efficiency of data-intensive computing,
studies of heterogeneous accelerators which focus on latest applications, have
become a hot issue in computer architecture domain. At present, the
implementation of heterogeneous accelerators mainly relies on heterogeneous
computing units such as Application-specific Integrated Circuit (ASIC),
Graphics Processing Unit (GPU), and Field Programmable Gate Array (FPGA). Among
the typical heterogeneous architectures above, FPGA-based reconfigurable
accelerators have two merits as follows: First, FPGA architecture contains a
large number of reconfigurable circuits, which satisfy requirements of high
performance and low power consumption when specific applications are running.
Second, the reconfigurable architectures of employing FPGA performs prototype
systems rapidly and features excellent customizability and reconfigurability.
Nowadays, in top-tier conferences of computer architecture, emerging a batch of
accelerating works based on FPGA or other reconfigurable architectures. To
better review the related work of reconfigurable computing accelerators
recently, this survey reserves latest high-level research products of
reconfigurable accelerator architectures and algorithm applications as the
basis. In this survey, we compare hot research issues and concern domains,
furthermore, analyze and illuminate advantages, disadvantages, and challenges
of reconfigurable accelerators. In the end, we prospect the development
tendency of accelerator architectures in the future, hoping to provide a
reference for computer architecture researchers
Software Defined Optical Networks (SDONs): A Comprehensive Survey
The emerging Software Defined Networking (SDN) paradigm separates the data
plane from the control plane and centralizes network control in an SDN
controller. Applications interact with controllers to implement network
services, such as network transport with Quality of Service (QoS). SDN
facilitates the virtualization of network functions so that multiple virtual
networks can operate over a given installed physical network infrastructure.
Due to the specific characteristics of optical (photonic) communication
components and the high optical transmission capacities, SDN based optical
networking poses particular challenges, but holds also great potential. In this
article, we comprehensively survey studies that examine the SDN paradigm in
optical networks; in brief, we survey the area of Software Defined Optical
Networks (SDONs). We mainly organize the SDON studies into studies focused on
the infrastructure layer, the control layer, and the application layer.
Moreover, we cover SDON studies focused on network virtualization, as well as
SDON studies focused on the orchestration of multilayer and multidomain
networking. Based on the survey, we identify open challenges for SDONs and
outline future directions
A Survey of Methods For Analyzing and Improving GPU Energy Efficiency
Recent years have witnessed a phenomenal growth in the computational
capabilities and applications of GPUs. However, this trend has also led to
dramatic increase in their power consumption. This paper surveys research works
on analyzing and improving energy efficiency of GPUs. It also provides a
classification of these techniques on the basis of their main research idea.
Further, it attempts to synthesize research works which compare energy
efficiency of GPUs with other computing systems, e.g. FPGAs and CPUs. The aim
of this survey is to provide researchers with knowledge of state-of-the-art in
GPU power management and motivate them to architect highly energy-efficient
GPUs of tomorrow.Comment: Accepted with minor revision in ACM Computing Survey Journal (impact
factor 3.85, five year impact of 7.85
A Comprehensive Survey of Interface Protocols for Software Defined Networks
Software Defined Networks has seen tremendous growth and deployment in
different types of networks. Compared to traditional networks it decouples the
control logic from network layer devices, and centralizes it for efficient
traffic forwarding and flow management across the domain. This multi-layered
architecture has data forwarding devices at the bottom in data plane, which are
programmed by controllers in the control plane. The high level application or
management plane interacts with control layer to program the whole network and
enforce different policies. The interaction among these layers is done through
interfaces which work as communication/programming protocols. In this survey,
we present a comprehensive study of such interfaces available for southbound,
northbound, and east/westbound communication. We have classified each type into
different categories based on their properties and capabilities. Virtualization
of networks devices is a common practice in Software Defined Networks. This
paper also analyzes interface solution which work with different virtualization
schemes. In addition, the paper highlights a number of short term and long term
research challenges and open issues related to SDN interfaces.Comment: Version 0.51. An advanced version is under revie
From 4G to 5G: Self-organized Network Management meets Machine Learning
In this paper, we provide an analysis of self-organized network management,
with an end-to-end perspective of the network. Self-organization as applied to
cellular networks is usually referred to Self-organizing Networks (SONs), and
it is a key driver for improving Operations, Administration, and Maintenance
(OAM) activities. SON aims at reducing the cost of installation and management
of 4G and future 5G networks, by simplifying operational tasks through the
capability to configure, optimize and heal itself. To satisfy 5G network
management requirements, this autonomous management vision has to be extended
to the end to end network. In literature and also in some instances of products
available in the market, Machine Learning (ML) has been identified as the key
tool to implement autonomous adaptability and take advantage of experience when
making decisions. In this paper, we survey how network management can
significantly benefit from ML solutions. We review and provide the basic
concepts and taxonomy for SON, network management and ML. We analyse the
available state of the art in the literature, standardization, and in the
market. We pay special attention to 3rd Generation Partnership Project (3GPP)
evolution in the area of network management and to the data that can be
extracted from 3GPP networks, in order to gain knowledge and experience in how
the network is working, and improve network performance in a proactive way.
Finally, we go through the main challenges associated with this line of
research, in both 4G and in what 5G is getting designed, while identifying new
directions for research.Comment: 23 pages, 3 figures, Surve
A Method for Ontology-based Architecture Reconstruction of Computing Platforms
Today's ubiquitous computing ecosystem involves various kinds of hardware and
software technologies for different computing environments. As the result,
computing systems can be seen as integrated system of hardware and software
systems. Realizing such complex systems is crucial for providing safety,
security, and maintenance. This is while the characterization of computing
systems is not possible without a systematic procedure for enumerating
different components and their structural/behavioral relationships.
Architecture Reconstruction (AR) is a practice defined in the domain of
software engineering for the realization of a specific software component.
However, it is not applicable to a whole system (including HW/SW). Inspired by
Symphony AR framework, we have proposed a generalized method to reconstruct the
architecture of a computing platform at HW/SW boundary. In order to cover
diverge set of existing HW/SW technologies, our method uses an ontology-based
approach to handle these complexities. Due to the lack of a comprehensive
accurate ontology in the literature, we have developed our own ontology --
called PLATOnt -- which is shown to be more effective by ONTOQA evaluation
framework. We have used our AR method in two use case scenarios to reconstruct
the architecture of ARM-based Trusted execution environment and a Raspberry-pi
platform have extensive application in embedded systems and IoT devices
The Role of Big Data Analytics in Industrial Internet of Things
Big data production in industrial Internet of Things (IIoT) is evident due to
the massive deployment of sensors and Internet of Things (IoT) devices.
However, big data processing is challenging due to limited computational,
networking and storage resources at IoT device-end. Big data analytics (BDA) is
expected to provide operational- and customer-level intelligence in IIoT
systems. Although numerous studies on IIoT and BDA exist, only a few studies
have explored the convergence of the two paradigms. In this study, we
investigate the recent BDA technologies, algorithms and techniques that can
lead to the development of intelligent IIoT systems. We devise a taxonomy by
classifying and categorising the literature on the basis of important
parameters (e.g. data sources, analytics tools, analytics techniques,
requirements, industrial analytics applications and analytics types). We
present the frameworks and case studies of the various enterprises that have
benefited from BDA. We also enumerate the considerable opportunities introduced
by BDA in IIoT.We identify and discuss the indispensable challenges that remain
to be addressed as future research directions as well
Data Management in Industry 4.0: State of the Art and Open Challenges
Information and communication technologies are permeating all aspects of
industrial and manufacturing systems, expediting the generation of large
volumes of industrial data. This article surveys the recent literature on data
management as it applies to networked industrial environments and identifies
several open research challenges for the future. As a first step, we extract
important data properties (volume, variety, traffic, criticality) and identify
the corresponding data enabling technologies of diverse fundamental industrial
use cases, based on practical applications. Secondly, we provide a detailed
outline of recent industrial architectural designs with respect to their data
management philosophy (data presence, data coordination, data computation) and
the extent of their distributiveness. Then, we conduct a holistic survey of the
recent literature from which we derive a taxonomy of the latest advances on
industrial data enabling technologies and data centric services, spanning all
the way from the field level deep in the physical deployments, up to the cloud
and applications level. Finally, motivated by the rich conclusions of this
critical analysis, we identify interesting open challenges for future research.
The concepts presented in this article thematically cover the largest part of
the industrial automation pyramid layers. Our approach is multidisciplinary, as
the selected publications were drawn from two fields; the communications,
networking and computation field as well as the industrial, manufacturing and
automation field. The article can help the readers to deeply understand how
data management is currently applied in networked industrial environments, and
select interesting open research opportunities to pursue
- …