2,661 research outputs found
A Survey of Green Networking Research
Reduction of unnecessary energy consumption is becoming a major concern in
wired networking, because of the potential economical benefits and of its
expected environmental impact. These issues, usually referred to as "green
networking", relate to embedding energy-awareness in the design, in the devices
and in the protocols of networks. In this work, we first formulate a more
precise definition of the "green" attribute. We furthermore identify a few
paradigms that are the key enablers of energy-aware networking research. We
then overview the current state of the art and provide a taxonomy of the
relevant work, with a special focus on wired networking. At a high level, we
identify four branches of green networking research that stem from different
observations on the root causes of energy waste, namely (i) Adaptive Link Rate,
(ii) Interface proxying, (iii) Energy-aware infrastructures and (iv)
Energy-aware applications. In this work, we do not only explore specific
proposals pertaining to each of the above branches, but also offer a
perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate;
Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications.
18 pages, 6 figures, 2 table
Novel Approach for Job Offloading Technique in Mobile Cloud Computing
User preferences for computing have evolved as a result of recent advancements in mobile computing technologies. Mobile devices (SMDs) are still low potential computer platforms with capped memory sizes, CPU speeds, and battery life. Computationally heavy mobile apps may be run on SMDs thanks to Mobile Cloud Computing (MCC), which uses computational offloading. In both the business and academic worlds, there is rising interest in the mobile cloud computing is a new computing paradigm. Lack of a comprehensive experimental framework to use in their experiments and to evaluate their proposed work is a serious issue for mobile cloud computing researchers. Through MCC, mobile devices will be able to serve a wider variety of resource-intensive tasks while maintaining and growing their resource pools. In order to improve the mobile user experience, it places a high priority on improving energy efficiency, storage capacity, computational power, and data security. Since both the mobile device and the Cloud must determine energy-time trade-offs and decisions made on one side have an influence on the performance of the other, designing MCC systems is a challenging task. According to an examination of the MCC literature, all present models are centred on mobile devices, with the Cloud viewed as a system with infinite resources. Furthermore, no MCC-specific simulation tool is currently known to exist. To fill this need, we present in this study a Novel Approach for Job Offloading in Cloud Environments such as Google Cloud, using OCR application, while attempting to reduce energy use (Power). We are measuring the results of this experiment on both Cloud Computing and Mobile Device Computing.
 
VIoLET: A Large-scale Virtual Environment for Internet of Things
IoT deployments have been growing manifold, encompassing sensors, networks,
edge, fog and cloud resources. Despite the intense interest from researchers
and practitioners, most do not have access to large-scale IoT testbeds for
validation. Simulation environments that allow analytical modeling are a poor
substitute for evaluating software platforms or application workloads in
realistic computing environments. Here, we propose VIoLET, a virtual
environment for defining and launching large-scale IoT deployments within cloud
VMs. It offers a declarative model to specify container-based compute resources
that match the performance of the native edge, fog and cloud devices using
Docker. These can be inter-connected by complex topologies on which
private/public networks, and bandwidth and latency rules are enforced. Users
can configure synthetic sensors for data generation on these devices as well.
We validate VIoLET for deployments with > 400 devices and > 1500 device-cores,
and show that the virtual IoT environment closely matches the expected compute
and network performance at modest costs. This fills an important gap between
IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European
Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31,
2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for
presentation at the Plenary Session of the conferenc
Adaptive learning-based resource management strategy in fog-to-cloud
Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots
of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for
bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be
more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to
efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for
designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C.
F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of
heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling
them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for
different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements.
So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and
resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for
identifying and defining the taxonomic model for all the participating devices and system involved services-tasks.
In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and
colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key
ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical
information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous
monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system.
Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks
to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely
distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed
and designed a secure and distributed database framework for effectively and securely distribute the data over the network.
To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources.
Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The
prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the
selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an
advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML
techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a
massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and
designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve
an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the
initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança rà pidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informà tiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromÃs d’acostar les instal·lacions informà tiques a prop de la xarxa i també ajudar el sistema informà tic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informà tics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informà tica combinada, coordinada i jerà rquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les caracterÃstiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contÃnua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadÃstica. En particular, en tenir aquesta informació, es fa molt més fà cil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadÃstica per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automà tic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version
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