6,465 research outputs found
Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
As the backbone technology of machine learning, deep neural networks (DNNs)
have have quickly ascended to the spotlight. Running DNNs on
resource-constrained mobile devices is, however, by no means trivial, since it
incurs high performance and energy overhead. While offloading DNNs to the cloud
for execution suffers unpredictable performance, due to the uncontrolled long
wide-area network latency. To address these challenges, in this paper, we
propose Edgent, a collaborative and on-demand DNN co-inference framework with
device-edge synergy. Edgent pursues two design knobs: (1) DNN partitioning that
adaptively partitions DNN computation between device and edge, in order to
leverage hybrid computation resources in proximity for real-time DNN inference.
(2) DNN right-sizing that accelerates DNN inference through early-exit at a
proper intermediate DNN layer to further reduce the computation latency. The
prototype implementation and extensive evaluations based on Raspberry Pi
demonstrate Edgent's effectiveness in enabling on-demand low-latency edge
intelligence.Comment: ACM SIGCOMM Workshop on Mobile Edge Communications, Budapest,
Hungary, August 21-23, 2018. https://dl.acm.org/authorize?N66547
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
By deploying machine-learning algorithms at the network edge, edge learning
can leverage the enormous real-time data generated by billions of mobile
devices to train AI models, which enable intelligent mobile applications. In
this emerging research area, one key direction is to efficiently utilize radio
resources for wireless data acquisition to minimize the latency of executing a
learning task at an edge server. Along this direction, we consider the specific
problem of retransmission decision in each communication round to ensure both
reliability and quantity of those training data for accelerating model
convergence. To solve the problem, a new retransmission protocol called
data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Unlike the classic ARQ focusing merely on reliability, importance ARQ
selectively retransmits a data sample based on its uncertainty which helps
learning and can be measured using the model under training. Underpinning the
proposed protocol is a derived elegant communication-learning relation between
two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data
uncertainty. This relation facilitates the design of a simple threshold based
policy for importance ARQ. The policy is first derived based on the classic
classifier model of support vector machine (SVM), where the uncertainty of a
data sample is measured by its distance to the decision boundary. The policy is
then extended to the more complex model of convolutional neural networks (CNN)
where data uncertainty is measured by entropy. Extensive experiments have been
conducted for both the SVM and CNN using real datasets with balanced and
imbalanced distributions. Experimental results demonstrate that importance ARQ
effectively copes with channel fading and noise in wireless data acquisition to
achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2)
consideration of imbalanced classification in the experiments. Submitted to
IEEE Journal for possible publicatio
Building Internal Cloud at NIC : A Preview
The most of computing environments in the IT support organization like NIC
are designed to run in centralized datacentre. The centralized infrastructure
of various development projects are used to deploy their services on it and
connecting remotely to that datacentre from all the stations of organization.
Currently these servers are mostly underutilized due to the static and
conventional approaches used for accessing and utilizing of these resources.
The cloud patterns is much needful for optimizing resource utilization and
reducing the investments on unnecessary costs. So, we build up and prototyped a
private cloud system called nIC(NIC Internal Cloud) to leverage the benefits of
cloud environment. For this system we adopted the combination of various
techniques from open source software community. The user-base of nIC consists
developers, web and database admins, service providers and desktop users from
various projects in NIC. We can optimize the resource usage by customizing the
user based template services on these virtualized infrastructure. It will also
increases the flexibility of the managing and maintenance of the operations
like archiving, disaster recovery and scaling of resources. The open-source
approach is further decreases the enterprise costs. In this paper, we describe
the design and analysis of implementing issues on internal cloud environments
in NIC and similar organizations
Digital curation and the cloud
Digital curation involves a wide range of activities, many of which could benefit from cloud
deployment to a greater or lesser extent. These range from infrequent, resource-intensive tasks
which benefit from the ability to rapidly provision resources to day-to-day collaborative activities
which can be facilitated by networked cloud services. Associated benefits are offset by risks
such as loss of data or service level, legal and governance incompatibilities and transfer
bottlenecks. There is considerable variability across both risks and benefits according to the
service and deployment models being adopted and the context in which activities are
performed. Some risks, such as legal liabilities, are mitigated by the use of alternative, e.g.,
private cloud models, but this is typically at the expense of benefits such as resource elasticity
and economies of scale. Infrastructure as a Service model may provide a basis on which more
specialised software services may be provided.
There is considerable work to be done in helping institutions understand the cloud and its
associated costs, risks and benefits, and how these compare to their current working methods,
in order that the most beneficial uses of cloud technologies may be identified. Specific
proposals, echoing recent work coordinated by EPSRC and JISC are the development of
advisory, costing and brokering services to facilitate appropriate cloud deployments, the
exploration of opportunities for certifying or accrediting cloud preservation providers, and
the targeted publicity of outputs from pilot studies to the full range of stakeholders within the
curation lifecycle, including data creators and owners, repositories, institutional IT support
professionals and senior manager
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
The EnTrak system : supporting energy action planning via the Internet
Recent energy policy is designed to foster better energy efficiency and assist with the deployment of clean energy systems, especially those derived from renewable energy sources. To attain the envisaged targets will require action at all levels and effective collaboration between disparate groups (e.g. policy makers, developers, local authorities, energy managers, building designers, consumers etc) impacting on energy and environment. To support such actions and collaborations, an Internet-enabled energy information system called 'EnTrak' was developed. The aim was to provide decision-makers with information on energy demands, supplies and impacts by sector, time, fuel type and so on, in support of energy action plan formulation and enactment. This paper describes the system structure and capabilities of the EnTrak system
A JSON Token-Based Authentication and Access Management Schema for Cloud SaaS Applications
Cloud computing is significantly reshaping the computing industry built
around core concepts such as virtualization, processing power, connectivity and
elasticity to store and share IT resources via a broad network. It has emerged
as the key technology that unleashes the potency of Big Data, Internet of
Things, Mobile and Web Applications, and other related technologies, but it
also comes with its challenges - such as governance, security, and privacy.
This paper is focused on the security and privacy challenges of cloud computing
with specific reference to user authentication and access management for cloud
SaaS applications. The suggested model uses a framework that harnesses the
stateless and secure nature of JWT for client authentication and session
management. Furthermore, authorized access to protected cloud SaaS resources
have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component
and a Policy Activity Monitor (PAM) component have been introduced. In
addition, other subcomponents such as a Policy Validation Unit (PVU) and a
Policy Proxy DB (PPDB) have also been established for optimized service
delivery. A theoretical analysis of the proposed model portrays a system that
is secure, lightweight and highly scalable for improved cloud resource security
and management.Comment: 6 Page
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A Survey on Cloud Computing Security
Computation encounter the new approach of cloud computing which maybe keeps
the world and possibly can prepare all the human's necessities. In other words,
cloud computing is the subsequent regular step in the evolution of on-demand
information technology services and products. The Cloud is a metaphor for the
Internet and is a concept for the covered complicated infrastructure; it also
depends on sketching in computer network diagrams. In this paper we will focus
on concept of cloud computing, cloud deployment models, cloud security
challenges encryption and data protection, privacy and security and data
management and movement from grid to cloud
Internet of Cloud: Security and Privacy issues
The synergy between the cloud and the IoT has emerged largely due to the
cloud having attributes which directly benefit the IoT and enable its continued
growth. IoT adopting Cloud services has brought new security challenges. In
this book chapter, we pursue two main goals: 1) to analyse the different
components of Cloud computing and the IoT and 2) to present security and
privacy problems that these systems face. We thoroughly investigate current
security and privacy preservation solutions that exist in this area, with an
eye on the Industrial Internet of Things, discuss open issues and propose
future directionsComment: 27 pages, 4 figure
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