5,213 research outputs found
IBM Deep Learning Service
Deep learning driven by large neural network models is overtaking traditional
machine learning methods for understanding unstructured and perceptual data
domains such as speech, text, and vision. At the same time, the
"as-a-Service"-based business model on the cloud is fundamentally transforming
the information technology industry. These two trends: deep learning, and
"as-a-service" are colliding to give rise to a new business model for cognitive
application delivery: deep learning as a service in the cloud. In this paper,
we will discuss the details of the software architecture behind IBM's deep
learning as a service (DLaaS). DLaaS provides developers the flexibility to use
popular deep learning libraries such as Caffe, Torch and TensorFlow, in the
cloud in a scalable and resilient manner with minimal effort. The platform uses
a distribution and orchestration layer that facilitates learning from a large
amount of data in a reasonable amount of time across compute nodes. A resource
provisioning layer enables flexible job management on heterogeneous resources,
such as graphics processing units (GPUs) and central processing units (CPUs),
in an infrastructure as a service (IaaS) cloud
Programming Cloud Resource Orchestration Framework: Operations and Research Challenges
The emergence of cloud computing over the past five years is potentially one
of the breakthrough advances in the history of computing. It delivers hardware
and software resources as virtualization-enabled services and in which
administrators are free from the burden of worrying about the low level
implementation or system administration details. Although cloud computing
offers considerable opportunities for the users (e.g. application developers,
governments, new startups, administrators, consultants, scientists, business
analyst, etc.) such as no up-front investment, lowering operating cost, and
infinite scalability, it has many unique research challenges that need to be
carefully addressed in the future. In this paper, we present a survey on key
cloud computing concepts, resource abstractions, and programming operations for
orchestrating resources and associated research challenges, wherever
applicable.Comment: 19 page
Big Data Computing Using Cloud-Based Technologies, Challenges and Future Perspectives
The excessive amounts of data generated by devices and Internet-based sources
at a regular basis constitute, big data. This data can be processed and
analyzed to develop useful applications for specific domains. Several
mathematical and data analytics techniques have found use in this sphere. This
has given rise to the development of computing models and tools for big data
computing. However, the storage and processing requirements are overwhelming
for traditional systems and technologies. Therefore, there is a need for
infrastructures that can adjust the storage and processing capability in
accordance with the changing data dimensions. Cloud Computing serves as a
potential solution to this problem. However, big data computing in the cloud
has its own set of challenges and research issues. This chapter surveys the big
data concept, discusses the mathematical and data analytics techniques that can
be used for big data and gives taxonomy of the existing tools, frameworks and
platforms available for different big data computing models. Besides this, it
also evaluates the viability of cloud-based big data computing, examines
existing challenges and opportunities, and provides future research directions
in this field
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur
An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms
Deep learning (DL) has recently achieved tremendous success in a variety of
cutting-edge applications, e.g., image recognition, speech and natural language
processing, and autonomous driving. Besides the available big data and hardware
evolution, DL frameworks and platforms play a key role to catalyze the
research, development, and deployment of DL intelligent solutions. However, the
difference in computation paradigm, architecture design and implementation of
existing DL frameworks and platforms brings challenges for DL software
development, deployment, maintenance, and migration. Up to the present, it
still lacks a comprehensive study on how current diverse DL frameworks and
platforms influence the DL software development process.
In this paper, we initiate the first step towards the investigation on how
existing state-of-the-art DL frameworks (i.e., TensorFlow, Theano, and Torch)
and platforms (i.e., server/desktop, web, and mobile) support the DL software
development activities. We perform an in-depth and comparative evaluation on
metrics such as learning accuracy, DL model size, robustness, and performance,
on state-of-the-art DL frameworks across platforms using two popular datasets
MNIST and CIFAR-10. Our study reveals that existing DL frameworks still suffer
from compatibility issues, which becomes even more severe when it comes to
different platforms. We pinpoint the current challenges and opportunities
towards developing high quality and compatible DL systems. To ignite further
investigation along this direction to address urgent industrial demands of
intelligent solutions, we make all of our assembled feasible toolchain and
dataset publicly available
A Survey on Geographically Distributed Big-Data Processing using MapReduce
Hadoop and Spark are widely used distributed processing frameworks for
large-scale data processing in an efficient and fault-tolerant manner on
private or public clouds. These big-data processing systems are extensively
used by many industries, e.g., Google, Facebook, and Amazon, for solving a
large class of problems, e.g., search, clustering, log analysis, different
types of join operations, matrix multiplication, pattern matching, and social
network analysis. However, all these popular systems have a major drawback in
terms of locally distributed computations, which prevent them in implementing
geographically distributed data processing. The increasing amount of
geographically distributed massive data is pushing industries and academia to
rethink the current big-data processing systems. The novel frameworks, which
will be beyond state-of-the-art architectures and technologies involved in the
current system, are expected to process geographically distributed data at
their locations without moving entire raw datasets to a single location. In
this paper, we investigate and discuss challenges and requirements in designing
geographically distributed data processing frameworks and protocols. We
classify and study batch processing (MapReduce-based systems), stream
processing (Spark-based systems), and SQL-style processing geo-distributed
frameworks, models, and algorithms with their overhead issues.Comment: IEEE Transactions on Big Data; Accepted June 2017. 20 page
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 ISTI Rapid Response on Exploring Cloud Computing 2018
This report describes eighteen projects that explored how commercial cloud
computing services can be utilized for scientific computation at national
laboratories. These demonstrations ranged from deploying proprietary software
in a cloud environment to leveraging established cloud-based analytics
workflows for processing scientific datasets. By and large, the projects were
successful and collectively they suggest that cloud computing can be a valuable
computational resource for scientific computation at national laboratories
Machine Learning Systems for Intelligent Services in the IoT: A Survey
Machine learning (ML) technologies are emerging in the Internet of Things
(IoT) to provision intelligent services. This survey moves beyond existing ML
algorithms and cloud-driven design to investigate the less-explored systems,
scaling and socio-technical aspects for consolidating ML and IoT. It covers the
latest developments (up to 2020) on scaling and distributing ML across cloud,
edge, and IoT devices. With a multi-layered framework to classify and
illuminate system design choices, this survey exposes fundamental concerns of
developing and deploying ML systems in the rising cloud-edge-device continuum
in terms of functionality, stakeholder alignment and trustworthiness.Comment: Requires rewor
Serverless Computing: Current Trends and Open Problems
Serverless computing has emerged as a new compelling paradigm for the
deployment of applications and services. It represents an evolution of cloud
programming models, abstractions, and platforms, and is a testament to the
maturity and wide adoption of cloud technologies. In this chapter, we survey
existing serverless platforms from industry, academia, and open source
projects, identify key characteristics and use cases, and describe technical
challenges and open problems
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