574 research outputs found
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Serving deep learning models in a serverless platform
Serverless computing has emerged as a compelling paradigm for the development
and deployment of a wide range of event based cloud applications. At the same
time, cloud providers and enterprise companies are heavily adopting machine
learning and Artificial Intelligence to either differentiate themselves, or
provide their customers with value added services. In this work we evaluate the
suitability of a serverless computing environment for the inferencing of large
neural network models. Our experimental evaluations are executed on the AWS
Lambda environment using the MxNet deep learning framework. Our experimental
results show that while the inferencing latency can be within an acceptable
range, longer delays due to cold starts can skew the latency distribution and
hence risk violating more stringent SLAs
Big Data Analytics for Smart Cities: The H2020 CLASS Project
Applying big-data technologies to field applications has resulted in several new needs. First, processing data across a
compute continuum spanning from cloud to edge to devices, with varying capacity, architecture etc. Second, some computations need to be made predictable (real-time response), thus supporting both data-in-motion processing and larger-scale data-at-rest processing. Last, employing an event-driven programming model that supports mixing different APIs and models, such as Map/Reduce, CEP, sequential code, etc.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CLASS Project (www.class-project.eu), grant agreement No. 780622.Peer ReviewedPostprint (author's final draft
Reproducible and Portable Big Data Analytics in the Cloud
Cloud computing has become a major approach to help reproduce computational
experiments because it supports on-demand hardware and software resource
provisioning. Yet there are still two main difficulties in reproducing big data
applications in the cloud. The first is how to automate end-to-end execution of
analytics including environment provisioning, analytics pipeline description,
pipeline execution, and resource termination. The second is that an application
developed for one cloud is difficult to be reproduced in another cloud, a.k.a.
vendor lock-in problem. To tackle these problems, we leverage serverless
computing and containerization techniques for automated scalable execution and
reproducibility, and utilize the adapter design pattern to enable application
portability and reproducibility across different clouds. We propose and develop
an open-source toolkit that supports 1) fully automated end-to-end execution
and reproduction via a single command, 2) automated data and configuration
storage for each execution, 3) flexible client modes based on user preferences,
4) execution history query, and 5) simple reproduction of existing executions
in the same environment or a different environment. We did extensive
experiments on both AWS and Azure using four big data analytics applications
that run on virtual CPU/GPU clusters. The experiments show our toolkit can
achieve good execution performance, scalability, and efficient reproducibility
for cloud-based big data analytics
TAXONOMY OF SECURITY AND PRIVACY ISSUES IN SERVERLESS COMPUTING
The advent of cloud computing has led to a new era of computer usage. Networking and physical security are some of the IT infrastructure concerns that IT administrators around the world had to worry about for their individual environments. Cloud computing took away that burden and redefined the meaning of IT administrators. Serverless computing as it relates to secure software development is creating the same kind of change. Developers can quickly spin up a secure development environment in a matter of minutes without having to worry about any of the underlying infrastructure setups. In the paper, we will look at the merits and demerits of serverless computing, what is drawing the demand for serverless computing among developers, the security and privacy issues of serverless technology, and detail the parameters to consider when setting up and using a secure development environment based on serverless computin
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