7,805 research outputs found
ClouNS - A Cloud-native Application Reference Model for Enterprise Architects
The capability to operate cloud-native applications can generate enormous
business growth and value. But enterprise architects should be aware that
cloud-native applications are vulnerable to vendor lock-in. We investigated
cloud-native application design principles, public cloud service providers, and
industrial cloud standards. All results indicate that most cloud service
categories seem to foster vendor lock-in situations which might be especially
problematic for enterprise architectures. This might sound disillusioning at
first. However, we present a reference model for cloud-native applications that
relies only on a small subset of well standardized IaaS services. The reference
model can be used for codifying cloud technologies. It can guide technology
identification, classification, adoption, research and development processes
for cloud-native application and for vendor lock-in aware enterprise
architecture engineering methodologies
Cloudbus Toolkit for Market-Oriented Cloud Computing
This keynote paper: (1) presents the 21st century vision of computing and
identifies various IT paradigms promising to deliver computing as a utility;
(2) defines the architecture for creating market-oriented Clouds and computing
atmosphere by leveraging technologies such as virtual machines; (3) provides
thoughts on market-based resource management strategies that encompass both
customer-driven service management and computational risk management to sustain
SLA-oriented resource allocation; (4) presents the work carried out as part of
our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a
Service software system containing SDK (Software Development Kit) for
construction of Cloud applications and deployment on private or public Clouds,
in addition to supporting market-oriented resource management; (ii)
internetworking of Clouds for dynamic creation of federated computing
environments for scaling of elastic applications; (iii) creation of 3rd party
Cloud brokering services for building content delivery networks and e-Science
applications and their deployment on capabilities of IaaS providers such as
Amazon along with Grid mashups; (iv) CloudSim supporting modelling and
simulation of Clouds for performance studies; (v) Energy Efficient Resource
Allocation Mechanisms and Techniques for creation and management of Green
Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape
Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster
The NEMO High Performance Computing Cluster at the University of Freiburg has
been made available to researchers of the ATLAS and CMS experiments. Users
access the cluster from external machines connected to the World-wide LHC
Computing Grid (WLCG). This paper describes how the full software environment
of the WLCG is provided in a virtual machine image. The interplay between the
schedulers for NEMO and for the external clusters is coordinated through the
ROCED service. A cloud computing infrastructure is deployed at NEMO to
orchestrate the simultaneous usage by bare metal and virtualized jobs. Through
the setup, resources are provided to users in a transparent, automatized, and
on-demand way. The performance of the virtualized environment has been
evaluated for particle physics applications
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
A study on performance measures for auto-scaling CPU-intensive containerized applications
Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented
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