2,061 research outputs found
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
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
Energy Efficiency Support through Intra-Layer Cloud Stack Adaptation
Energy consumption is a key concern in cloud computing. The paper reports on a cloud architecture to support energy efficiency at service construction, deployment, and operation. This is achieved through SaaS, PaaS and IaaS intra-layer self-adaptation in isolation. The self-adaptation mechanisms are discussed, as well as their implementation and evaluation. The experimental results show that the overall architecture is capable of adapting to meet the energy goals of applications on a per layer basis
Energy efficiency embedded service lifecycle: Towards an energy efficient cloud computing architecture
The paper argues the need to provide novel methods and tools to support software developers aiming to optimise energy efficiency and minimise the carbon footprint resulting from designing, developing, deploying and running software in Clouds, while maintaining other quality aspects of software to adequate and agreed levels. A cloud architecture to support energy efficiency at service construction, deployment, and operation is discussed, as well as its implementation and evaluation plans.Postprint (published version
Towards energy aware cloud computing application construction
The energy consumption of cloud computing continues to be an area of significant concern as data center growth continues to increase. This paper reports on an energy efficient interoperable cloud architecture realised as a cloud toolbox that focuses on reducing the energy consumption of cloud applications holistically across all deployment models. The architecture supports energy efficiency at service construction, deployment and operation. We discuss our practical experience during implementation of an architectural component, the Virtual Machine Image Constructor (VMIC), required to facilitate construction of energy aware cloud applications. We carry out a performance evaluation of the component on a cloud testbed. The results show the performance of Virtual Machine construction, primarily limited by available I/O, to be adequate for agile, energy aware software development. We conclude that the implementation of the VMIC is feasible, incurs minimal performance overhead comparatively to the time taken by other aspects of the cloud application construction life-cycle, and make recommendations on enhancing its performance
PaaS-IaaS Inter-Layer Adaptation in an Energy-Aware Cloud Environment
Cloud computing providers resort to a variety of techniques to improve energy consumption at each level of the cloud computing stack. Most of these techniques consider resource-level energy optimization at IaaS layer. This paper argues energy gains can be obtained by creating a cooperation between the PaaS layer (in charge of hosting the application/service) and the IaaS layer (in charge of handling the computing resources). It presents a novel method based on steering information and decision taking to trigger the PaaS and IaaS layers to adapt their energy mode in service operation, therefore enabling the Cloud stack to actively adapt to changing situations. Experimental results demonstrate such adaptation achieves dynamic energy management in each of the PaaS and IaaS cloud layers
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