26,054 research outputs found
Running scientific codes on amazon EC2: a performance analysis of five high-end instances
Amazon Web Services (AWS) is a well-known public Infrastructure-as-a-Service (IaaS) provider whose Elastic Computing Cloud (EC2) o ering includes some instances, known as cluster instances, aimed at High-Performance Computing (HPC) applications. In previous work, authors have shown that the scalability of HPC communication-intensive applications does not bene t from using higher computational power cluster instances as much as it could be expected.
Cost analysis recommends using lower computational power cluster instances unless high memory requirements preclude their use. Moreover, it has been observed that scalability is very poor when more than one instance is used due to network virtualization overhead. Based on those results, this paper gives more insight into the performance of running scienti c applications on the Amazon EC2 platform evaluating ve (of which two have been recently released) of the higher computational power instances in terms of single instance performance, intra-VM (Virtual Machine) scalability and cost-e ciency. The evaluation has been carried out using both an HPC benchmark suite and a real High-Troughput Computing (HTC) application.Facultad de Informátic
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
On Evaluating Commercial Cloud Services: A Systematic Review
Background: Cloud Computing is increasingly booming in industry with many
competing providers and services. Accordingly, evaluation of commercial Cloud
services is necessary. However, the existing evaluation studies are relatively
chaotic. There exists tremendous confusion and gap between practices and theory
about Cloud services evaluation. Aim: To facilitate relieving the
aforementioned chaos, this work aims to synthesize the existing evaluation
implementations to outline the state-of-the-practice and also identify research
opportunities in Cloud services evaluation. Method: Based on a conceptual
evaluation model comprising six steps, the Systematic Literature Review (SLR)
method was employed to collect relevant evidence to investigate the Cloud
services evaluation step by step. Results: This SLR identified 82 relevant
evaluation studies. The overall data collected from these studies essentially
represent the current practical landscape of implementing Cloud services
evaluation, and in turn can be reused to facilitate future evaluation work.
Conclusions: Evaluation of commercial Cloud services has become a world-wide
research topic. Some of the findings of this SLR identify several research gaps
in the area of Cloud services evaluation (e.g., the Elasticity and Security
evaluation of commercial Cloud services could be a long-term challenge), while
some other findings suggest the trend of applying commercial Cloud services
(e.g., compared with PaaS, IaaS seems more suitable for customers and is
particularly important in industry). This SLR study itself also confirms some
previous experiences and reveals new Evidence-Based Software Engineering (EBSE)
lessons
High-Performance Cloud Computing: A View of Scientific Applications
Scientific computing often requires the availability of a massive number of
computers for performing large scale experiments. Traditionally, these needs
have been addressed by using high-performance computing solutions and installed
facilities such as clusters and super computers, which are difficult to setup,
maintain, and operate. Cloud computing provides scientists with a completely
new model of utilizing the computing infrastructure. Compute resources, storage
resources, as well as applications, can be dynamically provisioned (and
integrated within the existing infrastructure) on a pay per use basis. These
resources can be released when they are no more needed. Such services are often
offered within the context of a Service Level Agreement (SLA), which ensure the
desired Quality of Service (QoS). Aneka, an enterprise Cloud computing
solution, harnesses the power of compute resources by relying on private and
public Clouds and delivers to users the desired QoS. Its flexible and service
based infrastructure supports multiple programming paradigms that make Aneka
address a variety of different scenarios: from finance applications to
computational science. As examples of scientific computing in the Cloud, we
present a preliminary case study on using Aneka for the classification of gene
expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape
On a Catalogue of Metrics for Evaluating Commercial Cloud Services
Given the continually increasing amount of commercial Cloud services in the
market, evaluation of different services plays a significant role in
cost-benefit analysis or decision making for choosing Cloud Computing. In
particular, employing suitable metrics is essential in evaluation
implementations. However, to the best of our knowledge, there is not any
systematic discussion about metrics for evaluating Cloud services. By using the
method of Systematic Literature Review (SLR), we have collected the de facto
metrics adopted in the existing Cloud services evaluation work. The collected
metrics were arranged following different Cloud service features to be
evaluated, which essentially constructed an evaluation metrics catalogue, as
shown in this paper. This metrics catalogue can be used to facilitate the
future practice and research in the area of Cloud services evaluation.
Moreover, considering metrics selection is a prerequisite of benchmark
selection in evaluation implementations, this work also supplements the
existing research in benchmarking the commercial Cloud services.Comment: 10 pages, Proceedings of the 13th ACM/IEEE International Conference
on Grid Computing (Grid 2012), pp. 164-173, Beijing, China, September 20-23,
201
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
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