1,638 research outputs found
Provisioning Spot Market Cloud Resources to Create Cost-effective Virtual Clusters
Infrastructure-as-a-Service providers are offering their unused resources in
the form of variable-priced virtual machines (VMs), known as "spot instances",
at prices significantly lower than their standard fixed-priced resources. To
lease spot instances, users specify a maximum price they are willing to pay per
hour and VMs will run only when the current price is lower than the user's bid.
This paper proposes a resource allocation policy that addresses the problem of
running deadline-constrained compute-intensive jobs on a pool of composed
solely of spot instances, while exploiting variations in price and performance
to run applications in a fast and economical way. Our policy relies on job
runtime estimations to decide what are the best types of VMs to run each job
and when jobs should run. Several estimation methods are evaluated and
compared, using trace-based simulations, which take real price variation traces
obtained from Amazon Web Services as input, as well as an application trace
from the Parallel Workload Archive. Results demonstrate the effectiveness of
running computational jobs on spot instances, at a fraction (up to 60% lower)
of the price that would normally cost on fixed priced resources.Comment: 14 pages, 4 figures, 11th International Conference on Algorithms and
Architectures for Parallel Processing (ICA3PP-11); Lecture Notes in Computer
Science, Vol. 7016, 201
Reliable Provisioning of Spot Instances for Compute-intensive Applications
Cloud computing providers are now offering their unused resources for leasing
in the spot market, which has been considered the first step towards a
full-fledged market economy for computational resources. Spot instances are
virtual machines (VMs) available at lower prices than their standard on-demand
counterparts. These VMs will run for as long as the current price is lower than
the maximum bid price users are willing to pay per hour. Spot instances have
been increasingly used for executing compute-intensive applications. In spite
of an apparent economical advantage, due to an intermittent nature of biddable
resources, application execution times may be prolonged or they may not finish
at all. This paper proposes a resource allocation strategy that addresses the
problem of running compute-intensive jobs on a pool of intermittent virtual
machines, while also aiming to run applications in a fast and economical way.
To mitigate potential unavailability periods, a multifaceted fault-aware
resource provisioning policy is proposed. Our solution employs price and
runtime estimation mechanisms, as well as three fault tolerance techniques,
namely checkpointing, task duplication and migration. We evaluate our
strategies using trace-driven simulations, which take as input real price
variation traces, as well as an application trace from the Parallel Workload
Archive. Our results demonstrate the effectiveness of executing applications on
spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
Notes on Cloud computing principles
This letter provides a review of fundamental distributed systems and economic
Cloud computing principles. These principles are frequently deployed in their
respective fields, but their inter-dependencies are often neglected. Given that
Cloud Computing first and foremost is a new business model, a new model to sell
computational resources, the understanding of these concepts is facilitated by
treating them in unison. Here, we review some of the most important concepts
and how they relate to each other
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Cloud computing systems promise to offer subscription-oriented,
enterprise-quality computing services to users worldwide. With the increased
demand for delivering services to a large number of users, they need to offer
differentiated services to users and meet their quality expectations. Existing
resource management systems in data centers are yet to support Service Level
Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to
realize cloud computing and utility computing. In addition, no work has been
done to collectively incorporate customer-driven service management,
computational risk management, and autonomic resource management into a
market-based resource management system to target the rapidly changing
enterprise requirements of Cloud computing. This paper presents vision,
challenges, and architectural elements of SLA-oriented resource management. The
proposed architecture supports integration of marketbased provisioning policies
and virtualisation technologies for flexible allocation of resources to
applications. The performance results obtained from our working prototype
system shows the feasibility and effectiveness of SLA-based resource
provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE
International Conference on Cloud and Service Computing (CSC 2011, IEEE
Press, USA), Hong Kong, China, December 12-14, 201
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
Self-managed Cost-efficient Virtual Elastic Clusters on Hybrid Cloud Infrastructures
In this study, we describe the further development of Elastic Cloud Computing Cluster (EC3), a tool for
creating self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service
(IaaS) clouds. By using spot instances and checkpointing techniques, EC3 can significantly reduce the total
execution cost as well as facilitating automatic fault tolerance. Moreover, EC3 can deploy and manage
hybrid clusters across on-premises and public cloud resources, thereby introducing cloud bursting
capabilities. We present the results of a case study that we conducted to assess the effectiveness of the
tool based on the structural dynamic analysis of buildings. In addition, we evaluated the checkpointing
algorithms in a real cloud environment with existing workloads to study their effectiveness. The results
demonstrate the feasibility and benefits of this type of cluster for computationally intensive applications.
© 2016 Elsevier B.V. All rights reserved.This study was supported by the program "Ayudas para la contratacion de personal investigador en formacion de caracter pre doctoral, programa VALi+d" under grant number ACIF/2013/003 from the Conselleria d'Educacio of the Generalitat Valenciana. We are also grateful for financial support received from The Spanish Ministry of Economy and Competitiveness to develop the project "CLUVIEM" under grant reference TIN2013-44390-R. Finally, we express our gratitude to D. David Ruzafa for support with the arduous task of analyzing the executions data.Calatrava Arroyo, A.; Romero Alcalde, E.; Moltó Martínez, G.; Caballer Fernández, M.; Alonso Ábalos, JM. (2016). Self-managed Cost-efficient Virtual Elastic Clusters on Hybrid Cloud Infrastructures. Future Generation Computer Systems. 61:13-25. https://doi.org/10.1016/j.future.2016.01.018S13256
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