1,511 research outputs found
EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud
Cloud computing has become more popular in provision of computing resources
under virtual machine (VM) abstraction for high performance computing (HPC)
users to run their applications. A HPC cloud is such cloud computing
environment. One of challenges of energy efficient resource allocation for VMs
in HPC cloud is tradeoff between minimizing total energy consumption of
physical machines (PMs) and satisfying Quality of Service (e.g. performance).
On one hand, cloud providers want to maximize their profit by reducing the
power cost (e.g. using the smallest number of running PMs). On the other hand,
cloud customers (users) want highest performance for their applications. In
this paper, we focus on the scenario that scheduler does not know global
information about user jobs and user applications in the future. Users will
request shortterm resources at fixed start times and non interrupted durations.
We then propose a new allocation heuristic (named Energy-aware and Performance
per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to
choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS
per Watt). Using information from Feitelson's Parallel Workload Archive to
model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on
heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF
can reduce significant total energy consumption in comparison with state of the
art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced
Computing and Applications, Journal of Science and Technology, Vietnamese
Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201
HIL: designing an exokernel for the data center
We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.Partial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation awards 1347525 and 1149232 as well as the several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or
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
Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service
In the near future FPGAs will be available by the hour, however this new
Infrastructure as a Service (IaaS) usage mode presents both an opportunity and
a challenge: The opportunity is that programmers can potentially trade
resources for performance on a much larger scale, for much shorter periods of
time than before. The challenge is in finding and traversing the trade-off for
heterogeneous IaaS that guarantees increased resources result in the greatest
possible increased performance. Such a trade-off is Pareto optimal. The Pareto
optimal trade-off for clusters of heterogeneous resources can be found by
solving multiple, multi-objective optimisation problems, resulting in an
optimal allocation of tasks to the available platforms. Solving these
optimisation programs can be done using simple heuristic approaches or formal
Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial
options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore
CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up
to 110% faster than a heuristic approach, and over 50% cheaper. These results
suggest that high quality performance-resource trade-offs of heterogeneous IaaS
are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software
Programmers (FSP 2015) (arXiv:1508.06320
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