22,582 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
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
A service-oriented architecture for scientific computing on cloud infrastructures
This paper describes a service-oriented architecture that eases the process of scientific application deployment and execution in IaaS Clouds, with a focus on High Throughput Computing applications. The system integrates i) a catalogue and repository of Virtual Machine Images, ii) an application deployment and configuration tool, iii) a meta-scheduler for job execution management and monitoring. The developed system significantly reduces the time required to port a scientific application to these computational environments. This is exemplified by a case study with a computationally intensive protein design application on both a private Cloud and a hybrid three-level infrastructure (Grid, private and public Cloud).The authors wish to thank the financial support received from the Generalitat Valenciana for the project GV/2012/076 and to the Ministerio de Econom´ıa y Competitividad for the project CodeCloud (TIN2010-17804)Moltó, G.; Calatrava Arroyo, A.; Hernández GarcÃa, V. (2013). A service-oriented architecture for scientific computing on cloud infrastructures. En High Performance Computing for Computational Science - VECPAR 2012. Springer Verlag (Germany). 163-176. doi:10.1007/978-3-642-38718-0_18S163176Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds. ACM SIGCOMM Computer Communication Review 39(1), 50 (2008)Armbrust, M., Fox, A., Griffith, R., Joseph, A.: Above the clouds: A berkeley view of cloud computing. Technical report, UC Berkeley Reliable Adaptive Distributed Systems Laboratory (2009)Rehr, J., Vila, F., Gardner, J., Svec, L., Prange, M.: Scientific computing in the cloud. Computing in Science 99 (2010)Keahey, K., Figueiredo, R., Fortes, J., Freeman, T., Tsugawa, M.: Science Clouds: Early Experiences in Cloud Computing for Scientific Applications. In: Cloud Computing and its Applications (2008)Carrión, J.V., Moltó, G., De Alfonso, C., Caballer, M., Hernández, V.: A Generic Catalog and Repository Service for Virtual Machine Images. In: 2nd International ICST Conference on Cloud Computing (CloudComp 2010) (2010)Moltó, G., Hernández, V., Alonso, J.: A service-oriented WSRF-based architecture for metascheduling on computational Grids. Future Generation Computer Systems 24(4), 317–328 (2008)Krishnan, S., Clementi, L., Ren, J., Papadopoulos, P., Li, W.: Design and Evaluation of Opal2: A Toolkit for Scientific Software as a Service. In: 2009 IEEE Congress on Services (2009)Distributed Management Task Force (DMTF): The Open Virtualization Format Specification (Technical report)Raman, R., Livny, M., Solomon, M.: Matchmaking: Distributed Resource Management for High Throughput Computing. In: Proceedings of the Seventh IEEE International Symposium on High Performance Distributed Computing, pp. 28–31 (1998)Wei, J., Zhang, X., Ammons, G., Bala, V., Ning, P.: Managing security of virtual machine images in a cloud environment. ACM Press, New York (2009)Keahey, K., Freeman, T.: Contextualization: Providing One-Click Virtual Clusters. In: Fourth IEEE International Conference on eScience, pp. 301–308 (2008)Foster, I.: Globus toolkit version 4: Software for service-oriented systems. Journal of Computer Science and Technology 21(4), 513–520 (2006)Moltó, G., Suárez, M., Tortosa, P., Alonso, J.M., Hernández, V., Jaramillo, A.: Protein design based on parallel dimensional reduction. Journal of Chemical Information and Modeling 49(5), 1261–1271 (2009)Calatrava, A.: In: Use of Grid and Cloud Hybrid Infrastructures for Scientific Computing (M.Sc. Thesis in Spanish), Universitat Politècnica de València (2012)Keahey, K., Freeman, T., Lauret, J., Olson, D.: Virtual workspaces for scientific applications. Journal of Physics: Conference Series 78(1), 012038 (2007)Pallickara, S., Pierce, M., Dong, Q., Kong, C.: Enabling Large Scale Scientific Computations for Expressed Sequence Tag Sequencing over Grid and Cloud Computing Clusters. In: Eigth International Conference on Parallel Processing and Applied Mathematics (PPAM 2009), Citeseer (2009)Merzky, A., Stamou, K., Jha, S.: Application Level Interoperability between Clouds and Grids. In: 2009 Workshops at the Grid and Pervasive Computing Conference, pp. 143–150 (2009)Thain, D., Tannenbaum, T., Livny, M.: Distributed computing in practice: the Condor experience. Concurrency and Computation: Practice and Experience 17(2-4), 323–356 (2005)Simmhan, Y., van Ingen, C., Subramanian, G., Li, J.: Bridging the Gap between Desktop and the Cloud for eScience Applications. In: 2010 IEEE 3rd International Conference on Cloud Computing, pp. 474–481. IEEE (2010)Chappell, D.: Introducing windows azure. Technical report (2009
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
VIoLET: A Large-scale Virtual Environment for Internet of Things
IoT deployments have been growing manifold, encompassing sensors, networks,
edge, fog and cloud resources. Despite the intense interest from researchers
and practitioners, most do not have access to large-scale IoT testbeds for
validation. Simulation environments that allow analytical modeling are a poor
substitute for evaluating software platforms or application workloads in
realistic computing environments. Here, we propose VIoLET, a virtual
environment for defining and launching large-scale IoT deployments within cloud
VMs. It offers a declarative model to specify container-based compute resources
that match the performance of the native edge, fog and cloud devices using
Docker. These can be inter-connected by complex topologies on which
private/public networks, and bandwidth and latency rules are enforced. Users
can configure synthetic sensors for data generation on these devices as well.
We validate VIoLET for deployments with > 400 devices and > 1500 device-cores,
and show that the virtual IoT environment closely matches the expected compute
and network performance at modest costs. This fills an important gap between
IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European
Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31,
2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for
presentation at the Plenary Session of the conferenc
Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters
To improve customer experience, datacenter operators offer support for
simplifying application and resource management. For example, running workloads
of workflows on behalf of customers is desirable, but requires increasingly
more sophisticated autoscaling policies, that is, policies that dynamically
provision resources for the customer. Although selecting and tuning autoscaling
policies is a challenging task for datacenter operators, so far relatively few
studies investigate the performance of autoscaling for workloads of workflows.
Complementing previous knowledge, in this work we propose the first
comprehensive performance study in the field. Using trace-based simulation, we
compare state-of-the-art autoscaling policies across multiple application
domains, workload arrival patterns (e.g., burstiness), and system utilization
levels. We further investigate the interplay between autoscaling and regular
allocation policies, and the complexity cost of autoscaling. Our quantitative
study focuses not only on traditional performance metrics and on
state-of-the-art elasticity metrics, but also on time- and memory-related
autoscaling-complexity metrics. Our main results give strong and quantitative
evidence about previously unreported operational behavior, for example, that
autoscaling policies perform differently across application domains and by how
much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based
Performance Study of Autoscaling Workloads of Workflows in Datacenters
Academic Cloud Computing Research: Five Pitfalls and Five Opportunities
This discussion paper argues that there are five fundamental pitfalls, which
can restrict academics from conducting cloud computing research at the
infrastructure level, which is currently where the vast majority of academic
research lies. Instead academics should be conducting higher risk research, in
order to gain understanding and open up entirely new areas.
We call for a renewed mindset and argue that academic research should focus
less upon physical infrastructure and embrace the abstractions provided by
clouds through five opportunities: user driven research, new programming
models, PaaS environments, and improved tools to support elasticity and
large-scale debugging. The objective of this paper is to foster discussion, and
to define a roadmap forward, which will allow academia to make longer-term
impacts to the cloud computing community.Comment: Accepted and presented at the 6th USENIX Workshop on Hot Topics in
Cloud Computing (HotCloud'14
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
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