Next Generation HPC Clouds: A View for Large-Scale Scientific and Data-Intensive Applications

Petcu, Dana; González–Vélez, Horacio; Nicolae, Bogdan; García‐Gómez, Juan Miguel; Fuster–Garcia, Elies; Sheridan, Craig

doi:10.1007/978-3-319-14313-2_3

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Once deployed, the application becomes available for end users via a web-based portal, and the only input required in this case are input parameters, which are supplied via web forms. Petcu et al [99] proposed a different framework…”

Section: Legacy To Saasmentioning

confidence: 99%

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HPC Cloud for Scientific and Business Applications

et al. 2018

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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. users have no visibility or concerns on costs of running jobs. However, large clusters do incur expenses and, when not properly managed, can generate resource wastage and poor quality of service.Motivated by the different utilization levels of clusters around the globe and by the need to run even larger parallel programs, in the early 2000s, Grid Computing became relevant for the HPC community. Grids offer users access to powerful resources managed by autonomous administrative domains [50,51]. The notion of monetary costs for running applications was soft, favoring a more collaborative model of resource sharing. Therefore, quality of service was not strict in Grids, having users relying on best-effort policies to run applications.In the late 2000s, cloud computing [8,26,91] was quickly increasing its maturity level and popularity, and studies started to emerge on the viability of executing HPC applications on remote cloud resources. These applications, which consume more resources than traditional cloud applications and usually are executed in batches rather than 24x7 services, range from parallel applications written in Message Passing Interface (MPI) [58,59] to the newest big data [11,14,39,101] and artificial intelligence applications-the latter mostly relying on deep learning [34,80]. Cloud then came up as an evolution of a series of technologies, mainly on virtualization and computer networks, which facilitated both workload management and interaction with remote resources respectively. Apart from software and hardware, cloud offers a business model where users pay for resources on demand. Compared to traditional HPC environments, in clouds users can quickly adjust their resource pools, via a mechanism known as elast...

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Section: Legacy To Saasmentioning

confidence: 99%

“…Legacy-to-SaaS [16,69,99] Related to transform legacy applications running in traditional computing platforms to cloud environments. Direct ports of legacy applications to cloud environments might have inefficiencies due to different infrastructure assumptions; principled methodologies can overcome such inefficiencies.…”

Section: Workflowmentioning

confidence: 99%

HPC Cloud for Scientific and Business Applications

et al. 2018

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“…Minimal data locality is achieved for each task by reducing the number of fragments for each task. However, this data division technique that uses minimum data locality may not prove to be efficient in a heterogeneous environment such as Hadoop where the number of nodes, the data sizes, and their nature varies according to the different applications [13].…”

Section: A Data Locality In Hadoopmentioning

confidence: 99%

Novel Data-Distribution Technique for Hadoop in Heterogeneous Cloud Environments

Ubarhande

Popescu

González–Vélez

2015

2015 Ninth International Conference on Complex, Intelligent, and Software Intensive Systems

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The Hadoop framework has been developed to effectively process data-intensive MapReduce applications. Hadoop users specify the application computation logic in terms of a map and a reduce function, which are often termed MapReduce applications. The Hadoop distributed file system is used to store the MapReduce application data on the Hadoop cluster nodes called Datanodes, whereas Namenode is a control point for all Datanodes. While its resilience is increased, its current data-distribution methodologies are not necessarily efficient for heterogeneous distributed environments such as public clouds.

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“…Such verification is based on well-established complex cryptology algorithms [70] (ergo, the term cryptocurrencies) which assure user anonymity and payment untraceability. With the convergence of HPC and clouds [102], elastic computational resource utilization has become commonplace in different domains, including of course, Cryptocurrencies. Moreover, a recent survey on open challenges and trends in cloud computing [18] recognizes blockchain as a disruptive influencer of the field.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Zamuda

Crescimanna

Burguillo

et al. 2019

Lecture Notes in Computer Science

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This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking

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Next Generation HPC Clouds: A View for Large-Scale Scientific and Data-Intensive Applications

Cited by 5 publications

References 14 publications

HPC Cloud for Scientific and Business Applications

HPC Cloud for Scientific and Business Applications

Novel Data-Distribution Technique for Hadoop in Heterogeneous Cloud Environments

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

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