Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS

Antonacci, Marica; Ceccanti, Andrea; Ciangottini, D.; Maria, R. Di; Donvito, Giacinto; Dūma, M.; Gaido, Luciano; García, Álvaro López; Hoz, Aida Palacio; Salomoni, Davide; Tracolli, Mirco

doi:10.1051/epjconf/201921407027

Cited by 11 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, following the main route that allocates resources at the grid sites, some HPC centers have setup grid middleware such as CEs, so their CPUs are made accessible via the submission of GlideinWMS [20] pilot jobs that spawn HTCondor execution nodes that join the CMS SI global HTCondor pool (the case of the CSCS Swiss site, for example). Alternatively, the HTCondor execution nodes can be launched at the HPC independently of the CMS SI route, by employing a vacuum model [21], which can be implemented for example, employing a DODAS [22] approach. The access for CMS workloads to HPC resources can also be granted by means of incorporating the HPC slots into externally-managed HTCondor pools, which are then federated with the centralized CMS infrastructure.…”

Section: Resource Provisioningmentioning

confidence: 99%

CMS strategy for HPC resource exploitation

Yzquierdo

2020

EPJ Web Conf.

View full text Add to dashboard Cite

High Energy Physics (HEP) experiments will enter a new era with the start of the HL-LHC program, with computing needs surpassing by large factors the current capacities. Anticipating such scenario, funding agencies from participating countries are encouraging the experimental collaborations to consider the rapidly developing High Performance Computing (HPC) international infrastructures to satisfy at least a fraction of the foreseen HEP processing demands. These HPC systems are highly non-standard facilities, custom-built for use cases largely different from HEP demands, namely the processing of particle collisions (real or simulated) which can be analyzed individually without correlation. The access and utilization of these systems by HEP experiments will not be trivial, given the diversity of configuration and requirements for access among HPC centers, increasing the level of complexity from the HEP experiment integration and operations perspectives. Additionally, while HEP data is residing on a distributed highly-interconnected storage infrastructure, HPC systems are in general not meant for accessing large data volumes residing outside the facility. Finally, the allocation policies to these resources are generally different from the current usage of pledged resources deployed at supporting Grid sites. This report covers the CMS strategy developed to make effective use of HPC resources, involving a closer collaboration between CMS and HPC centers in order to further understand and subsequently overcome the present obstacles. Progress in the necessary technical and operational adaptations being made in CMS computing is described.

show abstract

Section: Resource Provisioningmentioning

confidence: 99%

CMS strategy for HPC resource exploitation

Yzquierdo

2020

EPJ Web Conf.

View full text Add to dashboard Cite

show abstract

“…However, this does not preclude the possibility to easily run also on other types of infrastructure, and of course allows for a gradual transition from local running to fully Grid-like distributed computing. One more opportunity is the capability of deploying on-demand HTCondor clusters on Cloud resources through for example DoDAS [20], condor_annex (a mechanism provided by HTCondor) or other mechanisms, which is being explored. This would allow the collaborations to exploit heterogeneous or opportunistic resources transparently.…”

Section: Workload Managementmentioning

confidence: 99%

Virgo and Gravitational-Wave Computing in Europe

Bagnasco

2020

EPJ Web Conf.

View full text Add to dashboard Cite

Advanced Virgo is an interferometer for the detection of gravitational waves at the European Gravitational Observatory in Italy. Along with the two Advanced LIGO interferometers in the US, Advanced Virgo is being used to collect data from astrophysical sources such as compact binary coalescences and is currently running the third observational period, collecting gravitational wave event candidates at a rate of more than once per week. Data from the interferometer are processed by running search pipelines for several expected signals, from coalescing compact binaries to continuous waves and burst events. Furthermore, detector characterisation studies are run. Some of the processing needs to be done with low latency, to be able to provide triggers for other observatories and make multi-messenger observations possible. Deep searches are run offline on external computing centres. Thus, data needs also to be reliably and promptly distributed from the EGO site to computer centres in Europe and the US for further analysis and archival storage. Two of the defining characteristics of Virgo computing are the heterogeneity of the activities and the need to interoperate with LIGO. A very wide array of analysis pipelines differing in scientific target, implementation details and running environment assumptions have to be allowed to run ubiquitously and uniformly on dedicated resources and, in perspective, on heterogeneous infrastructures. The current status, possible strategies and outlook of Virgo computing are discussed.

show abstract

“…These resources are expected to be accessed on demand to create HTC clusters. In order to deal with this scenario we decided to rely on DODAS [5,6]. The latter is an open source deployer manager designed to dynamically exploit hybrid Cloud providers.…”

Section: Access To Cloud Based Resources: Dodasmentioning

confidence: 99%

Dynamic integration of distributed, Cloud-based HPC and HTC resources using JSON Web Tokens and the INDIGO IAM Service

et al. 2020

Self Cite

View full text Add to dashboard Cite

In the past couple of years, we have been actively developing the Dynamic On-Demand Analysis Service (DODAS) as an enabling technology to deploy container-based clusters over hybrid, private or public, Cloud infrastructures with almost zero effort. DODAS is particularly suitable for harvesting opportunistic computing resources; this is why several scientific communities already integrated their computing use cases into DODAS-instantiated clusters, automating the instantiation, management and federation of HTCondor batch systems. The increasing demand, availability and utilization of HPC resources by and for multidisciplinary user communities, often mandates the possibility to transparently integrate, manage and mix HTC and HPC resources. In this paper, we discuss our experience extending and using DODAS to connect HPC and HTC resources in the context of a distributed Italian regional infrastructure involving multiple sites and communities. In this use case, DODAS automatically generates HTCondor batch system on-demand. Moreover it dynamically and transparently federates sites that may also include HPC resources managed by SLURM; DODAS allows user workloads to make opportunistic and automated use of both HPC and HTC resources, thus effectively maximizing and optimizing resource utilization. We also report on our experience of using and federating HTCondor batch systems exploiting the JSON Web Token capabilities introduced in recent HTCondor versions, replacing the traditional X509 certificates in the whole chain of workload authorization. In this respect we also report on how we integrated HTCondor using OAuth with the INDIGO IAM service.

show abstract

Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS

Cited by 11 publications

References 14 publications

CMS strategy for HPC resource exploitation

CMS strategy for HPC resource exploitation

Virgo and Gravitational-Wave Computing in Europe

Dynamic integration of distributed, Cloud-based HPC and HTC resources using JSON Web Tokens and the INDIGO IAM Service

Contact Info

Product

Resources

About