Key aspects of the UNICORE 6 security model

Benedyczak, Krzysztof; Bała, Piotr; Berghe, S. Van den; Menday, Roger; Schuller, Bernd

doi:10.1016/j.future.2010.08.009

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…The solution for trust issues mentioned above is by using Trust Delegation security technique (Tawalbeh et al, 2015d) which is used in UNICORE6 (Benedyczak et al, 2011). In this approach, the mobile user delegates the connected CL to do jobs on behalf of him, so that the CL can communicate with other CLs or with the EC as a mobile user and performs the requested job.…”

Section: Trust Delegationmentioning

confidence: 99%

Trust delegation-based secure mobile cloud computing framework

Tawalbeh

Ababneh

Jararweh

et al. 2017

IJICS

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Mobile devices are used to perform many useful tasks in our daily life. All around the world, many people are using their smart phones to communicate with others, identify their location, and do online shopping. These useful applications involve sharing personal and sensitive information. Also, the smart devices have many weaknesses, such as: limited battery life time, limited processing capacity and security issues. The mobile cloud computing (MCC) can be used to overcome these limitations. Using MCC improved the performance of many applications by conducting the processing at the cloud and sending back the results to the mobile device. But there are still many issues need to be addressed and considered when using MCC specially when dealing with applications that need live interaction and protecting the privacy. In this paper, we will study the main issues associated with mobile cloud computing models that include: security, performance, and quality of service. Then, we will present secure implementation of the cloudlet-based mobile cloud computing framework with its prototype that uses trust delegation technique to provide better security/performance tradeoffs.Keywords: mobile cloud computing; MCC; cloudlet; security; mobile devices; trust delegation. Trust delegation-based secure mobile cloud computing framework 37Reference to this paper should be made as follows: Tawalbeh, L.A., Ababneh, F., Jararweh, Y. and AlDosari, F. (2017) Fahd AlDosari received his PhD in 2010 from Bradford University, Bradford, UK. He is currently the Dean of College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia. He has many publications in the areas of cloud computing, quality of service routing, simulation and modelling and adhoc networking. He is also a founder of expert houses in computing and higher education at Umm AlQura University. He is IEEE member.

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Section: Trust Delegationmentioning

confidence: 99%

Trust delegation-based secure mobile cloud computing framework

Tawalbeh

Ababneh

Jararweh

et al. 2017

IJICS

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“…Interoperable Security Model. The GFFS security model is based on the Security Assertion Markup Language (SAML) [41] standard, and takes the UNICORE SAML profile [14] as a reference implementation. The GFFS extends the UNICORE's SAML profile to represent the trust delegation chains of the Genesis II security model.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Enabling Scalable Data Processing and Management through Standards-based Job Execution and the Global Federated File System

Riedel¹,

Memon²,

Koeritz³

et al. 2016

SCPE

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Abstract. Emerging challenges for scientific communities are to efficiently process big data obtained by experimentation and computational simulations. Supercomputing architectures are available to support scalable and high performant processing environment, but many of the existing algorithm implementations are still unable to cope with its architectural complexity. One approach is to have innovative technologies that effectively use these resources and also deal with geographically dispersed large datasets. Those technologies should be accessible in a way that data scientists who are running data intensive computations do not have to deal with technical intricacies of the underling execution system. Our work primarily focuses on providing data scientists with transparent access to these resources in order to easily analyze data. Impact of our work is given by describing how we enabled access to multiple high performance computing resources through an open standards-based middleware that takes advantage of a unified data management provided by the the Global Federated File System. Our architectural design and its associated implementation is validated by a usecase that requires massivley parallel DBSCAN outlier detection on a 3D point clouds dataset.Key words: UNICORE, Genesis II, statistical data mining, data processing, distributed file system, security, standards, parallel processing AMS subject classifications. 68M14 1.Introduction. An ever increasing number of datasets from scientific experimentation such as earth observatories or computational simulations generate an enormous amount of information for discovering useful knowledge. In order to analyze data, the area of statistical data mining provides useful methods and tools to extract and explore useful patterns or prediction models. The field of statistical data mining comes with intuitive methods to learn from data, using a wide variety of algorithms for clustering, classification and regression. Several implementations are available, for example, Matlab, R, Octave [3], or scikit-learn. Mostly, these tools offer serial implementation of the algorithms, which is quite challenging (i.e. insufficient memory, extremely long running times, etc.) for processing the volume of data having terabytes or petabytes of magnitude. Considering that amount, the resources running the data processing tools require large number of processors, as well as much more primary and secondary storage. Therefore, parallel tools and platforms such as Hadoop [15] implementing the map reduce paradigm [18] and selected massively parallel algorithm developments based on the MPI and OpenMP environments are commonly used.We observe mainly tools for (High Performance Computing) HPC and High Throuput Computing (HTC) paradigms evolving concurrently, but each supporting their own set of requirements. Scientific communities, either from biology, physics and medicine adopt more conservative approaches in order to retain their focus on scientific findings and as such traditional HPC environme...

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“…GridFTP (data transport) uses proxy certificates that do not support SAML. UNICORE 15 (middleware used by some HPC providers) supports Explicit Trusted Delegation (ETD) [19] while iRODS 16 (used for federated data and metadata storage) provides authentication via Pluggable Authentication Modules (PAM) [20].…”

Section: A the Verce Science Gatewaymentioning

confidence: 99%

VERCE Delivers a Productive E-science Environment for Seismology Research

Atkinson

Carpené

Casarotti

et al. 2015

2015 IEEE 11th International Conference on E-Science

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The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.

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Key aspects of the UNICORE 6 security model

Cited by 15 publications

References 6 publications

Trust delegation-based secure mobile cloud computing framework

Trust delegation-based secure mobile cloud computing framework

Enabling Scalable Data Processing and Management through Standards-based Job Execution and the Global Federated File System

VERCE Delivers a Productive E-science Environment for Seismology Research

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