2,098 research outputs found
Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)
The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities
Managing Large Scale Project Analysis Teams through a Web Accessible Database
Large scale space programs analyze thousands of requirements while mitigating safety, performance, schedule, and cost risks. These efforts involve a variety of roles with interdependent use cases and goals. For example, study managers and facilitators identify ground-rules and assumptions for a collection of studies required for a program or project milestone. Task leaders derive product requirements from the ground rules and assumptions and describe activities to produce needed analytical products. Disciplined specialists produce the specified products and load results into a file management system. Organizational and project managers provide the personnel and funds to conduct the tasks. Each role has responsibilities to establish information linkages and provide status reports to management. Projects conduct design and analysis cycles to refine designs to meet the requirements and implement risk mitigation plans. At the program level, integrated design and analysis cycles studies are conducted to eliminate every 'to-be-determined' and develop plans to mitigate every risk. At the agency level, strategic studies analyze different approaches to exploration architectures and campaigns. This paper describes a web-accessible database developed by NASA to coordinate and manage tasks at three organizational levels. Other topics in this paper cover integration technologies and techniques for process modeling and enterprise architectures
On the Fly Orchestration of Unikernels: Tuning and Performance Evaluation of Virtual Infrastructure Managers
Network operators are facing significant challenges meeting the demand for
more bandwidth, agile infrastructures, innovative services, while keeping costs
low. Network Functions Virtualization (NFV) and Cloud Computing are emerging as
key trends of 5G network architectures, providing flexibility, fast
instantiation times, support of Commercial Off The Shelf hardware and
significant cost savings. NFV leverages Cloud Computing principles to move the
data-plane network functions from expensive, closed and proprietary hardware to
the so-called Virtual Network Functions (VNFs). In this paper we deal with the
management of virtual computing resources (Unikernels) for the execution of
VNFs. This functionality is performed by the Virtual Infrastructure Manager
(VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. We
discuss the instantiation process of virtual resources and propose a generic
reference model, starting from the analysis of three open source VIMs, namely
OpenStack, Nomad and OpenVIM. We improve the aforementioned VIMs introducing
the support for special-purpose Unikernels and aiming at reducing the duration
of the instantiation process. We evaluate some performance aspects of the VIMs,
considering both stock and tuned versions. The VIM extensions and performance
evaluation tools are available under a liberal open source licence
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