The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterpri...The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].展开更多
A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaini...A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaining impact.RFOs are therefore promoting FAIR research data management and stewardship(RDM)in their research funding cycle.However,the implementation of FAIR RDM still faces important obstacles and challenges.To solve these,stakeholders work together to develop innovative tools and practices.Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle,integrated with research community specific guidance,criteria and metadata,and enabling automatic assessments of progress and output from RDM.The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community.To fully benefit from the model,RFOs,research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures.As many stakeholders still need to get familiar with“human actionable”FAIR data practices,the introduction of the model will be stepwise,with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.展开更多
文摘The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].
文摘A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaining impact.RFOs are therefore promoting FAIR research data management and stewardship(RDM)in their research funding cycle.However,the implementation of FAIR RDM still faces important obstacles and challenges.To solve these,stakeholders work together to develop innovative tools and practices.Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle,integrated with research community specific guidance,criteria and metadata,and enabling automatic assessments of progress and output from RDM.The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community.To fully benefit from the model,RFOs,research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures.As many stakeholders still need to get familiar with“human actionable”FAIR data practices,the introduction of the model will be stepwise,with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.
