Operational Experience with CMS Tier-2 Sites

In the CMS computing model, more than one third of the computing resources are located at \mbox{Tier-2} sites, which are distributed across the countries in the collaboration. These sites are the primary platform for user analyses; they host datasets that are created at Tier-1 sites, and users from all CMS institutes submit analysis jobs that run on those data through grid interfaces. They are also the primary resource for the production of large simulation samples for general use in the experiment. As a result, Tier-2 sites have an interesting mix of organized experiment-controlled activities and chaotic user-controlled activities. CMS currently operates about 40 Tier-2 sites in 22 countries, making the sites a far-flung computational and social network. We describe our operational experience with the sites, touching on our achievements, the lessons learned, and the challenges for the future

Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.Peer ReviewedPostprint (author's final draft