We describe a high-throughput computing system for running jobs on public and private computing clouds using the HTCondor job scheduler and the cloudscheduler VM provisioning service. The distributed cloud computing system is designed to simultaneously use dedicated and opportunistic cloud resources at local and remote locations. It has been used for large-scale production particle physics workloads for many years using thousands of cores on three continents. A decade after its initial design and implementation, cloudscheduler has been modernized to take advantage of new software designs, improved operating system capabilities and support packages. The updated cloudscheduler is more resilient and scalable, with expanded capabilities. We present an overview of the original design and then describe the new version of the distributed compute cloud system. We conclude with a review of the current status and future plans.

Input data for applications that run in cloud computing centres can be stored at distant repositories, often with multiple copies of the popular data stored at many sites. Locating and retrieving the remote data can be challenging, and we believe that federating the storage can address this problem. A federation would locate the closest copy of the data on the basis of GeoIP information. Currently we are using the dynamic data federation Dynafed, a software solution developed by CERN IT. Dynafed supports several industry standards for connection protocols like Amazon's S3, Microsoft's Azure, as well as WebDAV and HTTP. Dynafed functions as an abstraction layer under which protocol-dependent authentication details are hidden from the user, requiring the user to only provide an X509 certificate. We have setup an instance of Dynafed and integrated it into the ATLAS data distribution management system. We report on the challenges faced during the installation and integration. We have tested ATLAS analysis jobs submitted by the PanDA production system and we report on our first experiences with its operation.