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The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data repository. From the results, we observed that the network bandwidth demand was reduced by nearly a factor of 3 over the study period. 

The High Luminosity Large Hadron Collider provides a data challenge. The amount of data recorded from the experiments and transported to hundreds of sites will see a thirty fold increase in annual data volume. A systematic approach to contrast the performance of different Third Party Copy (TPC) transfer protocols arises. Two contenders, XRootD-HTTPS and the GridFTP are evaluated in their performance for transferring files from one server to another over 100Gbps interfaces. The benchmarking is done by scheduling pods on the Pacific Research Platform Kubernetes cluster to ensure reproducible and repeatable results. This opens a future pathway for network testing of any TPC transfer protocol. 

Scientific computing needs are growing dramatically with time and are expanding in science domains that were previously not compute intensive. When compute workflows spike well in excess of the capacity of their local compute resource, capacity should be temporarily provisioned from somewhere else to both meet deadlines and to increase scientific output. Public Clouds have become an attractive option due to their ability to be provisioned with minimal advance notice. The available capacity of cost-effective instances is not well understood. This paper presents expanding the IceCube's production HTCondor pool using cost-effective GPU instances in preemptible mode gathered from the three major Cloud providers, namely Amazon Web Services, Microsoft Azure and the Google Cloud Platform. Using this setup, we sustained for a whole workday about 15k GPUs, corresponding to around 170 PFLOP32s, integrating over one EFLOP32 hour worth of science output for a price tag of about $60k. In this paper, we provide the reasoning behind Cloud instance selection, a description of the setup and an analysis of the provisioned resources, as well as a short description of the actual science output of the exercise. 

The University of California system maintains excellent networking between its campuses and a number of other Universities in California, including Caltech, most of them being connected at 100 Gbps. UCSD and Caltech Tier2 centers have joined their disk systems into a single logical caching system, with worker nodes from both sites accessing data from disks at either site. This successful setup has been in place for the last two years. However, coherently managing nodes at multiple physical locations is not trivial and requires an update on the operations model used. The Pacific Research Platform (PRP) provides Kubernetes resource pool spanning resources in the science demilitarized zones (DMZs) in several campuses in California and worldwide. We show how we migrated the XCache services from bare-metal deployments into containers using the PRP cluster. This paper presents the reasoning behind our hardware decisions and the experience in migrating to and operating in a mixed environment. 

A general problem faced by opportunistic users computing on the grid is that delivering cycles is simpler than delivering data to those cycles. In this project XRootD caches are placed on the internet backbone to create a content delivery network. Scientific workflows in the domains of high energy physics, gravitational waves, and others profit from this delivery network to increases CPU efficiency while decreasing network bandwidth use.