Scientific research is increasingly relying on complex workflows that span multiple computing paradigms, including high-performance computing (HPC), high-throughput computing (HTC), and machine learning/artificial intelligence (ML/AI). Traditional monolithic computing infrastructures often struggle to accommodate these diverse and evolving demands. The Reconfigurable Advanced Platform for Transdisciplinary Open Research (RAPTOR) addresses this challenge by providing a dynamically reconfigurable computing environment that integrates with federated resources. RAPTOR's architecture enables dynamic provisioning between an HPC cluster and the Chameleon Cloud platform based on workload requirements, supporting bare-metal customization for specialized applications. This paper focuses on RAPTOR's reconfigurability features and demonstrates their effectiveness through quantitative performance evaluations across four scientific domains: computational proteomics, climate modeling, weather research, and hurricane risk assessment. Our results demonstrate that RAPTOR's reconfigurable design significantly enhances research productivity by providing an appropriate computing environment for diverse computational needs.

This study addresses the knowledge gap in request-level storage trace analysis by incorporating workload characterization, compression, and synthesis. The aim is to better understand workload behavior and provide unique workloads for storage system testing under different scenarios. Machine learning techniques like K-means clustering and PCA analysis are employed to understand trace properties and reduce manual workload selection. By generating synthetic workloads, the proposed method facilitates simulation and modeling-based studies of storage systems, especially for emerging technologies like Storage Class Memory (SCM) with limited workload availability.

Data centers today host large numbers of workloads and many of these workloads consume significant storage resources. Given the long history of successes in storage caching, it is only natural such successes bear fruit in modern data centers, at scale. This paper presents CaaS, a generalized caching service for cloud data centers. Departing from existing application, storage, or data-type specific caches, CaaS unifies and abstracts data center caching resources making these available to any workload and for any data type. Also departing from past caching practices, CaaS is fault-tolerant allowing it to cache writes without risk of data loss. We expect that systems such as CaaS will help bridge the gap between heterogeneous and distributed cache resources and data-intensive applications in a data center.