In early 2024, ATLAS undertook an architectural review to evaluate the functionalities of its current components within the workflow and workload management ecosystem. Pivotal to the review was the assessment of the Production and Distributed Analysis (PanDA) system, which plays a vital role in the overall infrastructure. The review findings indicated that while the current system shows no apparent signs of scalability limitations or critical defects, several issues still require attention. These include areas for improvement, such as cleaning the historical accumulation of code over nearly two decades of continuous operation in ATLAS, further organizing development activities, maximizing the utilization of continuous integration and testing frameworks, bolstering efforts toward cross-experimental outreach, spreading greater awareness of workflows at the core level, expanding support for complex workflows, implementing a more advanced algorithm for workload distribution, optimizing tape and network resource usage, refining interface design, enhancing transparency to showcase system dynamism, ensuring allocation of key developers to R&D projects with clear long-term visions for integration and operation, and accommodating the growing diversity of resources. In this paper, we first highlight the issues identified in the review, exploring their historical and cultural roots. Then, we outline the recommendations derived from the review, and present the solutions developed to address these challenges and pave the way to sustainably support multiple experiments.
Korchuganova et al. (Tue,) studied this question.