R&D: Two Studies Published by ACM Computing Surveys

Survey of Disaggregated Memory: Cross-Layer Technique Insights for Next-Generation Datacenters
Authors provide a comprehensive review of disaggregated memory, including the methodology and technologies of disaggregated memory system foundation, optimization, and management.

ACM Computing Surveys has published an article written by Jing Wang, School of Computer Science and Technology, East China Normal University, Shanghai, China, Chao Li, Department of Computer Science and Engineering, Shanghai, China, Jiao Tong University, Shanghai China, Taolei Wang, Jinyang Guo Shanghai Jiao Tong University, Shanghai China, Hanzhang Yang, University of Toronto, Toronto, Canada, Yiming Zhuansun, Shanghai Jiao Tong University, Shanghai China, and Minyi Guo, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai China.

Abstract: “The growing scale of data requires efficient memory subsystems with large memory capacity and high memory performance. Disaggregated architecture has become a promising solution for today’s cloud and edge computing for its scalability and elasticity. As a critical part of disaggregation, disaggregated memory faces many design challenges in different dimensions, including hardware scalability, architecture structure, software system design, application programmability, resource allocation, power management, etc. These challenges inspire a number of novel solutions at different system levels to improve overall efficiency. In this paper, we provide a comprehensive review of disaggregated memory, including the methodology and technologies of disaggregated memory system foundation, optimization, and management. We study the technical essentials of disaggregated memory systems and analyze them from a bottom-up perspective, covering hardware, architecture, system, and application levels. Then, we compare the design details of typical cross-layer designs on disaggregated memory. Finally, we discuss the challenges and opportunities of future disaggregated memory works that serve better for next-generation elastic and efficient datacenters.“

Machine Learning-Based Caching and Tiering in Modern Data Storage Systems: A Survey
Manuscript reviews the state-of-the-art ML-based caching and tiering approaches, examining their theoretical foundations and practical implementations.

ACM Computing Surveys has published an article written by George Savva, Cyprus University of Technology, Limassol, Cyprus, Elena Kakoulli, Neapolis University Pafos, Paphos, Cyprus, and Herodotos Herodotou, Cyprus University of Technology, Limassol Cyprus.

Abstract: “Modern data storage systems contain numerous media devices (e.g., DRAM, NVRAM, SSDs, and HDDs) organized into multi-level caches and storage tiers to optimize the performance-to-cost ratio. Traditional caching policies for eviction, admission, and prefetching, as well as tiering policies for data placement and migration, offer simplicity but struggle to adapt to the dynamic and complex access patterns observed in modern data-intensive applications. As a result, machine learning (ML)-based policies have recently gained popularity due to their ability to make predictions and proactively optimize caching and tiering operations. These ML techniques not only increase cache hit rates and reduce latency but also provide scalable, cost-effective solutions that intelligently place data across different storage media. This manuscript reviews the state-of-the-art ML-based caching and tiering approaches, examining their theoretical foundations and practical implementations. It also presents the most common features for each policy type, the most popular baselines for comparison, and the typical evaluation metrics. Finally, it discusses emerging trends and outlines potential directions for future research in data storage systems.“