Cross-Tier and Distributed Caching and Data Management

The ever-growing number of services and Internet of Things (IoT) devices has resulted in data being distributed across different locations (regions and countries) and different storage tires. Additionally, data exhibits different usage patterns, including cold data (written once and never read), stream data (produced once and consumed by many), and hot data (written once and consumed by many). Furthermore, these data types have different performance and dependability requirements (e.g., low latency for data streams).

To ensure the reliability and improve the performance of data-intensive applications, data are either replicated or erasure-coded and distributed across different storage tiers, while frequently accessed data are stored on high-speed devices close to end users (i.e., cached). While much work has investigated data caching, data placement strategies (i.e., deciding what to cache), data movement, cache partitioning, cache eviction [1-8], and cost-efficient data redundancy techniques in caching systems [9], few efforts have focused holistic caching and data management when caches are distributed across heterogeneous platforms (from Edge to Cloud), utilize storage devices with varying performance and cost characteristics, and simultaneously serve diverse workloads, including traditional data services, serverless workflows, and data streaming.

The goal of this engineer position is to study, implement, and evaluate novel cross-tier and distributed caching strategies, alongside supporting data management techniques, for hierarchical multi-tier storage systems. The engineer will work closely with a PhD student on this topic.

References:

[1] Asit Dan and Don Towsley. 1990. An Approximate Analysis of the LRU and FIFO Buffer Replacement Schemes. SIGMETRICS Perform. Eval. Rev. 18, 1 (apr 1990), 143-152. https://doi.org/10.1145/98460.98525, [6] G. Aupy, O. Beaumont and L. Eyraud-Dubois, “Sizing and Partitioning Strategies for Burst-Buffers to Reduce IO Contention,” 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, 2019,

[7] ZHANG, Yazhuo, YANG, Juncheng, YUE, Yao, et al. {SIEVE} is simpler than {LRU}: an efficient {Turn-Key} eviction algorithm for web caches. In : 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24). 2024. p. 1229-1246.

[8] Juncheng Yang, Ziming Mao, Yao Yue, and K. V. Rashmi. GL-Cache: Group-level learning for efficient and high-performance caching. FAST’23, pages 115-134, 2023.

[9] RASHMI, K. V., CHOWDHURY, Mosharaf, KOSAIAN, Jack, et al.{EC-Cache}:{Load-Balanced},{Low-Latency} cluster caching with online erasure coding. In : 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 2016. p. 401-417.

Principales activités

• Study novel cross-tier and distributed caching strategies, alongside supporting data management techniques
• Prototype key caching strategies and data management techniques
• Run experiments and Evaluation of results
• Reporting, disseminating and presenting results.
• Participate in project meetings and discussions with other partners.

• A solid background in the area of distributed systems
• Experience with building systems and tools
• Software development skills: Python and Java
• Working experience in the areas of data management, storage and caching systems are advantageous
• Good collaborative and networking skills
• Excellent written and oral communication in English

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

The Inria Centre at Rennes University is one of Inria’s nine centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.