erasure coding

Project Idea Description Topics: Storage Systems, Machine Learning, Erasure Coding Skills: C/C++, Python, PyTorch, Bash scripting, Linux, Erasure Coding, Machine Learning Difficulty: Hard Size: Large (350 hours) Mentors: Meng Wang (primary contact), John Bent Large-scale data centers store immense amounts of user data across a multitude of disks, necessitating redundancy strategies like erasure coding (EC) to safeguard against disk failures.

Project Idea Description Topics: Storage Systems, Erasure Coding Skills: C/C++, Java, Bash scripting, Linux, HDFS, ZFS, Erasure Coding Difficulty: Hard Size: Large (350 hours) Mentors: Meng Wang (Main contact person) and Anjus George Multi-Level Erasure Coding (MLEC), which performs erasure coding at both network and local levels, has seen large deployments in practice.

Massive storage systems rely heavily on erasure coding (EC) to protect data from drive failures and provide data durability. Existing storage systems mostly adopt single-level erasure coding (SLEC) to protect data, either performing EC at the network level or performing EC at the local level.