Functional Broadcast Repair of Multiple Partial Failures in Wireless Distributed Storage Systems
Nitish Mital, Katina Kralevska, Cong Ling, Deniz Gunduz

TL;DR
This paper introduces a broadcast repair scheme for multiple partial failures in wireless distributed storage systems, reducing repair bandwidth and complexity while achieving optimal trade-offs.
Contribution
It derives the storage-bandwidth trade-off for partial repairs, proposes a high-probability feasible repair scheme, and demonstrates improved efficiency over traditional random linear codes.
Findings
Reduces repair bandwidth significantly using broadcast nature.
Achieves cut-set bound on the trade-off curve for certain parameters.
Lower overhead and computational complexity compared to random linear codes.
Abstract
We consider a distributed storage system with nodes, where a user can recover the stored file from any nodes, and study the problem of repairing partially failed nodes. We consider \textit{broadcast repair}, that is, surviving nodes transmit broadcast messages on an error-free wireless channel to the nodes being repaired, which are then used, together with the surviving data in the local memories of the failed nodes, to recover the lost content. First, we derive the trade-off between the storage capacity and the repair bandwidth for partial repair of multiple failed nodes, based on the cut-set bound for information flow graphs. It is shown that utilizing the broadcast nature of the wireless medium and the surviving contents at the partially failed nodes reduces the repair bandwidth per node significantly. Then, we list a set of invariant conditions that are…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Data Storage Technologies · Caching and Content Delivery · Cooperative Communication and Network Coding
