Optimized Disaster Recovery for Distributed Storage Systems: Lightweight Metadata Architectures to Overcome Cryptographic Hashing Bottleneck

• URL: http://arxiv.org/abs/2602.22237v1
• Date: Mon, 23 Feb 2026 21:34:25 GMT
• Title: Optimized Disaster Recovery for Distributed Storage Systems: Lightweight Metadata Architectures to Overcome Cryptographic Hashing Bottleneck
• Authors: Prasanna Kumar, Nishank Soni, Gaurang Munje,
• Abstract summary: This paper characterizes the operational conditions under which full or partial re-hashing becomes unavoidable.<n>The proposed framework assigns globally unique composite identifiers to data blocks at ingestion time-independent of content analysis enabling instantaneous delta during DR without any cryptographic overhead.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed storage architectures are foundational to modern cloud-native infrastructure, yet a critical operational bottleneck persists within disaster recovery (DR) workflows: the dependence on content-based cryptographic hashing for data identification and synchronization. While hash-based deduplication is effective for storage efficiency in steady-state operation, it becomes a systemic liability during failover and failback events when hash indexes are stale, incomplete, or must be rebuilt following a crash. This paper precisely characterizes the operational conditions under which full or partial re-hashing becomes unavoidable. The paper also analyzes the downstream impact of cryptographic re-hashing on Recovery Time Objective (RTO) compliance, and proposes a generalized architectural shift toward deterministic, metadata-driven identification. The proposed framework assigns globally unique composite identifiers to data blocks at ingestion time-independent of content analysis enabling instantaneous delta computation during DR without any cryptographic overhead.

Related papers

• FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse [4.210760734549566]
  FlashMem is a framework that distills intrinsic memory directly from transient reasoning states via computation reuse.<n>Experiments demonstrate that FlashMem matches the performance of heavy baselines while reducing inference latency by 5 times.
  arXiv  Detail & Related papers  (2026-01-09T03:27:43Z)
• Unleashing Degradation-Carrying Features in Symmetric U-Net: Simpler and Stronger Baselines for All-in-One Image Restoration [52.82397287366076]
  All-in-one image restoration aims to handle diverse degradations (e.g., noise, blur, adverse weather) within a unified framework.<n>In this work, we reveal a critical insight: well-crafted feature extraction inherently encodes degradation-carrying information.<n>Our symmetric design preserves intrinsic degradation signals robustly, rendering simple additive fusion in skip connections.
  arXiv  Detail & Related papers  (2025-12-11T12:20:31Z)
• Parallel Algorithms for Combined Regularized Support Vector Machines: Application in Music Genre Classification [5.98174311891588]
  We develop a distributed parallel alternating direction multipliers (ADMM) to efficiently compute CR-SVMs when data is stored in a distributed manner.<n>Experiments on synthetic and free music archiv datasets demonstrate the reliability, stability, and efficiency of the algorithm.
  arXiv  Detail & Related papers  (2025-12-08T11:41:06Z)
• Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition [51.03674130115878]
  We introduce the Knowledge-Informed Neural Network (KINN), a lightweight framework built upon a novel "compression-aggregation-compression" architecture.<n>KINN establishes a state-of-the-art in parameter-efficient recognition, offering exceptional generalization in data-scarce and out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2025-10-23T07:12:26Z)
• Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization [49.32786615205064]
  Split Inference (SI) partitions computation between edge devices and the cloud to reduce latency and protect user privacy.<n>Recent advances in Data Reconstruction Attacks (DRAs) reveal that intermediate features exchanged in SI can be exploited to recover sensitive input data.<n>Existing DRAs are typically effective only on shallow models and fail to fully leverage semantic priors.<n>We propose a novel GAN-based DRA framework with Progressive Feature Optimization (PFO), which decomposes the generator into hierarchical blocks and incrementally refines intermediate representations to enhance the semantic fidelity of reconstructed images.
  arXiv  Detail & Related papers  (2025-08-28T10:00:39Z)
• HexaMorphHash HMH- Homomorphic Hashing for Secure and Efficient Cryptographic Operations in Data Integrity Verification [0.0]
  This paper introduces an innovative approach using a lattice based homomorphic hash validating HexaHashMorph.<n>Our contributions present a viable solution for frequent update dissemination in expansive distributed systems, safeguarding both data integrity and system performance.
  arXiv  Detail & Related papers  (2025-07-01T18:53:23Z)
• Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs [67.0310240737424]
  We introduce a novel approach to safeguard the ownership of text datasets and effectively detect unauthorized use by the RA-LLMs.<n>Our approach preserves the original data completely unchanged while protecting it by inserting specifically designed canary documents into the IP dataset.<n>During the detection process, unauthorized usage is identified by querying the canary documents and analyzing the responses of RA-LLMs.
  arXiv  Detail & Related papers  (2025-02-15T04:56:45Z)
• Structured Token Retention and Computational Memory Paths in Large Language Models [0.0]
  This paper introduces a probabilistic selection framework that dynamically adjusts token persistence based on contextual significance.<n>It is extended through hierarchical memory allocation, refining retention efficiency through structured reallocation of token embeddings.<n>The integration of STR and CMP into an open-source model illustrates the adaptability of structured memory retention methodologies.
  arXiv  Detail & Related papers  (2025-02-05T11:59:22Z)
• Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval [67.52910255064762]
  We design a simple dual-stream structure, including a temporal layer and a hash layer. We first design a simple dual-stream structure, including a temporal layer and a hash layer. With the help of semantic similarity knowledge obtained from self-supervision, the hash layer learns to capture information for semantic retrieval. In this way, the model naturally preserves the disentangled semantics into binary codes.
  arXiv  Detail & Related papers  (2023-10-12T03:21:12Z)
• BGaitR-Net: Occluded Gait Sequence reconstructionwith temporally constrained model for gait recognition [1.151614782416873]
  We develop novel deep learning-based algorithms to identify occluded frames in an input sequence. We then reconstruct these frames by exploiting next-temporal information present in the gait sequence. Our LSTM-based model reconstructs occlusion and generates frames that are temporally consistent with the periodic pattern of a gait cycle.
  arXiv  Detail & Related papers  (2021-10-18T18:28:18Z)
• New advances in enumerative biclustering algorithms with online partitioning [80.22629846165306]
  This paper further extends RIn-Close_CVC, a biclustering algorithm capable of performing an efficient, complete, correct and non-redundant enumeration of maximal biclusters with constant values on columns in numerical datasets. The improved algorithm is called RIn-Close_CVC3, keeps those attractive properties of RIn-Close_CVC, and is characterized by: a drastic reduction in memory usage; a consistent gain in runtime.
  arXiv  Detail & Related papers  (2020-03-07T14:54:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.