4.5 Article

Coded Caching Schemes With Reduced Subpacketization From Linear Block Codes

期刊

IEEE TRANSACTIONS ON INFORMATION THEORY
卷 64, 期 4, 页码 3099-3120

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIT.2018.2800059

关键词

Coded caching; resolvable designs; cyclic codes; subpacketization level

资金

  1. National Science Foundation [CCF-1718470, CCF-1320416, CCF-1149860]
  2. Division of Computing and Communication Foundations
  3. Direct For Computer & Info Scie & Enginr [1149860] Funding Source: National Science Foundation

向作者/读者索取更多资源

Coded caching is a technique that generalizes conventional caching and promises significant reductions in traffic over caching networks. However, the basic coded caching scheme requires that each file hosted in the server be partitioned into a large number (i.e., the subpacketization level) of non-overlapping subfiles. From a practical perspective, this is problematic as it means that prior schemes are only applicable when the size of the files is extremely large. In this paper, we propose coded caching schemes based on combinatorial structures called resolvable designs. These structures can be obtained in a natural manner from linear block codes whose generator matrices possess certain rank properties. We obtain several schemes with subpacketization levels substantially lower than the basic scheme at the cost of an increased rate. Depending on the system parameters, our approach allows us to operate at various points on the subpacketization level vs. rate tradeoff.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Computer Science, Information Systems

Sum-Networks From Incidence Structures: Construction and Capacity Analysis

Ardhendu Tripathy, Aditya Ramamoorthy

IEEE TRANSACTIONS ON INFORMATION THEORY (2018)

Article Telecommunications

Erasure Coding for Distributed Matrix Multiplication for Matrices With Bounded Entries

Li Tang, Konstantinos Konstantinidis, Aditya Ramamoorthy

IEEE COMMUNICATIONS LETTERS (2019)

Article Engineering, Electrical & Electronic

Straggler-Resistant Distributed Matrix Computation via Coding Theory: Removing a Bottleneck in Large-Scale Data Processing

Aditya Ramamoorthy, Anindya Bijoy Das, Li Tang

IEEE SIGNAL PROCESSING MAGAZINE (2020)

Article Computer Science, Hardware & Architecture

Resolvable Designs for Speeding Up Distributed Computing

Konstantinos Konstantinidis, Aditya Ramamoorthy

IEEE-ACM TRANSACTIONS ON NETWORKING (2020)

Article Computer Science, Hardware & Architecture

Asynchronous Coded Caching With Uncoded Prefetching

Hooshang Ghasemi, Aditya Ramamoorthy

IEEE-ACM TRANSACTIONS ON NETWORKING (2020)

Article Computer Science, Information Systems

Efficient and Robust Distributed Matrix Computations via Convolutional Coding

Anindya Bijoy Das, Aditya Ramamoorthy, Namrata Vaswani

Summary: In distributed matrix computations, the problem of stragglers can be addressed by a convolutional coding approach that offers optimal straggler resilience and numerical robustness. Another approach with slightly higher decoding complexity allows operation close to the storage capacity lower bound while its numerical robustness can be quantified theoretically. Extensive experiments on the AWS cloud platform support these claims.

IEEE TRANSACTIONS ON INFORMATION THEORY (2021)

Article Computer Science, Information Systems

Coded Sparse Matrix Computation Schemes That Leverage Partial Stragglers

Anindya Bijoy Das, Aditya Ramamoorthy

Summary: Distributed matrix computations can be affected by slow or failed worker nodes, but coded computation can mitigate these issues. However, using MDS codes may destroy the sparsity of sparse matrices and ignore the partial computations from slow nodes. This research proposes a scheme that leverages partial computation and reduces coding time, leading to improved numerical stability in the decoding process.

IEEE TRANSACTIONS ON INFORMATION THEORY (2022)

Article Computer Science, Information Systems

Numerically Stable Coded Matrix Computations via Circulant and Rotation Matrix Embeddings

Aditya Ramamoorthy, Li Tang

Summary: Polynomial based methods can mitigate the effect of stragglers in distributed matrix computations. However, they suffer from serious numerical issues. This research proposes a novel approach using circulant permutation matrices and rotation matrices for coded matrix computation, and demonstrates an upper bound on the condition number of the recovery matrices.

IEEE TRANSACTIONS ON INFORMATION THEORY (2022)

Article Engineering, Electrical & Electronic

Federated Over-Air Subspace Tracking From Incomplete and Corrupted Data

Praneeth Narayanamurthy, Namrata Vaswani, Aditya Ramamoorthy

Summary: This work addresses the problem of Subspace Tracking with missing data and outliers. It proposes a novel algorithm that does not assume piecewise constant subspace change and is simpler compared to previous approaches. Furthermore, the study extends its approach to solving these problems in federated settings and over-air data communication mode.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2022)

Article Engineering, Electrical & Electronic

Distributed Matrix Multiplication Using Group Algebra for On-Device Edge Computing

Kyungrak Son, Aditya Ramamoorthy, Wan Choi

Summary: By leveraging group theory, this study proposes a distributed matrix multiplication scheme using the cyclic group and identifies the condition for perfect reconstruction. The proposed scheme demonstrates better error performance in a noisy channel compared to the uncoded scheme due to its diversity gain.

IEEE SIGNAL PROCESSING LETTERS (2021)

暂无数据