On Distributed Storage Allocations of Large Files for Maximum Service Rate

Peng, Pei; Soljanin, Emina

doi:10.1109/allerton.2018.8635856

Cited by 9 publications

(14 citation statements)

References 23 publications

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“…Summing up these three inequalities and dividing them by two results [4]. Summing up these four inequalities and dividing by three gives 4 i=1 λ i ≤ 16 3 .…”

Section: B Systematic First Order Reed-muller Codesmentioning

confidence: 99%

“…Amongst the most significant considerations in the design of the cloud storage systems, has been always serving a large number of users concurrently, which is also very important in several emerging applications such as distributed learning and fog computing. The service rate has been very recently recognized as an important performance metric that measures the number of users that can be simultaneously served by the storage system [1]- [4]. Maximizing the service rate reduces the latency experienced by users, particularly in high traffic.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, for k = 4 the demand vector 3 • e i + 1 • e j for all i, j ∈ [k] with i = j can be satisfied. For achieving the demand vector 4 3 •1, one can use all the systematic recovery sets {e 1 }, {e 2 }, {e 3 }, {e 4 } with capacity 1. Moreover, the remaining four columns can be used to build up four recovery sets consisting of a unique recovery set of cardinality 3 for each file f i , i ∈ [4], and from each of these sets the rate of 1 3 can be satisfied.…”

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confidence: 99%

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A Geometric View of the Service Rates of Codes Problem and its Application to the Service Rate of the First Order Reed-Muller Codes

Kazemi

Kurz

Soljanin

2020

2020 IEEE International Symposium on Information Theory (ISIT)

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We investigate the problem of characterizing the service rate region of a coded storage system by introducing a novel geometric approach. The service rate is an important performance metric that measures the number of users that can be simultaneously served by the storage system. One of the most significant advantages of our introduced geometric approach over the existing approaches is that it allows one to derive bounds on the service rate of a code without explicitly knowing the list of all possible recovery sets. As an illustration of the power of our geometric approach, we derive upper bounds on the service rate of the first order Reed-Muller codes and the simplex codes. Then, we show how these upper bounds can be achieved. Moreover, utilizing the same geometric technique, we show that given the service rate region of a code, a lower bound on the minimum distance of the code can be obtained.

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“…Summing up these three inequalities and dividing them by two results [4]. Summing up these four inequalities and dividing by three gives 4 i=1 λ i ≤ 16 3 .…”

Section: B Systematic First Order Reed-muller Codesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Geometric View of the Service Rates of Codes Problem and its Application to the Service Rate of the First Order Reed-Muller Codes

Kazemi

Kurz

Soljanin

2020

2020 IEEE International Symposium on Information Theory (ISIT)

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“…Research interest in using coding theoretical techniques for straggler mitigation in distributed content download and distributed computing is rapidly growing. The early body of work focused on content download, see e.g., [39][40][41][42][43]. Using codes for straggler mitigation in distributed computing started in [12] where the authors proposed the use of MDS codes for distributed linear machine learning algorithms in homogeneous workers setting.…”

Section: Related Workmentioning

confidence: 99%

Private and rateless adaptive coded matrix-vector multiplication

Bitar

Xing

Keshtkarjahromi

et al. 2021

J Wireless Com Network

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Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (1) the privacy requirements of IoT applications and devices, and (2) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.

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“…on Information Theory (ISIT) [1] and 2018 56th Annual Allerton Conf. on Communication, Control, and Computing [2]. Part of this research is based upon work supported by the National Science Foundation under Grant No.…”

Section: Introductionmentioning

confidence: 99%

Distributed Storage Allocations for Optimal Service Rates

Pei¹,

Noori²,

Soljanin³

2021

Preprint

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Redundant storage maintains the performance of distributed systems under various forms of uncertainty. This paper considers the uncertainty in node access and download service. We consider two access models under two download service models. In one access model, a user can access each node with a fixed probability, and in the other, a user can access a random fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the randomness associated with the file size is negligible. In the second (large file) model, randomness is associated with both the file size and the system's operations. We focus on the service rate of the system. For a fixed redundancy level, the systems' service rate is determined by the allocation of coded chunks over the storage nodes. We consider quasi-uniform allocations, where coded content is uniformly spread among a subset of nodes. The question we address asks what the size of this subset (spreading) should be. We show that in the small file model, concentrating the coded content to a minimum-size subset is universally optimal. For the large file model, the optimal spreading depends on the system parameters. These conclusions hold for both access models.

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On Distributed Storage Allocations of Large Files for Maximum Service Rate

Cited by 9 publications

References 23 publications

A Geometric View of the Service Rates of Codes Problem and its Application to the Service Rate of the First Order Reed-Muller Codes

A Geometric View of the Service Rates of Codes Problem and its Application to the Service Rate of the First Order Reed-Muller Codes

Private and rateless adaptive coded matrix-vector multiplication

Distributed Storage Allocations for Optimal Service Rates

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