Flexible download time analysis of coded storage systems

published_or_final_versio

HTSC and FH_HTSC: XOR-based codes to reduce access latency in distributed storage systems

A massive distributed storage system is the foundation for big data operations. Access latency performance is a key metric in distributed storage systems since it greatly impacts user experience while existing codes mainly focus on improving performance such as storage overhead and repair cost. By generating parity nodes from parity nodes, in this paper we design new XOR-based erasure codes hierarchical tree structure code (HTSC) and high failure tolerant HTSC (FH_HTSC) to reduce access latency in distributed storage systems. By comparing with other popular and representative codes, we show that, under the same repair cost, HTSC and FH.HTSC codes can reduce access latency while maintaining favorable performance in other metrics. In particular, under the same repair cost, FH.HTSC can achieve lower access latency, higher or equal failure tolerance and lower computation cost compared with the representative codes while enjoying similar storage overhead. Accordingly, FH.HTSC is a superior choice for applications requiring low access latency and outstanding failure tolerance capability at the same time.postprin

Performance models of access latency in cloud storage systems

Access latency is a key performance metric for cloud storage systems and has great impact on user experience, but most papers focus on other performance metrics such as storage overhead, repair cost and so on. Only recently do some models argue that coding can reduce access latency. However, they are developed for special scenarios, which may not reflect reality. To fill the gaps between existing work and practice, in this paper, we propose a more practical model to measure access latency. This model can also be used to compare access latency of different codes used by different companies. To the best of our knowledge, this model is the first to provide a general method to compare access latencies of different erasure codes.postprin

Reducing delay of flexible download in coded distributed storage system

postprin

Latency performance model of direct and k-access reads in distributed storage systems

2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS

Differentially Private Federated Clustering over Non-IID Data

In this paper, we investigate federated clustering (FedC) problem, that aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server, meanwhile considering data privacy. Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a non-convex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution. Then a novel FedC algorithm using differential privacy (DP) technique, referred to as DP-FedC, is proposed in which partial clients participation and multiple local model updating steps are also considered. Furthermore, various attributes of the proposed DP-FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of non-identically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP-FedC. Then some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP-FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results.Comment: 31 pages, 4 figures, 1 tabl

Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification

Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients' data. This paper delves into a class of federated problems characterized by non-convex and non-smooth loss functions, that are prevalent in FL applications but challenging to handle due to their intricate non-convexity and non-smoothness nature and the conflicting requirements on communication efficiency and privacy protection. In this paper, we propose a novel federated primal-dual algorithm with bidirectional model sparsification tailored for non-convex and non-smooth FL problems, and differential privacy is applied for strong privacy guarantee. Its unique insightful properties and some privacy and convergence analyses are also presented for the FL algorithm design guidelines. Extensive experiments on real-world data are conducted to demonstrate the effectiveness of the proposed algorithm and much superior performance than some state-of-the-art FL algorithms, together with the validation of all the analytical results and properties.Comment: 30 pages, 8 figure

Inelastic X-Ray Scattering Study of Exciton Properties in an Organic Molecular crystal

Excitons in a complex organic molecular crystal were studied by inelastic x-ray scattering (IXS) for the first time. The dynamic dielectric response function is measured over a large momentum transfer region, from which an exciton dispersion of 130 meV is observed. Semiempirical quantum chemical calculations reproduce well the momentum dependence of the measured dynamic dielectric responses, and thus unambiguously indicate that the lowest Frenkel exciton is confined within a fraction of the complex molecule. Our results demonstrate that IXS is a powerful tool for studying excitons in complex organic molecular systems. Besides the energy position, the IXS spectra provide a stringent test on the validity of the theoretically calculated exciton wave functions.Comment: 4 pages, 4 figure

Critical exponents of the two-layer Ising model

The symmetric two-layer Ising model (TLIM) is studied by the corner transfer matrix renormalisation group method. The critical points and critical exponents are calculated. It is found that the TLIM belongs to the same universality class as the Ising model. The shift exponent is calculated to be 1.773, which is consistent with the theoretical prediction 1.75 with 1.3% deviation.Comment: 7 pages, with 10 figures include