We propose NVLog, an NVM-based acceleration framework for disk file systems, designed to transparently harness the high performance of NVM within the legacy storage stack. NVLog provides on-demand byte-granularity sync absorption, reserving the fast DRAM path for asynchronous operations, meanwhile occupying NVM space only temporarily. To accomplish this, we designed a highly efficient log structure, developed mechanisms to address heterogeneous crash consistency, optimized for small writes, and implemented robust crash recovery and garbage collection methods. Compared to previous solutions, NVLog is lighter, more stable, and delivers higher performance, all while leveraging the mature kernel software stack and avoiding data migration overhead. Experimental results demonstrate that NVLog can accelerate disk file systems by up to 15.09x and outperform NOVA and SPFS in various scenarios by up to 3.72x and 324.11x, respectively.

With the rise of blockchain technology, the trend toward decentralization has spread to the field of remote storage, leading to the emergence of decentralized storage as a promising model. This change is highlighted by its features of open and fair access, reduced dependence on intermediaries, and strong privacy protections. However, similar to centralized storage, the decentralization of data management presents challenges, including the separation of ownership and control, along with the need for integrity auditing on externally managed data. The current popular centralized auditing model for the mainstream cloud storage cannot be directly used for decentralized storage environments. Additionally, Homomorphic Verification Tag (HVT)-based auditing models encounter significant problems such as high computational costs and inefficient auditing processes. In response to these needs, we introduce a novel Byzantine-tolerant Lazy Auditing framework (BLA) to ensure data integrity in decentralized storage settings. A key innovation is the hierarchical architecture used: the upper level employs a simplified Practical Byzantine Fault Tolerance (PBFT) protocol to help nodes reach a consensus on data integrity audits. At the lower level, nodes are grouped into clusters based on criteria such as accessibility, organized using a block design strategy. This approach reduces unnecessary information exchange during the auditing process. It maximizes parallel processing and strengthens fault tolerance and system resilience. By distributing data, it also reduces the impact of node failures. Our theoretical analyses and empirical evaluations clearly show that BLA reduces communication complexity compared with conventional PBFT protocols. Additionally, when compared with traditional HVT-based schemes, BLA demonstrates better storage efficiency and improved computational performance, making it a viable and effective solution for data integrity auditing in decentralized storage systems.

The emergence of cloud storage has solved many pain points of the traditional storage model. However, the issue of cloud storage data integrity - whether the cloud storage provider has kept the data intact - has raised concerns about cloud storage. Integrity auditing of cloud storage data allows users to know the integrity of the outsourced data without downloading it in its entirety. However it is not enough to know its integrity. Multi-replicas, as a common means of redundancy, improves the reliability of cloud storage data. And storing multi-replicas on multi-cloud storage providers (CSPs) enhances this feature. In a multi-replica multi-CSPs scenario, if the independence of CSPs is given full play and auditing is performed among CSPs, not only the introduction of third party auditor (TPA) can be eliminated, but also the tag generation, which is a huge computational overhead, can be removed. Inspired by this, in this paper we propose a new model for remote data integrity auditing: CIA (Collaborative Integrity Auditing). In addition to the reduction in computational overhead, the proposed scheme provides unprecedented support for free data blocking. The proposed scheme employs only hash functions in the calculation, which has a negligible computational overhead compared to the traditional bilinear pairing-based schemes. Theoretical analysis and experimental results show that the proposed scheme provides high efficiency and flexibility with security assurance, and can be used as a lightweight alternative to traditional remote data integrity auditing schemes in multi-replica multi-CSP scenarios.

BACKGROUND: Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms. RESULTS: In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse. CONCLUSIONS: Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1.