In this paper, we propose a new decoding algorithm for extremely skewed distribution, so that it can omit reading unnecessary bits and thus improve decoding performance. In particular, the proposed method does not require additional space to store the encoded stream. Specifically, we first reorder the compressed bit sequence as in [1], so that to support direct access without extra space. Then, we propose a labeling method to generate a temporary label corresponding to the encountered block, in order to indicate whether the length of the codeword stored in that block is determined. If it is determined, we can identify where to read the necessary bits for decoding the desired symbol. Otherwise, we read bits from the later blocks to update the labels until the codeword lengths are determined.

It is well-known that erasure codes have been widely applied in the distributed storage systems (DSSs). This paper concentrates on the update bandwidth of erasure codes, which is defined as the system network IO consumption required when a node is updated. Based on the OUB-codes [1] and MDS array codes [2], we introduce the array-OUB-codes which are vertical MDS array codes with triple fault tolerant and minimal update bandwidth. In addition, according to our experiments implemented by C++ program, our array-OUB-codes achieve better encoding and decoding performance compared with OUB-codes. Moreover, through the update bandwidth IO test, our array-OUB-codes can increase the average throughput by 37.5% compared with the RS scheme in [3].

Distributed storage systems ensure data reliability and availability in the event of a disk failure by employing array codes. Generalized Expanded Blaum-Roth (GEBR) codes and Generalized Expanded Independent Parity(GEIP) codes are MDS array codes with local properties that can tolerate multiple disk failures while maintaining good repair performance. However, when repairing a single-disk failure, the repair bandwidth (defined as the total number of symbols downloaded from other surviving disks in repairing the single-disk failure) is not optimal. To address this issue, we propose a random search algorithm that uses a simulated annealing algorithm to optimize the repair strategy. It starts with a viable repair strategy and gradually replaces the current strategy with another that downloads less data. Experimental results demonstrate that our proposed algorithm can significantly reduce the data required to repair single-disk failure for both GEBR and GEIP codes by 25%-38%, under the selection of suitable parameters, when compared with traditional repair methods. Moreover, our repair algorithm can be done in polynomial time.

With its wide range of applications, the Internet shows a future trend towards abundant and diverse data resources with multiple types of identifiers (multi-identifiers). However, the legacy Domain Name System (DNS) in the current TCP/IP network architecture has failed to manage these identifiers due to the centralized security issue. While some decentralized DNS alternatives have been proposed, they also face scalability issues. In this paper, we propose a blockchain-based Hierarchical Multi-Identifier System, named H-MIS, as a DNS alternative. Specially, it realizes optimal decentralization and scalability by introducing the Zero-Knowledge rollup (ZK-rollup) solution to synchronize the upper and lower on-chain identifier data, as well as off-chain associated resource data. Finally, we implement H-MIS on Ethereum and evaluate its performance. The experimental results indicate that compared to the original MIS and Ethereum Name Service (ENS), H-MIS has advantages in such aspects as efficiency, data consumption, and Gas fees.

Reed-Solomon (RS) codes are widely used in storage systems to provide high data reliability. The existing RS codes are constructed over finite field with field size larger than the code length that have high encoding/decoding complexity. In this paper, we propose a new construction for RS codes over a cyclic ring instead of finite field. Our new RS codes only incurs XOR and cyclic-shift operation in the encoding/decoding processes, and therefore have lower encoding/decoding complexity than the existing RS codes. In addition, we show that we can employ the efficient Reed-Muller (RM) transform in our new RS codes that reduces the encoding/decoding complexity. Moreover, we implement our new RS codes by C++ and show that our new RS codes have better encoding/decoding performance than the existing RS codes for the evaluated parameters.

A locally repairable code (LRC) is a linear code that each code symbol can be encoded by downloading a small number (at most r) of other code symbols. In this paper, we use the parity-check matrix approach to obtain 58 classes code parameters for octonion-field optimal LRCs of distance 3. Then three optimal oction-field LRCs of distance 4 with locality r = 2 and r = 3 are given by adding linear independent set to the parity-check matrix.

Improving the repair performance of erasure code is a critical issue in order to maintain high data reliability in modern large-scale storage systems. Locally repairable codes (LRC) can improve the repair performance by locally repairing any single-node failure. In modern distributed cluster storage systems, the cross-cluster bandwidth is more scarce than the innercluster bandwidth. In this paper, we propose a well-designed placement strategy for LRC which is suitable for storing hot data. We show that our proposed placement strategy can reduce the cross-cluster bandwidth overhead in repairing node failures. Compared with the flat placement, the cross-cluster repair bandwidth can be reduced by more than 91.7% with our placement strategy in repairing one single-node failure, while the cross-cluster repair bandwidth reduction is 90.8% in repairing two-node failures.

A (k+2,k,m) maximum distance separable (MDS) array code is an array of size m × (k + 2) such that any k out of k +2 columns can retrieve all km information bits. The (k + 2,k,m) array code can correct a single criss-cross error means that the code can retrieve all km information bits if any single column or row is in error. In this paper, we propose new systematic (k + 2,k,m) MDS array codes that can correct a single criss-cross error. In addition, we present efficient decoding algorithms that has lower decoding complexity for any two-row erasures or two-column erasures or one-row plus one-column erasures or one-row error or one-column error. Moreover, we show that our codes have much lower update complexity than that of the existing MDS array codes correcting a single criss-cross error [1].

As the Internet and the big data system evolve rapidly, the deployment of distributed applications becomes widespread, promoting the development of Distributed File System (DFS). The existing defense technologies for DFS, such as detection or patching, mainly aim to protect the system from known attacks and vulnerabilities. However, it is difficult for those systems to solve the growing security issues from the unknown threats due to their passiveness and hysteresis. In this paper, we propose MDFS, a mimic defense theory based architecture for DFS with the capability to improve the data security. Mimic Defense (MD), a proactive defense embedded in MDFS, emphasizes dynamism, heterogeneity and redundancy. The key benefits of MD are transferring the attack surface as well as increasing the cost of modification.

Ceph is a reliable, scalable, unified distributed storage system, and recently has become one of OpenStack's standard open source storage solutions. For the reason of the low performance, Ceph Filesystem (Cephfs) cannot employ erasure codes directly, especially for binary Reed-Solomon (BRS) codes, whose size of the parity block is larger than that of the data block. To address these problems, we implemented efficient BRS codes, and filled the gap between BRS codes and Cephfs by a simple conversion. In addition to that, an efficient framework consisting of filesystem, cache tier and storage tier was adopted in Ceph to ensure that the file data is finally stored with the erasure coding technology. The experimental results show that such a design spends a small amount of additional cost but obtains much better effects.

Distributed file system has emerged in recent years as an efficient solution to store the large amount of data produced anytime and anywhere. In order to guarantee data reliability, it is necessary to introduce redundancy to the storage systems. Compared to simple replication, practical systems are increasingly adopting erasure codes for better storage efficiency. However, traditional erasure codes such as maximum-distance-separable (MDS) codes, are designed over a large finite field, which inevitably hinders the wide implementation of erasure codes. In this paper, we propose a new family of MDS codes with high computation efficiency. More specifically, only XOR operation is included in the encoding process to generate parity blocks. Upon failure of a storage node, we use the efficient Zigzag decoding method to recover the failed blocks, which achieves the optimal encoding and an efficient decoding. Furthermore, we implement the proposed codes in a distributed file system, and the results show the high performance of the new codes.

Recently, traditional erasure codes such as Reed-Solomon (RS) codes have been increasingly deployed in many distributed storage systems to reduce the large storage overhead incurred by the widely adopted replication scheme. However, these codes require significantly high resources with respect to network bandwidth and disk I/O during recovery of missing or unavailable data. It is referred as the recovery problem. In this paper, we dedicate to integrating exact minimum bandwidth regenerating codes into practical systems to solve the recovery problem. We design an implementation friendly storage code with the recently proposed BASIC framework and ZigZag decodable code for saving recovery bandwidth and disk I/O. We build a system called STORE based on this code and evaluate our prototype atop a HDFS cluster testbed with 21 nodes. As shown in this paper, the recovery bandwidth achieves minimum approximately during recovery of both data block and parity block with STORE. Another attractive result is that the recovery disk I/O also achieves minimum approximately during recovery of data block. Due to the reduction of recovery bandwidth and disk I/O, the degraded read throughput is boosted notably.

Regenerating codes (RGC) are a class of distributed storage codes that can provide efficient repair of failure nodes in distributed storage systems. In general, the reduction of repair bandwidth of RGC is at the expense of a small increase in storage cost and computational cost. The high computational complexity of data coding over a finite field of large size makes it unsuitable for practical distributed storage systems. BASIC codes, which stands for Binary Addition and Shift Implementable Convolutional codes, is introduced in [1] with the aim of reducing computational complexity, while retaining the benefits of RGC. In this paper, we present a construction of exact-repair BASIC codes at the minimum-storage point (MSR). A helper node needs no coding to repair a failure node for the minimum-storage BASIC codes. The results of simulation show minimum-storage BASIC codes outperform Cauchy Reed-Solomon codes in both repairing cost and coding cost.

The explosion of big data stored in distributed file systems calls for more efficient storage paradigms. While replication is widely used to ensure data availability, erasure codes provide a much better tradeoff between storage and availability. Reed-Solomon (RS) codes are the standard design choice, however, their high repair cost is often considered an unavoidable price to pay for high storage efficiency and high reliability. BASIC codes can achieve the optimal tradeoff between storage capacity and repair bandwidth with much less complexity of regenerating codes, which is first proposed in [1]. This paper integrate one construction of the minimum storage BASIC (MS-BASIC) codes [2] into a Hadoop HDFS cluster testbed with up to 22 storage nodes. We demonstrate that MS-BASIC codes conform to the theoretical findings and achieve recovery bandwidth saving compared to the conventional recovery approach based on RS codes.