Clustered Storage Systems Research Articles

CephArmor: A Lightweight Cryptographic Interface for Secure High-Performance Ceph Storage Systems

Clustered storage systems are dominant solutions in modern-day data production. Ceph represents a sustainable clustered storage solution, supporting object, block, and file storage capabilities, with no single point of failure. Despite the strong management abilities, security remains a serious concern in the Ceph storage system. To date, authentication and access control are the only supported security protocols in the system. Data confidentiality will be undermined if a malicious insider or outside intruder accesses to storage devices. This study proposes a lightweight cryptographic-based interface, CephArmor, for a Ceph storage system to ensure data confidentiality in storage. The proposed method has been integrated into the stable Ceph version, Pacific, and evaluated through 45Drives Storinator servers, a commercial hardware commodity for storage solutions in real-world scenarios. The experimental results denote a nuanced overhead in terms of elapsed time, throughput, average operations per second, and latency on a write operation. While the read operations illustrated near zero performance overhead.

Optimal Repair-Scaling Trade-off in Locally Repairable Codes: Analysis and Evaluation

How to improve the repair performance of erasure-coded storage is a critical issue for maintaining high reliability of modern large-scale storage systems. Locally repairable codes (LRC) are one popular family of repair-efficient erasure codes that mitigate the repair bandwidth and are deployed in practice. To adapt to the changing demands of access efficiency and fault tolerance, modern storage systems also conduct frequent scaling operations on erasure-coded data. In this article, we analyze the optimal trade-off between the repair and scaling performance of LRC in clustered storage systems. Specifically, we focus on two optimal repair-scaling trade-offs, and design placement strategies that operate along the two optimal repair-scaling trade-off curves subject to the fault tolerance constraints. We prototype and evaluate our placement strategies on a LAN testbed, and show that they outperform the conventional placement schemes in repair and scaling operations.

Cluster-Aware Scattered Repair in Erasure-Coded Storage: Design and Analysis

Erasure coding is a storage-efficient means to guarantee data reliability in today's commodity storage systems, yet its repair performance is seriously hindered by the substantial repair traffic. Repair in clustered storage systems is even complicated because of the scarcity of the cross-cluster bandwidth. We present <inline-formula><tex-math notation="LaTeX">${\sf ClusterSR}$</tex-math></inline-formula> , a cluster-aware scattered repair approach. <inline-formula><tex-math notation="LaTeX">${\sf ClusterSR}$</tex-math></inline-formula> minimizes the cross-cluster repair traffic by carefully choosing the clusters for reading and repairing chunks. It further balances the cross-cluster repair traffic by scheduling the repair of multiple chunks. Large-scale simulation and Alibaba Cloud ECS experiments show that <inline-formula><tex-math notation="LaTeX">${\sf ClusterSR}$</tex-math></inline-formula> can reduce 5.6-52.7 percent of the cross-cluster repair traffic and improve 14.4–68.8 percent of the repair throughput.

A survey on novel classification of deduplication storage systems

The huge blast of information caused a lot of dilemmas in both storage and retrieval procedures. The enlargement in a massive quantity of digital data requirements imposes more storage space, which in turn radically increases performance and cost of backup. Data deduplication is one of the techniques that vanishes replicated data, decreases the bandwidth, and minimizes the disk usage and cost. Since various researches have been broadly considered in the literature, this paper reviews the ideas, categories, and different storage approaches that use data deduplication. Apart from the well-known classification that uses Granularity, Side, Timing, and Implementation for classifying the deduplication approaches, a new classification principle is adopted using the storage location. This classification identifies and describes the diverse methods. Moreover, the deduplication systems are comprehensively described according to the storage location, including Local, Centralized, and Clustered storage systems. Furthermore, the describing objectives, used techniques, features, and drawbacks of the most advanced methods of each type are broadly tackled. Finally, the major deduplication systems' challenges are recognized and illustrated.

Clustering Storage Method for Digital Evidence Storage Using Software Defined Storage

The increasing of cybercrime means an increase in the number of digital evidences produced in these criminal activities. Also, to pay attention to the security and integrity of digital data, the construction of digital evidence storage must pay attention to the need for convenience in adding storage and the need for investigators to avoid geographical problems in carrying out their duties. An ideal implementation of the problem is the development of network-based digital evidence storage using the Software-Defined Storage to build a clustered storage system. In this study, we build a clustered storage system using Ceph that will be functioned as digital evidence storage. Ceph is a Software-Defined Storage which is designed to provide excellent performance, reliability and scalability. On the other hand, since digital evidence is fragile, then the security of storage system must also be considered so that the stored digital evidence in the storage system can be accepted in court. The security includes aspects of confidentiality, integrity and availability aspects. We were able to build a storage system using Ceph and as a result, our system storage has met the aforementioned scalability, confidentiality, integrity and availability aspects. The storage system is expected to contribute in terms of digital evidence storage and preservations.

The Storage Versus Repair-Bandwidth Trade-off for Clustered Storage Systems

We study a generalization of the setting of regenerating codes, motivated by applications to storage systems consisting of clusters of storage nodes. There are $n$ clusters in total, with $m$ nodes per cluster. A data file is coded and stored across the $mn$ nodes, with each node storing $\alpha$ symbols. For availability of data, we require that the file be retrievable by downloading the entire content from any subset of $k$ clusters. Nodes represent entities that can fail. We distinguish between intra-cluster and inter-cluster bandwidth (BW) costs during node repair. Node-repair in a cluster is accomplished by downloading $\beta$ symbols each from any set of $d$ other clusters, dubbed remote helper clusters, and also up to $\alpha$ symbols each from any set of $\ell$ surviving nodes, dubbed local helper nodes, in the host cluster. We first identify the optimal trade-off between storage-overhead and inter-cluster repair-bandwidth under functional repair, and also present optimal exact-repair code constructions for a class of parameters. The new trade-off is strictly better than what is achievable via space-sharing existing coding solutions, whenever $\ell > 0$. We then obtain sharp lower bounds on the necessary intra-cluster repair BW to achieve optimal trade-off. Our bounds reveal the interesting fact that, while it is beneficial to increase the number of local helper nodes $\ell$ in order to improve the storage-vs-inter-cluster-repair-BW trade-off, increasing $\ell$ not only increases intra-cluster BW in the host-cluster, but also increases the intra-cluster BW in the remote helper clusters. We also analyze resilience of the clustered storage system against passive eavesdropping by providing file-size bounds and optimal code constructions.

Rack Aware Data Placement for Network Consumption in Erasure-Coded Clustered Storage Systems

The amount of encoded data replication in an erasure-coded clustered storage system has a great impact on the bandwidth consumption and network latency, mostly during data reconstruction. Aimed at the reasons that lead to the excess data transmission between racks, a rack aware data block placement method is proposed. In order to ensure rack-level fault tolerance and reduce the frequency and amount of the cross-rack data transmission during data reconstruction, the method deploys partial data block concentration to store the data blocks of a file in fewer racks. Theoretical analysis and simulation results show that our proposed strategy greatly reduces the frequency and data volume of the cross-rack transmission during data reconstruction. At the same time, it has better performance than the typical random distribution method in terms of network usage and data reconstruction efficiency.

PP: Popularity-based Proactive Data Recovery for HDFS RAID systems

Clustered storage systems as HDFS have become a popular platform for handling Big Data, susceptible to failures and data recovery whenever a failure is detected. However, existing recovery schemes in HDFS are passive, which affects the processing efficiency of MapReduce jobs and degrades the performance of Hadoop storage systems. In order to address this problem, this paper proposes a novel scheme called PP (Popularity-based Proactive Data Recovery), that significantly boost the recovery efficiency of HDFS RAID systems. PP tracks the popular data and immediately recovers the missing popular data when a node fails in Hadoop system, which effect and variation on the performance of MapReduce jobs is minimal. The lightweight prototype implementation of PP and extensive experiments demonstrate that, compared with Hadoop’s default locally-first scheduling and the degraded-first scheduling, PP significantly reduces the recovery time as well the execution time of MapReduce jobs concurrently.

Skewly replicating hot data to construct a power-efficient storage cluster

The exponential data growth is presenting challenges to traditional storage systems. Component-based cluster storage systems, due to their high scalability, are becoming the architecture of next generation storage systems. Cluster storage systems often use data replication to ensure high availability, fault tolerance, and load balance. However, this kind of data replication not only consumes a large amount of storage resources, but also generates more energy consumption. This paper presents a power-aware data replication strategy by leveraging data access behavior. This strategy uses 80/20 rule (80% of the data accesses often go to 20% of the storage space) to skewly replicate only a small amount of frequently accessed data. Furthermore, the storage nodes are divided into a hot node set and a cold node set. Hot nodes, which store a small amount of hot data copies, are always in an active state to guarantee the QoS of the system. The cold nodes which store a large volume of infrequently accessed cold data are placed in a low-power state, thus reducing the energy consumption of the cluster storage system. Simulation results show that the proposed strategy can effectively reduce the resource and energy consumption of the system, while ensuring system performance.

HPRD: High Performance Tiered Replica Distribution in Cluster Storage System

The cluster system exists as a variety of different machines. There are Nodes with high performance and nodes with lower performance. The distribution of data on each machine and I / O are determined by the equalization of load balancing. According to the traditional way of clustering data distribution, high performance node performance generally can't fulfill full potential. In this way, the lower performance nodes will become bottleneck. To address this issue we carried out some research and propose a High Performance Tiered Replica Distribution (HPRD) a way of the replica data distribution in the cluster storage system. HPRD can fulfill full high-performance nodes potential in the aspect of the performance benefits and high performance nodes can share more high-performance I/O load. Such performance bottleneck will not exist in the lower performance nodes. It can improve the performance of cluster system and the cluster system resource Utilization rate.It also can reduce inefficient use of resources.

Tiered Adaptive Large-Scale Storage System with High Performance

There are various calculations, transmission, and storage devices in terms of performance or reliability characteristics in great physical differences exist of large-scale cluster storage systems. Meanwhile, the actual traffic load data access for storage devices is also not uniform in space and time and there is a big difference. It is unrealistic and unwise if all the data stored on the high-performance devices. In order to resolve this problem effectively, we propose large-scale adaptive tiered storage system architecture in which structure can carry out effective monitoring access to the load and adapting allocation of storage resources based on the application environment. This can fulfill the full potential to the advantages of high-performance storage nodes to improve the performance of large-scale clustered storage systems.

CaPaS: an optimal security-aware cache replacement algorithm for cluster storage systems

In this paper, we introduce a novel dynamic cache management approach, i.e., a cache partitioning system (CaPaS), to optimise security levels in contemporary cluster storage systems for data-intensive applications with acceptable disk response times. CaPaS utilises an adaptive security control mechanism to adjust for varying workload conditions initiated by client-issued disk requests as well as security requirements and contains a cache partitioning scheme, a response-time estimator, and an adaptive security quality controller. The CaPaS algorithm solves a non-linear optimisation problem to increase the quality of security of disk requests in cluster storage systems while ensuring the disk requests to be completed within their desired response times. The efficiency of the proposed algorithm has been tested with the newly defined performance indicators on a cluster storage system including CaPaS, eight cryptographic and seven integrity services. Numerical simulation results demonstrate that the CaPaS algorithm improves the overall system performance enormously over two traditional baseline cache management algorithms.

Core-Edge design of storage area networks—A Single-edge formulation with problem-specific cuts

Optimization and heuristic methods for the design of medium to large storage area networks (SANs) are in the early stages of development, but are required if large clustered storage systems are to become a viable alternative to expensive monolithic storage. We present here a new mixed-integer formulation for optimal design of a storage area network. Our formulation models the Single-edge Core-Edge topology. Using a testbed of medium to large problems, we compare the solution times for our new formulation to the current benchmark in the literature—our formulation solves in significantly less time with an off-the-shelf optimization software package. We also generate problem-specific cuts to further reduce the solution time for our formulation. An algorithm, which includes an integer programming subproblem, is described for generating some of these cuts. For all test problems, the cuts yield a further reduction in the solution time.

Reliability analysis model of cluster storage system by object grouping

The reliability of the large-scale cluster storage system is one important research aspect in the related domain. A high-availability data objects placement algorithm was proposed. It groups objected into redundancy sets using RAID at the algorithm level. The redundancy allowed us to reconstruct any corrupted data objects and storage nodes when it failed and assured efficiently the high availability of storage system. The availability of storage system was quantitatively analyzed by using Markov reward model, and the computing results indicate the algorithm is efficient.

RISC: A resilient interconnection network for scalable cluster storage systems

The explosive growth of data generated by information digitization has been identified as the key driver to escalate storage requirements. It is becoming a big challenge to design a resilient and scalable interconnection network which consolidates hundreds even thousands of storage nodes to satisfy both the bandwidth and storage capacity requirements. This paper proposes a resilient interconnection network for storage cluster systems (RISC). The RISC divides storage nodes into multiple partitions to facilitate the data access locality. Multiple spare links between any two storage nodes are employed to offer strong resilience to reduce the impact of the failures of links, switches, and storage nodes. The scalability is guaranteed by plugging in additional switches and storage nodes without reconfiguring the overall system. Another salient feature is that the RISC achieves a dynamic scalability of resilience by expanding the partition size incrementally with additional storage nodes along with associated two network interfaces that expand resilience degree and balance workload proportionally. A metric named resilience coefficient is proposed to measure the interconnection network. A mathematical model and the corresponding case study are employed to illustrate the practicability and efficiency of the RISC.