Effective Metadata Cluster Management for Mass Data Storage

The operations of the metadata account for more than half of the load in the object-based storage system, while the replication strategy can improve the performance. Therefore, an elastic replication strategy is proposed in this paper, which is based on the communication model consisting of the physical nodes and logical elements in the object-based storage system. The replication environment is formalized and the hot degree of the metadata is defined. Afterwards, based on the capability of the metadata server (MDS), the metadata is adaptively replicated across the MDS cluster according to the access frequency of the corresponding data object, including the numbers of the replications and their storage locations. Through analysis and evaluation, it is proved that the replication strategy can improve the service efficiency of the metadata in the object-based storage system, which can further improve the performance of the system.

The failure of a single unreliable commodity component is very common in large-scale distributed storage systems. In order to ensure the reliability of data in large-scale distributed storage systems, many studies have emerged one after another. Among them, Erasure Codes are widely used in actual storage systems, such as Hadoop Distributed File System (HDFS), which provides high fault-tolerance with lower storage overhead. However, usually the recovery of erasure-coded storage system when encountering node failure results in severe cross-node and cross-rack bandwidth consumption, which affects the efficiency of failure recovery and wastes additional resources. In this paper, we improve the erasure coding storage strategy in distributed storage systems, and propose a low-overhead data recovery method based on cross-checking, namely HV-SNSP. In HV-SNSP, horizontal and vertical cross parity checking is realized by adding RS parity inside the data node, that is, <inline-formula> <tex-math notation="LaTeX">$\text{H}^{\mathrm {RS(n, k)}}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\text{V}^{\mathrm {RS(n', \textrm {}k')}}$ </tex-math></inline-formula> storage architecture. Based on <inline-formula> <tex-math notation="LaTeX">$\text{H}^{\mathrm {RS(n, \textrm {}k)}}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\text{V}^{\mathrm {RS(n', k')}}$ </tex-math></inline-formula>, a low-cost supply node selection strategy, namely SNSP, is designed, and nodes with shorter network distance and lower load are selected to participate in recovery. This strategy can effectively reduce the amount of data transmission, shorten the recovery time, and improve the recovery efficiency. The experimental results show that compared with traditional RS, HV-SNSP can reduce the amount of cross-rack data transmission by 62.5&#x0025; during data recovery, and can shorten the recovery time by up to 42.41&#x0025;; Compared with D3, HV-SNSP can reduce the occupation of cross-rack bandwidth by 25&#x0025; and shorten the recovery time by 36.58&#x0025;.

In this paper, we consider two rack-aware storage systems. First, large-scale rack-aware storage system, which is very common in large-scale storage system, is a rack-aware storage system where all sizes of racks are at least the number of redundant nodes. For such storage system, we prove that any Maximum Distance Separable (MDS) codes can have optimal inter-rack repair bandwidth and give a closed-form representation of all repair schemes with optimal inter-rack repair bandwidth. Furthermore, we show that the optimal repair access and optimal inter-rack repair bandwidth can be attained simultaneously for such storage system. Second, we investigate the rack-aware storage system of all racks with the same size, which is called uniform rack-aware storage system. We prove that, except the trivial cases, we cannot attain optimal inter-rack repair bandwidth and optimal repair access for such storage system at the same time. Specifically, we establish the lower bound of repair access for a repair scheme with optimal interrack repair bandwidth, which is tight for some parameters, and also the tight lower bound of inter-rack repair bandwidth for a repair scheme with optimal repair access.

High-performance computing (HPC) storage systems provide data availability and reliability using various hardware and software fault tolerance techniques. Usually, reliability and availability are calculated at the subsystem or component level using limited metrics such as, mean time to failure (MTTF) or mean time to data loss (MTTDL). This often means settling on simple and disconnected failure models (such as exponential failure rate) to achieve tractable and close-formed solutions. However, such models have been shown to be insufficient in assessing end-to-end storage system reliability and availability. We propose a generic simulation framework aimed at analyzing the reliability and availability of storage systems at scale, and investigating what-if scenarios. The framework is designed for an end-to-end storage system, accommodating the various components and subsystems, their interconnections, failure patterns and propagation, and performs dependency analysis to capture a wide-range of failure cases. We evaluate the framework against a large-scale storage system that is in production and analyze its failure projections toward and beyond the end of lifecycle. We also examine the potential operational impact by studying how different types of components affect the overall system reliability and availability, and present the preliminary results

The need for reliable and fast storage systems is increasingly critical in various fields including artificial intelligence and data analytics. This paper proposes a new architecture for large-scale data storage systems, focusing on comparing performance of software and hardware storage technologies that effectively reduce the computational latency and improve performance. The main contributions include: (1) the combination of Singled Magnetic Recording (SMR) for storing data and Solid State Devices (SSD) for storing metadata is a viable solution for implementing large data storage systems; and (2) the combination of Conventional Magnetic Recording (CMR) for storing data and SSD for storing metadata shows the highest performance for high performance computing. Our experiments are carried out in multiple settings, demonstrating that the proposed architecture successfully improves performance for sequential and random reads/writes. The prototypes are evaluated with a set of workloads, showing the superiority of the proposed data storage configurations. This work provides new opportunities for efficiently processing and storing data and metadata in large-scale data analysis systems.

We started a feasibility study of high-end computing systems as a 2-year national project in 2012 for exa-scale computing era. In order to realize the exa-scale system, it is extremely important to design a large scale storage system with high bandwidth.We made a conceptual design of a mass storage system with high-speed I/O technology by studying future I/O requirements from simulations for disaster prevention and mitigation research. We concluded that a distributed and parallel file system with multiple meta-data servers is required, because a meta-data server (MDS) using in the Lustre file system is apparently insufficient to handle a large scale file system. In this paper, the storage system architecture and its performance are presented.

Hydrogen produced by electrolysis is one of the promising means by which to store electricity coming from renewables and to address its intermittency. With the growing global electricity share from renewable energy sources, large-scale storage systems are required. Alkaline electrolysis can be a sustainable solution for the large-scale energy storage system. However, high overpotential and slow kinetics particularly of the oxygen evolution reaction (OER) still remains as a hurdle to limit efficiency of the system. To enhance efficiency of alkaline electrolysis cells, catalysts which have high electrocatalytic activity for OER should be developed. Platinum-like behavior of Tungsten carbide in surface catalysis was first reported by Levy and Boudart in 1973[1]. In addition, tungsten carbide was reported to have Pt-like electronic structure by Colton in 1975[2]. After that, tungsten carbide has been studied widely and expected to substitute noble metal catalysts which are used in many electrochemical reactions. Here, we studied tungsten carbide as a support for 3d-transition metals and make bi-metallic surfaces for developing a high activity OER catalyst. First, WC-Co was synthesized by heat treatment. Carbon nitride (g-C3N4) and tungsten chloride were used as a carbide precursor and cobalt acetate was also used for helping carbonization of tungsten. After heat treatment, acidic leaching process was followed to eliminate cobalt. Through additional mixing with Nickel precursor and heat treatment, Ni/WC was synthesized and to compare electrocatalytic activity Ni/C was also prepared. In addition, many different Ni/WC which have different Ni to WC ratios were synthesized and measured in electrochemical way. Finally, the optimal ratio was determined and it show better electrocatalytic activity than precious catalysts and any other non-noble catalysts. Cyclic voltammetry was measured to evaluate OER activity in 1 M KOH in potential range from 1 to 1.8 V. Figure 1 shows that pure WC and Ni/C have 394 mV and 413 mV overpotentials at 10 mA∙cm-2 respectively. In Metal/WC cases, Cobalt/WC shows only 417 mV. But Nickel/WC shows 340 mV overpotential at 10 mA∙cm-2 and higher electrocatalytic activity for OER than RuO2 in alkaline media. η @ 10 mA cm-2 (mV) Ni/C 394 WC 413 Co/WC 417 Ni/WC 340 RuO2 355 Table1. Activities for OER at 10 mA cm-2 References [1] R. B. Levy, M. Boudart, Science, New series, Vol. 181, No. 4099 (1973) 547-579 [2] Richard J. Colton, Jan-Tsyu j. Huang, J.Wayne Rabalais, Chemical Physics Letters, Vol. 34, No. 2 (1975) 337-339 Figure1. Cyclic voltammetry for the oxygen evolution reaction in 1 M KOH of Ni/C, WC, Co/WC, Ni/WC, RuO2 Figure 1

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Flash-based Solid State Drive (SSD) has been productively shipped and deployed in large scale storage systems. However, a single flash-based SSD cannot satisfy the capacity, performance and reliability requirements of the modern storage systems that support increasingly demanding data-intensive computing applications. Applying RAID schemes to SSDs to meet these requirements, while a logical and viable solution, faces many challenges. In this article, we propose a Hybrid Parity-based Disk Array architecture (short for HPDA), which combines a group of SSDs and two hard disk drives (HDDs) to improve the performance and reliability of SSD-based storage systems. In HPDA, the SSDs (data disks) and part of one HDD (parity disk) compose a RAID4 disk array. Meanwhile, a second HDD and the free space of the parity disk are mirrored to form a RAID1-style write buffer that temporarily absorbs the small write requests and acts as a surrogate set during recovery when a disk fails. The write data is reclaimed to the data disks during the lightly loaded or idle periods of the system. Reliability analysis shows that the reliability of HPDA, in terms of MTTDL (Mean Time To Data Loss), is better than that of either pure HDD-based or SSD-based disk array. Our prototype implementation of HPDA and the performance evaluations show that HPDA significantly outperforms either HDD-based or SSD-based disk array.

Distributed storage systems provide large-scale data storage services, yet they are confronted with frequent node failures. To ensure data availability, a storage system often introduces data redundancy via replication or erasure coding. As erasure coding incurs significantly less redundancy overhead than replication under the same fault tolerance, it has been increasingly adopted in large-scale storage systems. In erasure-coded storage systems, degraded reads to temporarily unavailable data are very common, and hence boosting the performance of degraded reads becomes important. One challenge is that storage nodes tend to be heterogeneous with different storage capacities and I/O bandwidths. To this end, we propose FastDR, a system that addresses node heterogeneity and exploits I/O parallelism, so as to boost the performance of degraded reads to temporarily unavailable data. FastDR incorporates a greedy algorithm that seeks to reduce the data transfer cost of reading surviving data for degraded reads, while allowing the search of the efficient degraded read solution to be completed in a timely manner. We implement a FastDR prototype, and conduct extensive evaluation through simulation studies as well as testbed experiments on a Hadoop cluster with 10 storage nodes. We demonstrate that our FastDR achieves efficient degraded reads compared to existing approaches.

This section presents a scalable and adaptive decentralized metadata lookup scheme for ultra large-scale file systems (more than Petabytes or even Exabytes). Our scheme logically organizes metadata servers (MDSs) into a multilayered query hierarchy and exploits grouped Bloom filters to efficiently route metadata requests to desired MDSs through the hierarchy. This metadata lookup scheme can be executed at the network or memory speed, without being bounded by the performance of slow disks. An effective workload balance method is also developed for server reconfigurations. This scheme is evaluated through extensive trace-driven simulations and a prototype implementation in Linux. Experimental results show that this scheme can significantly improve metadata management scalability and query efficiency in ultra large-scale storage systems (©{2011}IEEE. Reprinted, with permission, from Ref. [1].).

Power-saving has become a central issue for well-configured SOC platforms. In particular, as a high percentage of the total energy is used by the storage systems, the cost effectiveness of data management is equally as important as reliability and availability. To address this issue, we propose the dynamic grid quorum as a method for reducing the power consumption of large-scale distributed storage systems. The basic principle of our approach is to skew the workload toward a small number of quorums. This can be realized using the following three techniques. First, our system allows reconfiguration by exchanging nodes without any data migration, so that high-capacity nodes can be reallocated to busier quorums. Second, for more effective skewing of the workload, we introduce the notion of dual allocation, which makes it possible to consider two distinct allocations in the same grid for write and read quorums. Finally, we present an optimization algorithm to find a pair of a strategy and an allocation of nodes, which minimizes power for a given system setting and its workload. We also demonstrate that the dynamic grid quorum saves, on average, 14---25% energy compared with static configurations, when the intensity of the total workload changes.

Due to consistent improvements in memory and processor technology, object storage devices (OSDs) have greater memory space and more powerful processing power, which allow the OSDs to execute user-defined programs. Shifting part of an application's processing to the disk drives drops the amount of data transferred across the network and explores the parallelism of large-scale distributed storage systems, reducing the execution time for many basic data analytics tasks. In this paper, we propose a large-scale object-based active storage platform, named Gem, for data analytics in the internet of things (IoT). All data from the IoT that resides in disk drives form objects with attributes, methods and policies. For some applications such as data analytics, application-specific operations are executed by the drive processors. In this way, only the results are returned to clients, rather than data files being read by the clients. Therefore, the platform Gem is able to greatly reduce the overhead of data analytics applications in the Internet of Things. By conducting performance evaluation, experimental results demonstrate the effectiveness and scalability of Gem.

At present, global climate change is intensifying and climate extreme events are occurring frequently, so the new energy system represented by the combined wind and storage system will play an important role in the future decarbonization process. Based on the life cycle analysis method, this study constructs a carbon footprint accounting model for large-scale combined wind and storage systems. The results show that the emission hotspot of the onshore wind power system is the component manufacturing stage, and the emission hotspot of the energy storage system is the operation and maintenance stage. This study provides a basis for the accurate calculation of the carbon emissions of the combined wind and storage system. It provides technical support for realizing the goals of “carbon peaking” and “carbon neutrality”.

The development of stationary battery storage systems in Germany – status 2020