Existing Storage Systems Research Articles

Модернизация резервуарного парка региональной дирекции материально-технического снабжения железнодорожного транспорта

Objective: current system of organizing material and technical supply (MTS) in the Russian Railways Holding evaluation, determining the place and role of the regional supply department (RSD) in it, and identifying the most significant aspects of its activities. Methods: methods of comparative analysis of industry methodological documents and regulations for the functioning of both the entire MTS system and its individual departments, methods for calculating coefficients assessing the condition and use of the RSD fuel tank farm, statistical analysis and data processing, were used to achieve the goal of the study. Materials from scientific works on the chosen topic and periodicals were used to carry out the research. Results: highlighted the distinctive features of the MTS system in railway transport, that are expressed in a management and financing system high degree of centralization, in a number of financial and organizational restrictions of the regional material and technical supply department activities, reducing its functions in the procurement and delivery processes to operational control. The state and efficiency of using the fuel resource storage system in the department are evaluated, some proposals are made for its modernization. Practical significance: based on the calculations and comparative analysis of the results obtained, the advantages of modernizing the existing storage system for fuel and energy resources using modular storage technologies in directions with insignificant and changing cargo flows are substantiated. Those proposals can be used to improve the efficiency of the system for providing fuel resources to railway transport organizations.

XkitS：A computational storage framework for high energy physics based on EOS storage system

Large-scale high-energy physics experiments generate scientific data at the scale of petabytes or even exabytes, requiring high-performance data IO for processing. However, in large computing centers, computing and storage devices are typically separated. Large-scale data transfer has become a bottleneck for some data-intensive computing tasks, such as data encoding and decoding, compression, sorting, etc. The time spent on data transfer can account for 50% of the entire computing task. The larger the amount of data accessed, the more significant this cost becomes. One attractive solution to address this problem is to offload a portion of data processing to the storage layer. However, modifying traditional storage systems to support computation offloading is often cumbersome and requires a broad understanding of their internal principles. Therefore, we have designed a flexible software framework called XkitS, which builds a computable storage system by extending the existing storage system EOS. This framework is deployed on the EOS FTS storage server and offloads computational tasks by invoking the computing capabilities (CPU, FPGA, etc.) on FTS. Currently, it has been tested and applied in the data processing of the Large High Altitude Air Shower Observatory (LHAASO), and the results show that the time spent on data decoding using the computable storage technology is half of that using the original method.

Robust energy storage system for stable in wind and solar

Energy storage systems (ESS) have become a conspicuous research hotspot since they store power and supply it during peak hours. Existing storage systems must be replaced by advanced energy storage with improved performance, energy management, and a control interface due to issues with size, dependability, and charging/discharging. The suggested robust energy retention system uses a battery and a super-capacitor to generate power from wind and solar energy. A Multiport DC converter with a buck-boost capacitor is used to properly discharge the stored energy to DC loads via a DC bus. The inverter is then delivered to a Power Flow Controller (PFC) and a Voltage Source Inverter (VSI). The Gravitational Search Algorithm (GSA) method optimizes the power controller settings based on the fluctuation of the system's active and reactive power. The optimization method ensures improved power flow while dealing with the least amount of power variance in imbalanced load situations. Based on the power variation, the proposed method generates appropriate control signals for the inverter system. The covers convert the inverter and reduce harmonics with a controller. Furthermore, the inverter AC supplied to the AC bus is converted into reactive power by an inductive load, and unwanted signals and harmonics are extracted using a Cascaded C-type filter. As a result, the described method has preserved total harmonic distortion, electrical efficiency, and finer transient stability.

Optimal Reference for DNA Synthesis

In recent years, DNA has emerged as a potentially viable storage technology. DNA synthesis, which refers to the task of writing the data into DNA, is perhaps the most costly part of existing storage systems. Consequently, the high cost and low throughput limit the practical use of available DNA synthesis technologies. It has been found that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homopolymer run</i> (i.e., the repetition of the same nucleotide) is a major factor affecting the synthesis and sequencing errors. Recently, [26] raised and studied the coding problem for efficient synthesis for DNA-based storage systems. Among other things, they studied the maximal code size under synthesis constraints. In [29], the authors studied the role of batch optimization in reducing the cost of large-scale DNA synthesis, for a given pool <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> of random quaternary strings of fixed length. This problem is related to the problem posed in [26] which can be viewed as the opposite side of the coin. Instead of seeking the largest code in which every codeword can be synthesized in a certain amount of time, they asked what is the average synthesis time of a randomly chosen string. Following the lead of [29], in this paper, we take a step forward towards the theoretical understanding of DNA synthesis, and study the homopolymer run of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ≥ 1. Specifically, we are given a set of DNA strands <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> , randomly drawn from a Markovian distribution modeling a general homopolymer run length constraint, that we wish to synthesize. For this problem, we derive asymptotically tight high probability lower and upper bounds on the cost of DNA synthesis, for any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ≥ 1. Our bounds imply that, perhaps surprisingly, the periodic sequence ACGT is asymptotically optimal in the sense of achieving the smallest possible cost. Our main technical contribution is the representation of the DNA synthesis process as a certain constrained system, for which string techniques can be applied.

Operational Experience in Degradation (Ageing Effects) and Implementation of Ageing Management Programmes for Dry Storage Systems in Ukraine

The Coordinated Research Projects (CRPs) of International Atomic Energy Agency (IAEA) is a power tool to engage simultaneously a lot of different countries that operate Spent Fuel Storage Facilities for solving important safety issues at a deep level of competence and expertise. One of the topics that needs thorough research is behavior and state assessment of spent fuel assemblies and fuel rods during long/very long term storage in spent fuel storage facilities. It is naturally that over a long period, special attention is drawn to degradation mechanisms, ageing effects and ageing management programs that should be implemented in order to avoid and mitigate the impact of degradation mechanisms on spent fuel elements. This article describes the first step of implementing new IAEA CRP project “T13019. Performance Assessment of Storage Systems for Extended Durations (PASSED)” with contribution from the State Scientific and Technical Center for Nuclear and Radiation Safety (SSTC NRS), which conducts research regarding the “Operational experience on degradation (ageing effects) of dry storage systems” during the first stage” at the first stage. The overall objective of the research proposed by the SSTC NRS is to strengthen technical knowledge, experience and understanding of the long-term behavior of the dry spent fuel storage systems, and of the currently applied inspection and monitoring technologies. At the same time, a specific objective of involving the SSTC NRS in this project was not only to present national experience and practices, but also to study the existing experience of the participating countries, which also joined the CRP proposed by the IAEA. The article considers the existing storage systems for spent nuclear fuel at the Chornobyl and Zaporizhzhya NPP sites, provides examples of the aging management system for components and structures, as well as examples of the issues that require development, implementation and maintenance of aging management programs.

A recipe for an omnichannel warehouse storage system: Improving the storage efficiency by integrating block stacking and racking

Warehouses in multi-channel retailing are expected to possess storage capacity based on the purpose of operations. However, the inefficient use of storage capacity due to omnichannel retail restructuring, wherein mixed-storage items feature both online and offline, has prompted us to revisit the nature of existing storage systems (block stacking and racking). Most warehouses continue to improvise and undertake critical decision-making with regard to profiling balanced storage systems between block stacking and racking, including the efficient slotting of unit-load items across storage systems. This study aimed to establish a framework to systemically improve storage efficiency by achieving an optimum balance between block stacking and racking under inventory uncertainty – lot size and item dimensions. This study ingeniously proposes a two-stage stochastic programming model for robust optimisation during long-run profiling (redesign) and the multi-dimensional knapsack problem for achieving minimum space wastage during slotting (reshuffle). The study adopted a two-fold approach. First, the hypothetical directions for a balanced system, combining increases in buildings’ ceiling heights with its tendency to store low-volume, high-mix items, were examined. Second, the applicability of the two methods – redesign and reshuffle – was examined by considering a real-life case study and the life cycle of warehouse storage design. It was found that the reshuffling approach can resourcefully serve as a short-term solution for warehouses with imminent space issues. Redesigning also demonstrated a long-term space gain in floor space, thus minimising space wastage.

Dense protein data storage based on nanopore sensing.

The exponential growth of digital data worldwide has motivated the researches for new storage media. Among them, protein is a promising alternative for high-density and durable information storage because of the stable chemical properties and diverse chemical components (more than 20 natural amino acids and 400 variants incorporated). However, it is still an urgent need to develop a way to assess the rapid and accurate decoding of protein sequences for the building of protein data storage. Herein, we proposed a strategy for protein data storage based on the single-molecule nanopore sensor, which is already proof-of-concept for protein sequencing. Starting from a model nonapeptide, the alphabet of the peptide has been expanded by site-directed mutation and post-translational modification to achieve a comparable storage density. By the precise identification with a specific nanopore, the peptide sequence data could be decoded to reveal the information in their components. Furthermore, the accuracy and efficiency of data decoding could be improved by the reasonable design of the nanopore to modulate the interactions between nanopore interface and peptides of different sequences. Combined with the array of microwell, we present that the nanopore-based method would promote the development of protein data storage, which would be expected to offer an effective complement to the existing storage systems.

Efficient Crash Consistency for NVMe over PCIe and RDMA

This article presents crash-consistent Non-Volatile Memory Express (ccNVMe), a novel extension of the NVMe that defines how host software communicates with the non-volatile memory (e.g., solid-state drive) across a PCI Express bus and RDMA-capable networks with both crash consistency and performance efficiency. Existing storage systems pay a huge tax on crash consistency, and thus cannot fully exploit the multi-queue parallelism and low latency of the NVMe and RDMA interfaces. ccNVMe alleviates this major bottleneck by coupling the crash consistency to the data dissemination. This new idea allows the storage system to achieve crash consistency by taking the free rides of the data dissemination mechanism of NVMe, using only two lightweight memory-mapped I/Os (MMIOs), unlike traditional systems that use complex update protocol and synchronized block I/Os. ccNVMe introduces a series of techniques including transaction-aware MMIO/doorbell and I/O command coalescing to reduce the PCIe traffic as well as to provide atomicity. We present how to build a high-performance and crash-consistent file system named MQFS atop ccNVMe. We experimentally show that MQFS increases the IOPS of RocksDB by 36% and 28% compared to a state-of-the-art file system and Ext4 without journaling, respectively.

A Google Earth-GIS based approach to examine the potential of the current rainwater harvesting practices to meet water demands in Mityana district, Uganda

Rainwater harvesting (RWH) has become an integral part of global efforts to improve water access. Despite the increasing adoption of RWH in Uganda, there remains a significant knowledge gap in the assessment of RWH systems to meet water demands. In this study, a simplified methodology to estimate rainwater harvesting potential (RWHP) as a function of mean seasonal rainfall and rooftop area, generated using Google Earth and GIS tools is applied. Desired tank storage (DTS) capacities based on user population, demand and dry period lengths, were compared with RWHP to assess whether rooftop areas and tank storage can sustainably supply water for use during the March—May (MAM) and September-November (SON) 90-day dry periods, for three demand levels (i.e. for drinking and cooking (15 litres per capita per day (l/c/d)); for drinking, cooking and hand washing (20 l/c/d); and for drinking, cooking, hand washing, bathing and laundry (50 l/c/d)). Our findings document minimum catchment areas of 60m2 to have rainwater harvesting potential that can sustain households for 90-day dry periods for all three demand levels. However, considering their storage capacities, 25%, 48% and 97% of the existing RWHTs (with storage capacities below 8,000, 10,000 and 20,000 litres respectively) are unable to meet the demand of 15 l/c/d, 20 l/c/d and 50 l/c/d respectively for a 90-day dry period. The results document that the existing storage systems are under-sized for estimated water use under 50 l/c/d demand scenarios. Costs of between 2,000,000–4,500,000 Ugandan shillings (~ 600–1, 250 USD) would be needed to increase existing tank capacities to meet the 50 l/c/d demands for a 90-day dry period. These findings document onerous financial costs to achieve rainwater harvesting potential, meaning that households in Mityana district may have to resort to other sources of water during times of shortage.

LKSM: Light Weight Key-Value Store for Efficient Application Services on Local Distributed Mobile Devices

With the development of mobile network and corresponding techniques, more and more works focus on providing efficient services based on mobile devices. Furthermore, motivated by IoT, studies of local distributed mobile devices attract attentions of both industry and academia in recent years. However, existing storage systems cannot manage data and support the QoS of mobile services well. This paper presents LKSM, a light weight key-value storage system, which can be deployed on either one node or multiple nodes. To the best of our knowledge, it is the first attempt to propose key-value store in this scenario. We carefully analyze the challenges when designing the system on mobile clusters, and further propose RDS for addressing. With the help of RDS, LKSM achieves the goal of lower latency, better scalability, and higher availability. Furthermore, based on RDS, a novel data management strategy is presented, which successfully avoid energy holes of mobile clusters and achieves the tradeoff between performance and energy. We organize LKSM using a log-structured merge-tree and implement it based on LevelDB, an open source key-value storage system proposed by Google. Experiments on physical smartphones demonstrate that LKSM presents much higher performance compared with the ported LevelDB on mobile devices.

Image storage onto synthetic DNA

Living in the age of data explosion, the research of solutions for efficient long term storage of the infrequently used ”cold” data is becoming of great interest. However, even if existing storage systems suggest efficiency in capacity, they are lacking in durability. Hard disks, flash, tape or even optical storage have limited lifespan in the range of 5 to 20 years. Interestingly, recent studies have proven that due to its biological properties, the DNA is a strong candidate for the storage of digital information allowing also data longevity. The DNA’s biological properties allows the storage of a great amount of information into an extraordinary small volume while also promising efficient storage for centuries or even longer with no loss of information. However, the biological procedures of DNA synthesis and sequencing are expensive while also introducing important restrictions in the encoding process. More precisely the encoding of digital data onto DNA is not obvious, because when decoding, we have to face the problem of sequencing noise robustness. This work proposes a coding solution for the storage of digital images onto synthetic DNA. We developed a new encoding algorithm which generates a DNA code robust to biological errors coming from the synthesis and the sequencing processes. Furthermore, we compare this new algorithm to the state of the art encoding techniques analyzing the advantages of using the proposed method.

Neural-assisted image-dependent encryption scheme for medical image cloud storage

Current medical technology evolves massive reports such as electronic patient records and scanned medical images; such reports are needed to be stored securely for future references. Existing storage systems are not feasible for massive data storage. Fortunately, cloud storage services meet the demand through their properties such as scalability and availability. Cloud computing is encouraged by amazing web innovation and modern electronic contraptions. Medical images can be stored in the cloud area, but most of the cloud service providers keep the client data in the plain text format. Cloud users need to take the responsibility to preserve the medical data with their strategy. Most of the existing image encryption solutions are vulnerable to the chosen-plaintext attack because the increasing power of computers and ingenuity of hackers are opening up more and more cracks in this mathematical armour. This paper proposes Hopfield neural network (HNN)-influenced image encryption technique to withstand against various attacks which optimize and improvise system through continuous learning and updating. These methods provide a critical security feature that adapts itself for day-to-day miracles of the real world. In this scheme, the back propagation neural network has been employed to generate image-specific keys that increase the resiliency against hackers. The generated keys are used as an initial seed for confusion and diffusion sequence generation through HNN.

ОБОБЩЕННЫЙ СПОСОБ ПРИМЕНЕНИЯ ХЭШ-ФУНКЦИИ ДЛЯ КОНТРОЛЯ ЦЕЛОСТНОСТИ ДАННЫХ

Data storage systems designed for storing large multidimensional arrays of information, functioning in conditions of destructive influences of an attacker and the environment, are considered. One of the most urgent tasks for such systems is the organization of secure data storage, and taking into account such operating conditions - ensuring their integrity. Ensuring data integrity is a difficult task, due to its complexity, as it includes both recovery and data integrity control. One of the well-known and widely used methods of data integrity control is the use of cryptographic methods, in particular, the hashing function. However, despite the widespread use of hash functions, they have been very little researched, and practical proposals for their use are very few and are characterized by a number of disadvantages associated with the need to introduce a high redundancy of control information. In the context of limitations on the existing resource of data storage systems, this can lead to a decrease in the probability of completing the task of their functioning, or even to its failure. A generalized method is proposed, the use of which will reduce the amount of introduced redundancy when monitoring the integrity of information in data storage systems, based on the use of cryptographic hash functions, a distinctive feature of which is the use of rules for constructing error-resistant codes. In addition, the developed method provides the data integrity control procedure with a new property: the ability to control the integrity of not only the data to be protected, but also the reference hash codes themselves. Calculated data of the required volume of the introduced redundancy are obtained when monitoring the data integrity in existing storage systems using the developed method.

A Hive-Based Retrieval Optimization Scheme for Long-Term Storage of Massive Call Detail Records

With the dramatic rise of mobile internet users and the administrative requirements of long-term data retention, telecom providers are facing increasingly challenging storage and retrieval issues of call detail records (CDRs). The existing storage system can only achieve the requirement of online query and offline analysis of the CDRs. However, to the best of our knowledge, few studies have focused on the topic of CDRs retrieval optimization with long-term storage. In order to improve the retrieval speed while ensuring a high compression ratio, in this paper we propose a novel hash storage scheme, termed dual-column bucketing (DCB), based on the Hive platform by making use of its Bucketing nature. Compared to the conventional scheme, the proposed DCB scheme can improve the performance both for CDRs compression and query. Second, similar storage scenarios such as storage of SMS, email and extended detail records (XDRs) are included in the optimization scope of the DCB. Experiments on real-world CDRs show that in contrast to the conventional scheme, the proposed DCB scheme can save the storage space by approximately 40%, reduces the amount of disk read to 2%, and improve the retrieval speed of known phone number queries by up to seven times.

Using Hadoop Distributed and Deduplicated File System (HD2FS) in Astronomy

AbstractDuring the last years, the amount of data has skyrocketed. As a consequence, the data has become more expensive to store than to generate. The storage needs for astronomical data are also following this trend. Storage systems in Astronomy contain redundant copies of data such as identical files or within sub-file regions. We propose the use of the Hadoop Distributed and Deduplicated File System (HD2FS) in Astronomy. HD2FS is a deduplication storage system that was created to improve data storage capacity and efficiency in distributed file systems without compromising Input/Output performance. HD2FS can be developed by modifying existing storage system environments such as the Hadoop Distributed File System. By taking advantage of deduplication technology, we can better manage the underlying redundancy of data in astronomy and reduce the space needed to store these files in the file systems, thus allowing for more capacity per volume.

Lightweight Indexing and Querying Services for Big Spatial Data

With the widespread use of GPS-equipped smartphones and Internet of Things devices, a huge amount of data with location information is being generated at an unprecedented rate. To gain a deeper insight into such a plethora of spatial data, scientists and engineers are widely using spatial queries for their big data applications. However, because of not only the massive spatial data size but also the complexity of spatial query processing, they are struggling to efficiently process the spatial queries. In this paper, we propose lightweight and scalable indexing and querying services for big spatial data stored in distributed storage systems or graph-based systems. Our spatial services have several advantages over existing approaches. First, our services can be easily applied to existing storage systems or graph-based models without modifying the internal implementation of existing systems/models. Second, our services achieve high pruning power by efficiently selecting only relevant spatial objects based on a simple yet effective filter. Third, our services support a customizable and easy-to-use control of index data size by adjusting the precision of indexed geometries. Lastly, our services support efficient updates of spatial data. Our experimental results using real-world datasets validate the effectiveness and efficiency of our spatial services.

A New Adaptive Coding Selection Method for Distributed Storage Systems

Erasure codes, such as Reed–Solomon (RS) codes and local reconstruction codes (LRCs), are being increasingly adopted in distributed storage systems since they offer lower redundancy than data replication. While these codes significantly save storage space, they can incur large I/O overhead and network traffic in reconstructing unavailable data. Most existing storage systems use replication for hot data and an erasure code for warm and cold data, thereby achieving a good tradeoff between storage overhead and recovery performance. However, these storage systems do not take the access characteristics of data into account and tend to use only an erasure code, which hinders the possibility of reducing storage overhead and recovery cost. In this paper, we propose a new adaptive coding selection method that instead uses multiple LRCs for warm data. The LRCs are selected based on the access characteristics of the data. Each time a file is accessed, we assume that each of the involved data blocks is unavailable, in turn. It is necessary to calculate the I/O cost to recover unavailable blocks for different LRCs. The sum of the I/O costs for each LRC is calculated, and the LRC with the minimal I/O cost is selected for warm data. For cold data, we use an RS code that is optimized for storage overhead to reduce the storage burden. Our method is implemented on the top of the Hadoop distributed file system. Evaluations show that it reduces the storage overhead by up to 5% and the reconstruction traffic by up to 22%.

ENT COBRA ONTOLOGY: the covariates classification system proposed by the Head & Neck and Skin GEC-ESTRO Working Group for interdisciplinary standardized data collection in head and neck patient cohorts treated with interventional radiotherapy (brachytherapy).

PurposeClinical data collecting is expensive in terms of time and human resources. Data can be collected in different ways; therefore, performing multicentric research based on previously stored data is often difficult. The primary objective of the ENT COBRA (COnsortium for BRachytherapy data Analysis) ontology is to define a specific terminological system to standardized data collection for head and neck (H&N) cancer patients treated with interventional radiotherapy.Material and methodsENT-COBRA is a consortium for standardized data collection for H&N patients treated with interventional radiotherapy. It is linked to H&N and Skin GEC-ESTRO Working Group and includes 11 centers from 6 countries. Its ontology was firstly defined by a multicentric working group, then evaluated by the consortium followed by a multi-professional technical commission involving a mathematician, an engineer, a physician with experience in data storage, a programmer, and a software expert.ResultsTwo hundred and forty variables were defined on 13 input forms. There are 3 levels, each offering a specific type of analysis: 1. Registry level (epidemiology analysis); 2. Procedures level (standard oncology analysis); 3. Research level (radiomics analysis). The ontology was approved by the consortium and technical commission; an ad-hoc software architecture (“broker”) remaps the data present in already existing storage systems of the various centers according to the shared terminology system. The first data sharing was successfully performed using COBRA software and the ENT COBRA Ontology, automatically collecting data directly from 3 different hospital databases (Lübeck, Navarra, and Rome) in November 2017.ConclusionsThe COBRA Ontology is a good response to the multi-dimensional criticalities of data collection, retrieval, and usability. It allows to create a software for large multicentric databases with implementation of specific remapping functions wherever necessary. This approach is well-received by all involved parties, primarily because it does not change a single center’s storing technologies, procedures, and habits.

Two-stage collaborative global optimization design model of the CHPG microgrid

With the continuous developing of technology and reducing of investment costs, renewable energy proportion in the power grid is becoming higher and higher because of the clean and environmental characteristics, which may need more larger-capacity energy storage devices, increasing the cost. A two-stage collaborative global optimization design model of the combined-heat-power-and-gas (abbreviated as CHPG) microgrid is proposed in this paper, to minimize the cost by using virtual storage without extending the existing storage system. P2G technology is used as virtual multi-energy storage in CHPG, which can coordinate the operation of electric energy network and natural gas network at the same time. Demand response is also one kind of good virtual storage, including economic guide for the DGs and heat pumps in demand side and priority scheduling of controllable loads. Two kinds of storage will coordinate to smooth the high-frequency fluctuations and low-frequency fluctuations of renewable energy respectively, and achieve a lower-cost operation scheme simultaneously. Finally, the feasibility and superiority of proposed design model is proved in a simulation of a CHPG microgrid.

An Efficient Storage System for Adult Odonata Specimens, With Application for Other Museum Collections

Abstract A new system of storing adult Odonata (damselfly and dragonfly) specimens is described and compared to existing storage systems. The major design innovation is the use of tongue and groove (“zipper lock”) resealable polyethylene envelopes manufactured to fit the standard index card and specimen arrangement currently used in major collections. Other design improvements include low-cost, adhesive-free specimen trays and glass-top drawers built to fit in standard-dimension Cornell insect cabinets. Comparisons of materials and designs with other available systems are presented and discussed. Finally, examples are presented of this new system's applicability to other collections such as Lepidoptera and Archeology.