Storage Architecture Research Articles

Migrating a research data warehouse to a public cloud: challenges and opportunities.

ObjectiveClinical research data warehouses (RDWs) linked to genomic pipelines and open data archives are being created to support innovative, complex data-driven discoveries. The computing and storage needs of these research environments may quickly exceed the capacity of on-premises systems. New RDWs are migrating to cloud platforms for the scalability and flexibility needed to meet these challenges. We describe our experience in migrating a multi-institutional RDW to a public cloud.Materials and MethodsThis study is descriptive. Primary materials included internal and public presentations before and after the transition, analysis documents, and actual billing records. Findings were aggregated into topical categories.ResultsEight categories of migration issues were identified. Unanticipated challenges included legacy system limitations; network, computing, and storage architectures that realize performance and cost benefits in the face of hyper-innovation, complex security reviews and approvals, and limited cloud consulting expertise.DiscussionCloud architectures enable previously unavailable capabilities, but numerous pitfalls can impede realizing the full benefits of a cloud environment. Rapid changes in cloud capabilities can quickly obsolete existing architectures and associated institutional policies. Touchpoints with on-premise networks and systems can add unforeseen complexity. Governance, resource management, and cost oversight are critical to allow rapid innovation while minimizing wasted resources and unnecessary costs.ConclusionsMigrating our RDW to the cloud has enabled capabilities and innovations that would not have been possible with an on-premises environment. Notwithstanding the challenges of managing cloud resources, the resulting RDW capabilities have been highly positive to our institution, research community, and partners.

Secure Integration of IoT and Cloud Computing

Mobility Technique Is a fairly latest invention that relates to an information storage and processing architecture that is separate from the wireless phone. The IoT is a brand-new concept. The Internet of Things (IoT) is a comparatively recent networking technologies that is increasing popularity swiftly. IoT is more particularly linked to wireless telecommunications. The

Wax and bentonite blends for prototyping industrial clay development: preliminary results

Wax and bentonite blends for prototyping industrial clay development: preliminary results

A Secure Data Storage Architecture for Internet of Medical Things (IoMT) Using an Adaptive Gaussian Mutation Based Sine Cosine Optimization Algorithm and Fuzzy-Based Secure Clustering

The fast-growing Internet of Things (IoT) paradigm can be referred to be the interconnection of physical things or human beings along with the digital electronics devices, Softwares to exchange data with centralized systems through the defined communication infrastructures. Moreover, it provides better opportunities for the direct connection between the physical world and computer-based systems. Nevertheless, the security of data transmission through IoMT (Internet of Medical Things) is the biggest challenge. Hence to provide better security we proposed a novel method known as, Fuzzy based Adaptive Gaussian Mutation based Sine Cosine Optimization approach to ensure security and Quality of Service (QoS) by exploiting the Mutual authentication mechanism. Moreover, an adaptive Gaussian mutation-based SCO algorithm is exploited for cluster formulation of IoMT nodes. The Intuitionistic fuzzy based weight estimation is made for the selection of CH and effective routing. Our proposed work is compared to several recent works. From the performance analysis and evaluation, we conclude that the proposed work outperforms all the existing works and provides better QoS and security.

Files Storage & Sharing Platform Using Cloud

Abstract: The concept of cloud computing becomes more popular in latest years. Data storage is very important and valuable research field in cloud computing. Cloud based file sharing is a file sharing security in cloud. The required security from unauthorized access of the file in the cloud is provided by the encryption and decryption function. The admin can provide file access option to the authorized users. This facility limits the number and time of access of the shared files by the admin for the authorized user. Cloud data storage technology is the core area in cloud computing and solves the data storage mode of cloud environment. This project introduces the concept of cloud computing and cloud storage as well as the architecture of cloud storage firstly. Then we analyze the cloud data storage technology amazon web services, wasabi, Digital Ocean etc. We will improve the traditional file storage method and we will make a platform which will get more privileges. Keywords: Cloud, Storage, AWS, Wasabi, File Management, Files Storage, Files Sharing, DMS, CMS, Drive store, Private Cloud.

Distributed Storage Strategy and Visual Analysis for Economic Big Data

With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).

Layered nanoreactor assisted to produce B-doped and P-doped 3D carbon nanostructures for supercapacitor electrodes

In the use of the electric double layer capacitors (EDLCs) with carbon electrode materials, what remains an important technological challenge is that existing carbon electrodes suffer from low energy density and low volumetric capacitance, the today's need for making energy storage architectures. The incorporation of the n-type and/or p-type dopant elements in carbon structure may result in an enhancement in the capacitance. Accordingly, the idea to use the layered nanoreactors was applied to produce phosphorous-doped and boron-doped porous carbon nanostructures with high specific capacitance (CS) values when used in supercapacitor electrodes. So that, the best P-doped and B-doped carbon electrodes show the CS values of 322 and 275 F/g, respectively, at a current density of 1 A/g in the three-electrode system. A symmetric supercapacitor device made using the best doped carbon material (CG-P) showed a CS value of 163.2 F/g at a current density of 0.1 A/g. Herein, this work shows that the high values of CS for carbon materials are due to high accessible surface area, excellent pore size distribution, the presence and uniform dispersion of the dopant agents in carbon structures.

Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques

Network security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumer’s confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes and identification of normal connection nodes. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as intrusion and this type of monitoring system is called as an Intrusion detection system (IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques and HDFS. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage and processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

Design Guidelines for Cooperative UAV-supported Services and Applications

Internet of Things (IoT) systems have advanced greatly in the past few years, especially with the support of Machine Learning (ML) and Artificial Intelligence (AI) solutions. Numerous AI-supported IoT devices are playing a significant role in providing complex and user-specific smart city services. Given the multitude of heterogeneous wireless networks, the plethora of computer and storage architectures and paradigms, and the abundance of mobile and vehicular IoT devices, true smart city experiences are only attainable through a cooperative intelligent and secure IoT framework. This article provides an extensive study on different cooperative systems and envisions a cooperative solution that supports the integration and collaboration among both centralized and distributed systems, in which intelligent AI-supported IoT devices such as smart UAVs provide support in the data collection, processing and service provisioning process. Moreover, secure and collaborative decentralized solutions such as Blockchain are considered in the service provisioning process to enable enhanced privacy and authentication features for IoT applications. As such, user-specific complex services and applications within smart city environments will be delivered and made available in a timely, secure, and efficient manner.

On distributed object storage architecture based on mimic defense

With the advent of the era of big data, cloud computing, Internet of things, and other information industries continue to develop. There is an increasing amount of unstructured data such as pictures, audio, and video on the Internet. And the distributed object storage system has become the mainstream cloud storage solution. With the increasing number of distributed applications, data security in the distributed object storage system has become the focus. For the distributed object storage system, traditional defenses are means that fix discovered system vulnerabilities and backdoors by patching, or means to modify the corresponding structure and upgrade. However, these two kinds of means are hysteretic and hardly deal with unknown security threats. Based on mimic defense theory, this paper constructs the principle framework of the distributed object storage system and introduces the dynamic redundancy and heterogeneous function in the distributed object storage system architecture, which increases the attack cost, and greatly improves the security and availability of data.

GenoVault: a cloud based genomics repository

GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack-based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface, a JavaFX-based desktop client has also been developed to meet the requirements of large file uploads that are usually seen in NGS datasets. Users can store files in their respective object-based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally using OpenStack-based cloud features. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture which is also supported by Secure File Transfer Protocol (SFTP) for data upload and download. The data is uploaded by the user in individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and the output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per the user’s permissions. GenoVault is useful for maintaining the organization-wide NGS data generated in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud.

Extended endurance performance and reduced threshold voltage by doping Si in GeSe-based ovonic threshold switching selectors

Selectors are essential part of the high-density storage architectures to operate the storage device in the crossbar arrays by providing a desired net voltage drop and suppressing undesired current flow. Germanium selenide -based Ovonic Threshold Switching (OTS) selector shows a promising candidate for simple binary composition and high selectivity, however, the high threshold voltage and thermal budget lower than required for Complementary Metal Oxide Semiconductor process are needed to overcome. In this paper, we demonstrate the effect of silicon doping Ge42Se58 thin films on their electrical characteristics of OTS devices and the electronic structure of materials. Si doping allows low threshold voltages ranging from 1.5 to 3 V and improved thermal stability, Si-Ge-Se with Si 13% can cycle steadily more than 5 × 106 cycles. In addition, the optical energy gap of films decreases with increasing Si content. These results, being interpreted as Si atoms modify the bonding type of Ge42Se58 thin film as well as electronic structure includes the energy gap and trap states, suggest an effective method to modulate threshold voltage of the GeSe-based selector by using Si as dopant for various OTS device applications. According to the unified analytical model (Poole-Poole-Frenkel) based on trap-limited conduction, the trap density of Si-Ge-Se thin films in the switching process decreases from 1.29×1017cm−3 to 2.88×1016cm−3with increasing Si content.

A secure and efficient Internet of Things cloud encryption scheme with forensics investigation compatibility based on identity-based encryption

Data security is a challenge for end-users of cloud services as the users have no control over their data once it is transmitted to the cloud. A potentially corrupt cloud service provider can obtain the end-users’ data. Conventional PKI-based solutions are insufficient for large-scale cloud systems, considering efficiency, scalability, and security. In large-scale cloud systems, the key management requirements include scalable encryption, authentication, and non-repudiation services, as well as the ability to share files with different users and data recovery when the user keys of encrypted data are not accessible. Further requirements in cloud systems include the ability to provide the means for digital forensic investigations on encrypted data. Once data on the cloud is encrypted with a user’s key it becomes impossible to access by forensic investigation teams. In this regard, distributing the trust of key management into multiple authorities is desirable. In the literature, there is no available secure cloud storage system with secure and efficient Type-3 pairings, supporting Encryption-as-a-Service (EaaS) and multiple Public Key Generators (PKGs). This paper proposes an efficient Identity-based cryptography (IBC) architecture for secure cloud storage, named Secure Cloud Storage System (SCSS), which supports distributed key management and encryption mechanisms and support for multiple PKGs. During forensic investigations, the legal authorities will be able to use the multiple PKG mechanism for data access, while an account locking mechanism prevents a single authority to access user data due to trust distribution. We also demonstrate that, the IBC scheme used in SCSS has better performance compared to similar schemes in the literature. For the security levels of 128-bits and above, SCSS has better scalability compared to existing schemes, with respect to encryption and decryption operations. Since the decryption operation is frequently needed for forensic analysis, the improved scalability results in a streamlined forensic investigation process on the encrypted data in the cloud.

Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

Tremella-like Mo and N Codoped Graphitic Nanosheets by In Situ Carbonization of Phthalocyanine for Potassium-Ion Battery.

A tremella-like Mo and N codoped graphitic nanosheet array supported on activated carbon (Mo2C-MoC/AC-N) is prepared via in situ carbonization of nitrogen-rich cobalt phthalocyanine nanoparticulates anchored on activated carbon as a high-performance anode for potassium-ion batteries. The nanosheets about 5 nm thick are uniformly distributed on the surface of activated carbon for fast K-ion intercalation, and the abundant micropores in activated carbon provide additional adsorption sites of potassium ions, forming a three-dimensional architecture for potassium storage. The 3.9 atom % Mo in Mo2C-MoC/AC-N is in the form of Mo2C and MoC flakes (around 1:1) attached to the graphitic nanosheets. X-ray diffraction (XRD) analysis revealed that the reaction with Mo2C (forming K2C) happens mainly at 0.8-0.4 V, while the reaction with MoC (forming K2C) occurs primarily at 0.4-0.01 V. The N doping (9.6 atom %) causes an interlayer spacing expansion of 0.3 Å in the graphitic nanosheets, beneficial to the potassium-ion insertion reaction to form KC8 at 0.4-0.01 V. The Mo2C-MoC/AC-N anode exhibits a capacity of 457.5 mA h g-1 at a current density of 0.05 A g-1 and an excellent capacity of 144.4 mA h g-1 at a high current of 5 A g-1 with a capacity loss rate of 0.49‰ per cycle.

Aircraft Hybrid-Electric Propulsion: Development Trends, Challenges and Opportunities

The present work is a survey on aircraft hybrid electric propulsion (HEP) that aims to present state-of-the-art technologies and future tendencies in the following areas: air transport market, hybrid demonstrators, HEP topologies applications, aircraft design, electrical systems for aircraft, energy storage, aircraft internal combustion engines, and management and control strategies. Several changes on aircraft propulsion will occur in the next 30 years, following the aircraft market demand and environmental regulations. Two commercial areas are in evolution, electrical urban air mobility (UAM) and hybrid-electric regional aircraft. The first one is expected to come into service in the next 10 years with small devices. The last one will gradually come into service, starting with small aircraft according to developments in energy storage, fuel cells, aircraft design and hybrid architectures integration. All-electric architecture seems to be more adapted to UAM. Turbo-electric hybrid architecture combined with distributed propulsion and boundary layer ingestion seems to have more success for regional aircraft, attaining environmental goals for 2030 and 2050. Computational models supported by powerful simulation tools will be a key to support research and aircraft HEP design in the coming years. Brazilian research in these challenging areas is in the beginning, and a multidisciplinary collaboration will be critical for success in the next few years.

A Comparative Analysis of Semantic Web Databases Based on Scalability and Performance

Research is rapidly increasing day by day that taken too much efforts in exploring some interesting and some related publications over the internet.as we already know that every data bases have a different architecture that varies the performance in terms of storage architecture and medium. In this research paper we analyzed of two main big data types of Semantic web that iscategorized into two types (i) in memory Native (ii) Non-native Non-memory which are disk reside and Non-native is used for services management for instance, SQL, MySQL, and another is Oracle that is just used for storing purpose. Data bases is very important model specially, when any model come into existence. For instance, when we offer for storing purpose of that data then where it should have o store and then definitely it must be access efficiently. The proposed methodology consist test case for data retrieving and query optimization method to analyze performance of databases. When we talk about access data bases from any source then we query them for accessing. LUMB (Lehigh University Benchmark) is being used for testing performance and it cannot be used for storing data. Semantic Web Data (SWD) give a capability in such a way if anybody want to access / encode related data then it can be retrieved efficiently. Our main objective of research we have compared two types of SWD Native store and Non-nativestore and then we analyzed them

Promiscuous molecules for smarter file operations in DNA-based data storage

DNA holds significant promise as a data storage medium due to its density, longevity, and resource and energy conservation. These advantages arise from the inherent biomolecular structure of DNA which differentiates it from conventional storage media. The unique molecular architecture of DNA storage also prompts important discussions on how data should be organized, accessed, and manipulated and what practical functionalities may be possible. Here we leverage thermodynamic tuning of biomolecular interactions to implement useful data access and organizational features. Specific sets of environmental conditions including distinct DNA concentrations and temperatures were screened for their ability to switchably access either all DNA strands encoding full image files from a GB-sized background database or subsets of those strands encoding low resolution, File Preview, versions. We demonstrate File Preview with four JPEG images and provide an argument for the substantial and practical economic benefit of this generalizable strategy to organize data.

Carbon nanotubes interpenetrating MOFs-derived Co-Ni-S composite spheres with interconnected architecture for high performance hybrid supercapacitor

Recently, carbon nanotubes (CNT)-based interconnected architectures exhibit promising prospects in supercapacitors due to their flexibility and high electrical conductivity. Herein, a three-dimensional (3D) interconnected network structure combined with conductive carbon nanotubes interpenetrating MOFs-derived Co-Ni-S composite spheres (Co-Ni-S/CNTs) was synthesized. Such 3D interconnected architecture significantly leads to a favorable electronic structure, fast charge-transfer capacity, and more pseudocapacitive. The Co-Ni-S/CNTs-based hybrid electrode exhibits an extraordinary specific capacitance of 540.6C g−1 at 1 A g−1 and competitive rate performance (capacity retention rate of 69.9% when the current density increases to 10 times). Subsequently, a hybrid supercapacitor is assembled using Co-Ni-S/CNTs as the positive electrode and commercial activated carbon as negative electrode. The device delivers a high energy density of 63.5 W h kg−1 at 800 W kg−1 and keeps 83.0% initial capacitance retention after 10,000 cycles. The encouraging performances demonstrate the significant contribution of the 3D interconnected architecture for the future energy storage.

Optimal bidding and scheduling of AA-CAES based energy hub considering cascaded consumption of heat

The increasing level of uncertainty caused by high penetration of renewable energy and the widening gap of peak-valley demands call for the deployment of energy storage in power systems. Advanced-adiabatic compressed air energy storage (AA-CAES) is a large-scale physical energy storage technology with the merits of long lifetime, low environmental impact, and no emission. Moreover, AA-CAES works with electricity and heat, making it an excellent choice to realize the framework of energy hub. This paper proposes a cogeneration and storage architecture of an AA-CAES based energy hub in an industrial park. Particularly, the cascaded use of thermal energy to supply heat demands with different temperatures is modeled. A bi-level optimization model is established to study the optimal bidding and scheduling of AA-CAES based energy hub in the day-ahead market. The upper level is the power purchase and self-scheduling of the energy hub, aiming at minimizing the daily operation cost; the lower level represents the market clearing problem which determines the electricity price based on alternating-current optimal power flow. A radial basis function based surrogate optimization method is developed to solve the bi-level model with a nonlinear lower level problem. The problem is decomposed into second-order cone programs and a nonlinear surrogate model which can be solved without much computational effort. Numerical examples verify the effectiveness of the proposed method.