Cloud Storage Infrastructure Research Articles

A QR code-enabled framework for fast biomedical image processing in medical diagnosis using deep learning

In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this conventional storage approach often incurs high infrastructure costs and results in sluggish information retrieval, ultimately leading to delays in diagnosis and consequential wastage of valuable time for patients. The methodology proposed in this paper offers a pioneering solution to expedite the diagnosis of medical conditions while simultaneously reducing infrastructure costs associated with data storage. Through this study, a high-speed biomedical image processing approach is designed to facilitate rapid prognosis and diagnosis. The proposed framework includes Deep learning QR code technique using an optimized database design aimed at alleviating the burden of intensive on-premises database requirements. The work includes medical dataset from Crawford Image and Data Archive and Duke CIVM for evaluating the proposed work suing different performance metrics, The work has also been compared from the previous research further enhancing the system's efficiency. By providing healthcare providers with high-speed access to medical records, this system enables swift retrieval of comprehensive patient details, thereby improving accuracy in diagnosis and supporting informed decision-making.

INTERFACES OF VIRTUAL DATA STORAGE IN THE CONDITIONS OF A FAST-CHANGING INFORMATION ENVIRONMENT

The article analyzes data storage architectures, explores the main approaches to cloud technologies, and highlights the features of virtual data storage architecture formation. In the process of analysis, positive and negative features of individual data storage architectures were highlighted. Cloud information technologies are considered from the point of view of their use for data storage and distribution. When considering cloud data storage, attention is focused on their architectural features from the point of view of efficiency and availability for users. Storage efficiency is an important characteristic of cloud storage infrastructure, especially given its emphasis on overall cost savings. Performance has many aspects, but the main task of a cloud storage system is to move data between the user and the remote cloud service provider. The system architecture was designed based on the proposed and modeled methods of organizing access to the cloud storage. According to the designed architecture, the data flows in the system were analyzed and investigated. The developed algorithm for the collection and integration of disparate data based on virtual data warehouses, in contrast to the Inmon and Kimball models, makes it possible to process NoSQL temporal (InfluxDB), graph (Neo4j) and document-oriented (MongoDB) data with a significantly lower value of capacity complexity and the possibility execution of recursive queries. The developed interface should be able to work with relational, temporal, graph and document-oriented data sources. To do this, it must provide the ability to perform SQL-like queries against relational databases, as well as specialized queries for each type of non-relational data. It allows to increase the efficiency of process0ng both relational and non-relational data, which makes it a powerful tool for analyzing and processing information from various sources. The development of virtual data warehouses may be associated with the introduction of new technologies, such as artificial intelligence, blockchain, quantum computing, etc. In addition, further improvements in metadata management and data security are expected.

Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure

Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.

Strategic integration of technology in precision agriculture

This research focuses on the strategic integration of advanced technologies in microclonal propagation for precision agriculture. The development of an automated system, incorporating computer vision, robotic manipulation, QR code identification, and cloud storage, aims to revolutionize the efficiency and precision of plant sorting processes. Computer vision facilitates early detection of viruses, while a moving platform and robotic arm enhance mobility and accuracy. The QR code system and cloud storage infrastructure enable seamless plant identification and dynamic data analysis. Challenges, including biological variability and technological obsolescence, are addressed through iterative learning and modular design. The research represents a transformative leap in agriculture, offering a promising avenue for sustainable and data-driven cultivation practices to meet the challenges of an evolving global food landscape.

Enhancing privacy‐preserving mechanisms in Cloud storage: A novel conceptual framework

SummaryData privacy is critical for users who want to use Cloud storage services. There is a significant focus on Cloud service providers to address this need. However, in the evolving dynamic cyber‐space, privacy infractions are rising and pose threats to Cloud storage infrastructures. Several studies developed various models and techniques to ensure the privacy of Cloud storage contents. However, these models came with several shortages in the privacy‐preserving attributes they cover. Thus, this article identified a comprehensive set of Cloud data storage privacy‐preserving attributes to propose a flexible and efficient framework to handle the privacy problem. This framework uses a multi‐layer encryption storage structure and a one‐time password authentication technique. The findings of this article intend to help future communities to enhance existing techniques or develop new research‐based practical alternatives. Since Cloud computing is a rapidly growing technology, new privacy vulnerabilities emerge daily. Future research might confirm the findings of this article and test the suggested framework in different contexts.

Determination of Cloud Storage and IT Infrastructure on File Security

This study discusses the determination of cloud storage and IT infrastructure for file security. The purpose of this study is to find out whether the application of cloud storage plays a role in securing files and implementing IT infrastructure plays a role in securing files. The research method used is descriptive qualitative, which aims to obtain hypotheses for further research. The results of this study include: 1) Cloud storage plays a role in securing files, where by implementing and using cloud storage, companies can more easily access cloud computing. So that it does not require disk space on hardware and 2) IT infrastructure plays a role in securing files, where with adequate IT infrastructure such as: network, hardware and software it will make it easier for companies to secure files. Other IT infrastructure that can be done is in the form of developing the speed of the company's internet network, in addition to making it easier for company employees to convey information. High internet speed is also useful in transferring and securing files and backing up files. If one of the company's hardware is attacked by a virus, the data can be quickly transferred with high internet speed. Another infrastructure is the development of its own applications, so that companies can easily control and operate applications according to the wishes and needs of the company. There are other factors that affect file security, including: 1) Employee performance; 2) Employee education; and 3) Competitive advantage.

Secure Distributed Cloud Storage based on the Blockchain Technology and Smart Contracts

Objectives: This paper addresses the problem of secure data storage and sharing over cloud storage infrastructures. A secure, distributed cloud storage structure incorporating the blockchain structure is proposed that supports confidentiality, integrity, and availability. Methods/Analysis: The proposed structure combines two well-known technologies: one of them is the Ethereum Blockchain and its Smart Contracts and the other is the RSA encryption and authentication scheme. The Ethereum Blockchain is used as a data structure, which ensures data availability and integrity while RSA provides sensitive data confidentiality and source authentication. Findings: As a result, users of the proposed structure can trust it and be certain that they can securely exchange information through a publicly accessible and shared cloud storage. The application can be used either through a user interface (UI) or a command-line interface (CLI). Novelty /Improvement:The novelty of this work is that the system that is proposed could be used for secure data storage on the cloud as well as for file sharing and authentication verification. Also, secure data storage and file sharing are already offered by the proposed system. Doi: 10.28991/ESJ-2023-07-02-012 Full Text: PDF

Pindex: Private multi-linked index for encrypted document retrieval.

Cryptographic cloud storage is used to make optimal use of the cloud storage infrastructure to outsource sensitive and mission-critical data. The continuous growth of encrypted data outsourced to cloud storage requires continuous updating. Attacks like file-injection are reported to compromise confidentiality of the user as a consequence of information leakage during update. It is required that dynamic schemes provide forward privacy guarantees. Updates should not leak information to the untrusted server regarding the previously issued queries. Therefore, the challenge is to design an efficient searchable encryption scheme with dynamic updates and forward privacy guarantees. In this paper, a novel private multi-linked dynamic index for encrypted document retrieval namely Pindex is proposed. The multi-linked dynamic index is constructed using probabilistic homomorphic encryption mechanism and secret orthogonal vectors. Full security proofs for correctness and forward privacy in the random oracle model is provided. Experiments on real world Enron dataset demonstrates that our construction is practical and efficient. The security and performance analysis of Pindex shows that the dynamic multi-linked index guarantees forward privacy without significant loss of efficiency.

Computing Frameworks for VM Migration in Cloud

Cloud Service users world wide are increasing with advancement in IT infrastructre. This leads to the demand of online storage for various operational reasons. The applications across various organizations are exploiting the cloud infrastructure offered by different service providers like Microsoft, Amazon, Twitter, IBM, and Hewlett-Packard, enable developers to distribute software through a central organization's hosting machine. Online storage is the next logical phase in the development of IT infrastructure. Cloud Storage Infrastructure has become a norm in distributed environment to host the multiple application on easily available networks. Rather than bothering about the Capital Expenditutre, the organizations are now using funds to invest in operational cost of running the services through Cloud service provider increasing the efficiency. While attempting to migrate the workload needs to be considered as imbalanced workload may result in system failure. Also the VM consolidation need to be done while the sytem is live without any failure. The security concerns for live migration are enlisted in limitations which is the largest concern for an organization whle deciding future course of action. This article reviews the cloud computing systems in the light of migrating the existing legacy systems to the cloud environment and suggest the operational features that an organization should consider before the migration.

IMPLEMENTASI CLOUD STORAGE MENGGUNAKAN OWNCLOUD DAN UBUNTU SERVER STUDI KASUS PADA PT INDONESIA NIPPON SEIKI

The purpose of this study was to produce real work based on the need for cloud storage for ease in the company's business processes which in its benefits are intended for file sharing in the e-mail delivery process and provide security for attached files because infrastructure such as servers and storage media are within the scope of the company. The research method that I use is the prototype
 method, where the author first analyzes the needs and from the results of the analysis the authors design the stages that will be carried out and use UML (Unified Modeling Language) as a software modeling tool developed. The results achieved are that the file sharing facility in cloud storage can be used as a medium for sending large files in the process of sending e-mails by uploading files to be sent into cloud storage to then get the download link to be sent via e-mail to then downloaded by the recipient user, and information security can be better maintained because all infrastructure both from the server and storage media are within the scope of the company, this is done to prevent theft of information by irresponsible parties. The conclusion is that before applying cloud storage, the maximum limit for sending email is only 5 Mb, after cloud storage is implemented at the company, the business process of sending e-mails with large file attachment sizes is not a problem because users can utilize cloud storage facilities, and data security. To be guaranteed because cloud storage infrastructure is within the scope of the company.

Космічна інфраструктура інтернету речей. Стан та перспективи розвитку

We present an overview of possibilities for existing Satellite Communication Systems utilization to provide Internet of Things Services. It is shown that existing Satellite Communication Systems provide traffic transmission for IoT Systems with Cloud Architecture. The proposals are explicated on the possibility of using Fog and Edge Computing for Satellite Communication Systems. The implementation of Fog and Edge computing in IoT Systems requires the modernization of the Low-Earth Orbit (LEO) and Geostationary Orbit (GEO) Satellite Communication Systems, and we show the ways of their enhancement. To increase the efficiency of IoT data processing and the reliability of IoT Data Storage, we propose to construct an Orbital Cloud Data Storage in GEO, which consists of several GEO Satellites - Cloud Computing Data Centers. Such a structure would require the development of methods of access providing to the Orbital Cloud Data Storage. For these purposes, our propositions include using GEO High-Throughput Satellites and satellites from the structure of LEO Satellite Communication. The issues of interaction between Orbital Cloud Data Storage and ground-based Cloud Data Processing and Storage Infrastructure are briefly considered. The orbital slots in GEO are proposed for the location of GEO Satellites - Cloud Computing Data Centers.

HyCloud: Tweaking Hybrid Cloud Storage Services for Cost-Efficient Filesystem Hosting

Today’s cloud storage infrastructures typically provide two distinct types of services for hosting files: object storage like Amazon S3 and filesystem storage like Amazon EFS. In practice, a cloud storage user often desires the advantages of both—efficient filesystem operations with a low unit storage price. An intuitive approach to achieving this goal is to combine the two types of services, e.g., by hosting large files in S3 and small files together with directory structures in EFS. Unfortunately, our benchmark experiments indicate that the clients’ download performance for large files becomes a severe system bottleneck. In this article, we attempt to address the bottleneck with little overhead by carefully tweaking the usages of S3 and EFS. Guided by two key observations, we design and implement an open-source system called HyCloud. It automatically invokes the data APIs of S3 and EFS on behalf of users, and intelligently schedules the data transfer among S3, EFS and the clients in a distributed manner. Real-world evaluations demonstrate that the unit storage price of HyCloud is close to that of S3, and the filesystem operations are executed as quickly as in EFS in most times (sometimes even more quickly than in EFS).

Optimizing Search and Data Analytics of Twitter Data using Elastic Search Algorithms

Quick data acquisition and analysis became an important tool in the contemporary era. Real time data is made available in World Wide Web (WWW) and social media. Especially social media data is rich in opinions of people of all walks of life. Searching and analysing such data provides required business intelligence (BI) for applications of various domains in the real world. The application may be in the area of politics or banking or insurance or healthcare industry. With the emergence of cloud computing, volumes of data are added to cloud storage infrastructure and it is growing exponentially. In this context, Elasticsearch is the distributed search and analytics engine that is very crucial part of Elastic Stack. For data collection, aggregation and enriching it Beats and Logstash are used and such data is stored in Elasticsearch. For interactive exploration and visualization Kibana is used. Elasticsearch helps in indexing of data, searching efficiently and performing data analytics. In this paper, the utility of Elasticsearch is evaluated for optimising search and data analytics of Twitter data. Empirical study is made with the Elasticsearch tool configured for Windows and also using Amazon Elasticsearch and the results are compared with state of art. The experimental results revealed that the Elasticsearch performs better than the existing ones.

Algorithms for optimal replica placement under correlated failure in hierarchical failure domains

In data centers, data replication is the primary method used to ensure availability of customer data. To avoid correlated failure, cloud storage infrastructure providers model hierarchical failure domains using a tree, and avoid placing a large number of data replicas within the same failure domain (i.e. on the same branch of the tree). Typical best practices ensure that replicas are distributed across failure domains, but relatively little is known concerning optimization algorithms for distributing data replicas. Using a hierarchical model, we answer how to distribute replicas across failure domains optimally. We formulate a novel optimization problem for replica placement in data centers. As part of our problem, we formalize and present a new criterion for optimizing a replica placement. Our overall goal is to choose placements in which correlated failures disable as few replicas as possible.In this work, we provide two optimization algorithms for dependency models represented by trees. We first present an O(n+ρlog⁡ρ) time dynamic programming algorithm for optimally placing ρ replicas of a single block on the leaves (representing servers) of a tree with n vertices. We next consider the problem of optimally placing replicas of multiple blocks of data, where every block may have a different replication factor. For this problem, we give a dynamic programming algorithm that runs in O(nρmax3δ2mpoly(δ)), where m denotes the number of blocks, ρmax denotes the maximum replication factor of a block, and δ denotes the maximum difference in the replication factors of any two blocks. The running time of the algorithm is polynomial when the δ, which we refer to as the skew, is a constant.

Comparison of Performance, Availability and Scalability of a Private Cloud Storage Infrastructure Service for Document Preservation

Cloud computing platforms provide easy and transparent access to a company’s storage infrastructure through services. it allows users to store their data at remote disks and access them anytime from any place. On the other hand, with the deployment of Electronic Document Management (EDM) to manage the insertion, sharing, and retrieval of information assigned to a digital document it is necessary that the processes of searching, indexing and storage of digital documents are carried out efficiently. Besides the use of computational tools focused on archiving digital documents, the storage solution must provide reliability and authenticity on the availability of digital documents. With object storage systems being increasingly accepted as replacements for traditional file or block systems and recognized as a preferred way to expose one’s storage infrastructure, it is important to effectively measure the performance of these services. This paper addresses the deployment project of a content-addressable storage infrastructure in a large company and conducts performance, availability and scalability evaluation to measure the gains in r/w access of the proposed solution in different scenarios proposed.

A STUDY ON DATA DEDUPLICATION IN CLOUD COMPUTING

Cloud computing provides scalable, low-cost and location-independent services over the internet. The services provided ranges from simple backup services to cloud storage infrastructures. The fast growth of data volumes has greatly increased the demand for techniques for saving disk space and network bandwidth. Cloud storage services like Dropbox, Mozy, Google Drive choose a deduplication technique where the cloud server stores only a single copy of redundant data and creates links to the copy instead of storing actual copies. The security of users data become a new challenge. Hence the users encrypt the data before outsourcing to the cloud. Conventional encryption techniques are incompatible with deduplication while convergent encryption resolves this problem effectively. Various research papers have been studied from the literature, as a result, this paper attempts to survey data deduplication techniques in cloud storage along with concepts, categories and methods used in data deduplication.

Modeling performances of concurrent big data applications

SummaryBig Data applications are characterized by a non‐negligible number of complex parallel transactions on a huge amount of data that continuously varies, generally increasing over time. Because of the amount of needed resources, the ideal runtime scenario for these applications is based on complex cloud computing and storage infrastructures, providing a scalable degree of parallelism together with isolation between different applications and resource abstraction. However, such additional abstraction degree also introduces significant complexity in performance modeling and decision making. Potential concurrency of many applications on the same cloud infrastructure has to be evaluated, and, simultaneously, scalability of applications over time has to be studied through proper modeling practices, in order to predict the system behavior as the usage patterns evolve and the load increases. For this purpose, in this paper, we propose an analytic modeling technique based on the use of Markovian Agents and Mean Field Analysis that allows the effective description of different concurrent Big Data applications on a same, multi‐site cloud infrastructure, accounting for mutual interactions, in order to support the careful evaluation of several elements in terms of real costs/risks/benefits for correctly dimensioning and allocating the resources and verifying the existing service level agreements. Copyright © 2014 John Wiley & Sons, Ltd.

Design of High Performance Cloud Storage Platform Based on Cheap PC Clusters Using MongoDB and Hadoop

To lay the foundation for the high performance private cloud storage platform, this paper proposes a new cloud storage structure with horizontal scalability using MongoDB and Hadoop. MongoDB is a powerful NOSQL database which is used to construct the cloud storage platform. In certain scenarios, the map-reduce provided by MongoDB can not meet the need of the complex data analysis, especially for the mass complex unstructured data such as videos and documents. This paper introduce the key technologies in MongoDB and Hadoop, then aggregate the advantages of them to build a high performance private cloud storage infrastructure based on cheap personal computer clusters. This infrastructure combines the high horizontal scalability of MongoDB and the high-performance analysis capability from Hadoop.

A File Storage Service on a Cloud Computing Environment for Digital Libraries

This paper introduces a file storage service that is implemented on a private/hybrid cloud computing environment. The entire system was implemented using open source software. The characteristic of elasticity is supported by virtualization technologies allowing to increase and to decrease the computing and storage resources based on their demand. An evaluation of performance and resource consumption was made using several levels of data availability and fault tolerance. The set of modules included in this storage environment can be taken as a reference guide for IT staff that wants to have some experience building a modest cloud storage infrastructure.

PC-Cluster Based Storage System Architecture for Cloud Storage

Design and architecture of cloud storage system plays a vital role in cloud computing infrastructure in order to improve the storage capacity as well as cost effectiveness. Usually cloud storage system provides users to efficient storage space with elasticity feature. One of the challenges of cloud storage system is difficult to balance the providing huge elastic capacity of storage and investment of expensive cost for it. In order to solve this issue in the cloud storage infrastructure, low cost PC cluster based storage server is configured to be activated for large amount of data to provide cloud users. Moreover, one of the contributions of this system is proposed an analytical model using M/M/1 queuing network model, which is modelled on intended architecture to provide better response time, utilization of storage as well as pending time when the system is running. According to the analytical result on experimental testing, the storage can be utilized more than 90% of storage space. In this paper, two parts have been described such as (i) design and architecture of PC cluster based cloud storage system. On this system, related to cloud applications, services configurations are explained in detailed. (ii) Analytical model has been enhanced to be increased the storage utilization on the target architecture.