Storage Management Method Research Articles

Deduplication on Encrypted Data in Cloud Computing

Cloud storage is a crucial component of cloud computing, allowing users to expand their storage without upgrading their equipment and overcome resource constraints. Cloud users' data is always encrypted before being outsourced to ensure their security and privacy. However, encrypted data may result in a significant waste of cloud resources. Storage complicates data sharing among authorized users. We continue to face issues with encrypted data storage and deduplication. Traditional deduplication strategies are designed for certain application settings where data owners or cloud servers have full control over the process. They cannot meet data owners' varying requests based on data sensitivity. This study proposes a flexible data storage management method that combines deduplication and access control across various Clouds. Service Providers (CSP). We assess its performance through security analyses, comparisons, and implementations. The results demonstrate security, effectiveness, and efficiency for actual applications.

Storage management optimization based on electrical consumption and production forecast in a photovoltaic system

Decentralized energy production, particularly from photovoltaic (PV) systems, is becoming increasingly prevalent, leading to a rise in the number of energy producers and consumers, or ”prosumers”. These prosumers, equipped with their own energy generation and storage systems, are not just passive consumers but active participants in the energy market. They generate their own electricity, often from renewable sources, and can feed excess power back into the grid, store it for later use, or share it within a local energy community. This evolving energy paradigm presents new opportunities and challenges in terms of energy management and optimization, necessitating innovative approaches to ensure efficient and sustainable use of energy resources. This paper introduces an innovative storage management method for grid-connected photovoltaic (PV) systems. The method is designed to minimize either the economic or ecological cost, or to find an optimal balance between the two, under various tariff scenarios. This is achieved while adhering to a full self-consumption constraint imposed by the distribution system operator. The control strategy is underpinned by forecasts of electrical consumption, production, and CO2 emissions, which are developed using feedforward neural network models. These models are trained on data from a real-scale smart-grid demonstrator at the Catholic University of Lille, France. The results of the study offer a comparative analysis of the economic and ecological benefits of the three proposed strategies, demonstrating that the best compromise is achieved when considering the off-peak tariff option. Furthermore, a real-time controller was implemented on the Energy Management System (EMS) of the demonstrator and tested over a 24-hour period, yielding satisfactory results. This paper, therefore, presents a significant advancement in the field of storage management for grid-connected PV systems.

Efficient Storage Management for Social Network Events Based on Clustering and Hot/Cold Data Classification

Social network events are related to the national economy and people’s livelihood, so timely perception and processing of massive social network events data are becoming increasingly important for public opinion analysis. How to fully exploit the access feature of event information to store and manage social network events is of great significance for accurate and real-time query analysis. We propose a social network event storage management method based on microblog text clustering and hot/cold data classification. First, for the microblog text data, we construct a keyword provenance graph by using the information entropy to measure the weight of the edge between keyword nodes. Then, we cluster the events using the provenance-based community partition (PCP) with local modularity to improve the event clustering accuracy. In addition, we can further filter noisy data via incremental clustering, enable hot/cold event data classification and dynamic migration, and compress cold data to save space on a hybrid storage architecture. The experimental results show that the clustering purity can reach more than 93% and the query time can be reduced by more than 70% using clustering and hybrid storage policy.

Management of Education and Correction of Problem Teenagers Based on Information Encryption Storage Method

In order to effectively realize the management of educational correction for problem teenagers, improve the security and reliability of encrypted storage of educational correction information for problem teenagers, and shorten the encrypted storage time of educational correction information for problem teenagers, educational correction management for problem teenagers based on information encryption storage method is proposed. Analyze the information encryption storage method and combine it with the necessary theoretical foundation of education and correction of issue teenagers to create an encryption and secure storage management method for education and correction of problem teenagers’ information. The CP-ABE algorithm is used to obtain the initial key of the system, express the encrypted cipher-text, specify the attributes of each user in the problematic youth education and correction user permission list, and obtain the problem youth education and correction user permission control structure. Using tent mapping and cat mapping methods, the key parameters are calculated, respectively, and the encrypted file is piecewise, linearly mapped. Then, XOR is the plaintext bytes and key bytes of the encrypted and safe storage of the problem youth’s education and correction information, completing the encrypted and safe storage of the data information in the problem youth’s education and correction management and realizing the problem youth’s education and correction management. The results of the experiments show that the proposed technology is secure and reliable and that it can efficiently reduce the encryption storage time of educational corrective material for troubled teenagers.

Full Time Domain Storage Method for Power Grid Data

In this paper, a storage definition and management method of information and data holographic complete survival process management for power system operation and management is proposed, including information structure definition and storage and storage management methods. Specifically, it provides a definition and storage of power system data full-time management and storage service management method. It implements information and data management in power system, and realizes power system hologram or defines area and scope by a definition and storage management mechanism suitable for information full-change process storage. The management of the whole time and changing process of information. It can provide data view of time section for different applications of power system.

Метод управління дворівневим сховищем віртуалізованого центру обробки даних

Internet of things applications and data analysis services require a high performance and resilient data storage systems. In the era of servers, networks and storage virtualization, there is a need to develop new models and methods of managing storages for the purpose of improving their performance and reducing the capital and operating costs of the IT infrastructure as a whole. In this paper the two-tier storage model and management method are proposed. The proposed method allows to increase the performance of read/write operations of virtual machines in cloud data centers without significant increase in the storage cost. The research results show that the use of two-tier storage management method allows to reduce the cost of storage by reducing the size and number of storage devices at the fastest level. The analysis of the simulation results shows that the two-tier storage and the proposed management method allows to reduce the waiting time for file access, that leads to an increase in the performance of the two-tier storage while simultaneously serving virtual machine and container requests. Problems in programming 2018; 4: 003-014

GeoSpark SQL: An Effective Framework Enabling Spatial Queries on Spark

In the era of big data, Internet-based geospatial information services such as various LBS apps are deployed everywhere, followed by an increasing number of queries against the massive spatial data. As a result, the traditional relational spatial database (e.g., PostgreSQL with PostGIS and Oracle Spatial) cannot adapt well to the needs of large-scale spatial query processing. Spark is an emerging outstanding distributed computing framework in the Hadoop ecosystem. This paper aims to address the increasingly large-scale spatial query-processing requirement in the era of big data, and proposes an effective framework GeoSpark SQL, which enables spatial queries on Spark. On the one hand, GeoSpark SQL provides a convenient SQL interface; on the other hand, GeoSpark SQL achieves both efficient storage management and high-performance parallel computing through integrating Hive and Spark. In this study, the following key issues are discussed and addressed: (1) storage management methods under the GeoSpark SQL framework, (2) the spatial operator implementation approach in the Spark environment, and (3) spatial query optimization methods under Spark. Experimental evaluation is also performed and the results show that GeoSpark SQL is able to achieve real-time query processing. It should be noted that Spark is not a panacea. It is observed that the traditional spatial database PostGIS/PostgreSQL performs better than GeoSpark SQL in some query scenarios, especially for the spatial queries with high selectivity, such as the point query and the window query. In general, GeoSpark SQL performs better when dealing with compute-intensive spatial queries such as the kNN query and the spatial join query.

A Study and Design on the Data Storage Management Methods in Mobile Environment

A Study and Design on the Data Storage Management Methods in Mobile Environment

Temporal pattern recognition based interactive video-on-demand streaming technique

In this paper, we propose an interactive video-on-demand VoD streaming technique using the temporal pattern recognition for an efficient utilisation of video proxy server. The storage management method of video proxy server is based on probabilistic parameters of a Markov model for selective saving of often-used video data. At the first step, the request orders of video data are sequentially identified and the bi-gram transition probability BTP of video is calculated using request orders. At the second step, the total probability or base probability is calculated using the created state transition model. Finally, the decision probability is calculated using the video data on the input queue when the storage size is insufficient. If the base probability is lower than the decision probability, we select the video data with the lowest conditional probability as a removal object in order to free up storage space of video proxy server.

Research about NAND-Based Adaptive Storage Management Method

Since the physical characteristics and access mode of flash memory both have significant difference with disk, storage management research become the basis and key factor of flash memory database, the performance of storage management often directly impacted on the performance of other subsystems in flash memory database system. Against the characteristics of database data access and problems of existing methods, this paper presented an adaptive flash memory management method which improved performance of data updating in flash memory while taking into account of the better read performance.

모바일 DBMS를 위한 압축 데이터 관리 시스템의 기능 고도화

Recently, mobile computing devices are used popularly. And quantity of information on mobile computing devices is being increased due to digitalization of information. So it needs an embedded DBMS for effective information management. Furthermore, since flash memory having a restriction on the number of partial write cycles is rapidly deployed on mobile computing devices as data storage and is more expensive than the conventional magnetic hard disk, the compressed data management system(CDMS) has been considered as an effective storage management technique for mobile computing devices in previous research. However, the research of CDMS is at the initial stage and has several problems. Hence, in this paper, we present additional storage management methods to solve the problems and improve the effectiveness of the CDMS for embedded DBMS.