TDOPS: Time series based deduplication and optimal data placement strategy for IIoT in cloud environment

Optimal bidirectional amplifier placement method for stable radio frequency reference dissemination

Storage of multimedia data is a critical issue for the overall system's performance and functionality. Multimedia applications must be able to support a variety of data defined as multimedia objects. Multimedia data consist of various objects such as text, image, video and graphics, which need to be synchronized and meet certain timing requirements. The development and evolution of these new applications characterized by high storage needs resulted in strengthening the role and importance of Tertiary Storage Systems. Recent technological advances have resulted in wide availability of commercial products offering near-line, robot-based, tertiary storage libraries. Therefore, such libraries have become an important component of modern large-scale storage servers. The issue of optimal data placement strategies in tertiary storage is considered below. Different configurations of tape libraries and magnetic tape technologies as well as the special attributes of stored data have an impact on the optimal placement.

Data redundancy consumes huge storage space while setting up or employing cloud and fog storage. The dynamic cloud nature primarily focuses on the static environments which must be revised. Data deduplication solutions help minimize and control this issue by eradicating duplicate data from cloud storage systems. Since it might improve storage economy and security, data deduplication (DD) over encrypted data is a crucial problem in computing and storage systems. In this research, a novel approach to building secure deduplication systems across cloud and fog environments is developed. It uses MCDD and convergent cryptographic algorithms. The two most significant objectives of such systems are the focus of the suggested approach. Data redundancy must be minimized, but it also needs to be secured using a robust encryption method, which needs to be devised. The suggested approach is ideally suited for tasks like a user uploading new data to cloud storage or the fog. The proposed method might eliminate data redundancy by detecting redundancy at the block level. The testing results indicate that the recommended methodology can surpass a few cutting-edge techniques regarding computing effectiveness and security levels. The file is encrypted twice, once with the modified cryptographic model for deduplication (MCDD) and once with convergence encryption (CE).

Nowadays, modern data centers are seeking for importing renewable energy together with conventional energy in order to be more environment-friendly and to reduce operation expenditures. Meanwhile, considering the fact that electricity prices and renewable energy generations are diverse in time and geography, a task scheduling strategy should be designed to ensure the efficient and economic operations of data centers. In this paper, an optimal task placement strategy is presented for geo-distributed data centers powered by mixed renewable and conventional energies with dynamic voltage and frequency scaling technique. We aim at minimizing the total electricity cost and making full use of the renewable energy so as to construct green and economic data centers. The optimal task placement problem is formulated as a mixed integer nonlinear problem (MINLP), in which the quality-of-service constraint is restricted by an M/G/1 queuing model. To tackle the complexity of the MINLP, we first transform it into a tractable form, and then develop an optimal sever activation configuration and task placement algorithm to solve it. The proposed algorithm can obtain the global optimal solution of the electricity minimization problem and meanwhile dramatically reduce the complexity of the problem solving. Finally, evaluations based on real-world traces exhibit impacts of different system parameters on the electricity cost and sever activation configurations, which prove the superiority of our proposed algorithm and provide us some illuminations on how to build cost-effective and eco-friendly data centers.

In this data era, massive volumes of data are being generated every second in variety of domains such as Geoscience, Social Web, Finance, e-Commerce, Health Care, Climate modelling, Physics, Astronomy, Government sectors etc. Hadoop has been well-recognized as de factobig data processing platform that have been extensively adopted, and is currently widely used, in many application domains processing Big Data. Even though it is considered as an efficient solution for such complex query processing, it has its own limitation when the data to be processed exhibit interest locality. The data required for any query execution follows grouping behavior wherein only a part of the Big-Data is accessed frequently. During such scenarion, the time taken to execute a queryand return results, increases exponentially as the amount of data increases leading to much waiting time for the user. Since Hadoop default data placement strategy (HDDPS) does not consider such grouping behavior, it does not perform efficiently resulting in lacunas such as decreased local map task execution, increased query execution time etc. Hence proposed an Optimal Data Placement Strategy (ODPS) based on grouping semantics. In this paper we experiment the significance oftwo most promising clustering techniques viz. Hierarchical Agglomerative Clustering (HAC) and Markov Clustering (MCL) in grouping aware data placement for data intensive applications having interest locality. Initially user access pattern is identified by dynamically analyzing history log.Then both clustering techniques (HAC & MCL) are separately applied over the access pattern to obtain independent clusters. These clusters are interpreted and validated to extract the Optimal Data Groupings (ODG). Finally proposed strategy reorganizes the default data layouts in HDFSbased on ODG to achieve maximum parallel execution per group subjective to Load Balancer and Rack Awareness. Our proposed strategy is tested in 10 node cluster placed in a multi rack with Hadoop installed in every node deployed in cloud platform. Proposed strategy reduces the query execution time, significantly improves the data locality and has proved to be more efficient for massive datasets processing in heterogeneous distributed environment. Also MCL shows a marginal improved performance over HAC for queries exhibiting interest localities.

Optimal vibration control of smart fiber reinforced composite shell structures using improved genetic algorithm

Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.

Over the past few decades, advances in energy storage technology, particularly in the form of Battery Energy Storage Systems (BESS), have provided innovative solutions to address various challenges in the power grid such as voltage fluctuations and high levels of losses, which negatively impact the efficiency and quality of electricity provision. BESS has advantages over other energy storage technologies such as having lower costs, faster response times to power equipment or devices, and increased efficiency and flexibility. The purpose of this research is to determine the optimal capacity and location of the placement of BESS to get an improvement in the voltage profile and losses in the Central Java Province power system. In this study, BESS is incorporated into the Jelok substation based on the calculation method under day and night conditions, which will be sought for the most optimal placement. After getting the most optimal placement, the optimal BESS capacity based on the calculation method, 15 MWh, and 25 MWh will be compared. The effect of optimum BESS placement and sizing of up to 0.0035 pu, and reduce losses up to 1.87 MW.

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

The optimal placement method of sensors is carried out based on improved genetic algorithm for solving the sensors optimal placement problem of the health monitoring system for long-span railway bridge. Dual structure coding method is introduced to improve the individual encoding method in the Genetic Algorithm. Adaptive partial matching crossover and inversus mutation method is adopted in the optimal preservation strategy, and the probabilities of crossover and mutation are changed automatically according to the fitness value for obtaining the global optimal solution of the sensor placement. So some defects in other Genetic Algorithm applied in the optimal placement of sensors for major bridge structure, such as slow convergence speed and easily falling into local optimum etc., are overcome, and the convergence is ensured. Then the optimal sensors placement of the health monitoring system for one certain long-span railway steel truss cable-stayed bridge is taken as the example to verify the proposed improved genetic algorithm. The result shows that the proposed method has better global optimization, computational efficiency and reliability in compare with the Simple Genetic Algorithm and General Genetic Algorithm, and can be applied to the actual railway cable-stayed bridge health monitoring system for the optimal sensors placement.

Cloud computing has empowered the individual user by providing seemingly unlimited storage space and availability and accessibility of data anytime and anywhere. Cloud service providers are able to maximize data storage space by incorporating data deduplication into cloud storage. Although data deduplication removes data redundancy and data replication, it also introduces major data privacy and security issues for the user. In this paper, a new privacy-preserving framework that addresses this issue is proposed. Our framework uses an efficient deduplication algorithm to divide a given file into smaller units. These units are then encrypted by the user using the combination of a secure hash function and a block encryption algorithm. An index tree of hash values of these units is also generated and encrypted using an asymmetric search encryption scheme by the user. This index tree will enable the cloud service provider to search through the index and return the requested units. We will show that our proposed framework will allow cloud service and storage providers to employ data deduplication techniques without giving them access to either the users' plaintexts or the users' decryption keys.

Cloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality. Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems. However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others. Such issues need to be carefully studied and solved to enable the wide deployment and adoption of Cloud computing in businesses, industry, etc. The purpose of this special issue is to publish recent advances in Cloud-based collaborative systems and Cloud storage. The special issue comprises five high-quality papers, which are arranged as follows. De Maio et al in the first paper1 “Social Media Marketing through Time-Aware Collaborative Filtering,” define a time-aware collaborative filtering for estimating users' interest along the time in Twitter. Their approach uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time. A model-based approach implementing K-Nearest Neighbors is used to estimate user's similarity representing their profile by sampling user's interest with three different techniques: Vectorial Representation, Symbolic Aggregation Approximation, and Median. The authors present experimental results comparing these techniques and performing model training in different time windows. The second paper2 “SERNOTATE: An Automated Approach for Business Service Description Annotation for efficient Service Retrieval and Composition” by Chotipant et al proposes an automated approach, namely, SERNOTATE, for business service description annotation for efficient service retrieval and composition. To that end, the authors employ new semantic-based linking approaches, namely, Extended Case-based Reasoning, Vector-based and Classification-based, that automatically annotate business services to relevant service concepts. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset. In the third paper,3 “Sensor Data Management in the Cloud: Data Storage, Data Ingestion, and Data Retrieval,” the authors address an increasing need to capture, store, and analyse the dynamic semistructured data from sensors. A similar growth of semistructured data in the modern Web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis. The authors study how sensor data management can benefit from MongoDB with Apache Spark technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. The performance of MongoDB sharding and no-sharding databases with Apache Spark are evaluated to identify the right software environment for sensor data management. Park et al in the fourth paper,4 “Study on the SDN-IP based Solution of Well-known Bottleneck Problems in Private Sector of National R&E Network for Big Data Transfer,” deal with issues arising in Software Defined Networking (SDN). The key feature of SDN networks is the provisioning of dynamic service architecture. Therefore, it is more appropriate for the big data science computing trends, for handling today's big data science, which requires massive parallel processing and a constant demand for additional capacity and on demand connectivity. The authors bring the experience from KREONET (Korea Research Environment Open Network) and Korea National R&E Network, which adopt SDN as a platform service for big data scientific application. The big data application for KREONET SDN service comprises high-energy physics, astrophysics, biology/genetics, meteorology, artificial satellite data, etc. The last paper5 “Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation” by Yang et al investigates issues in private set intersection (PSI) protocols, which enable two parties to compute the intersection of their inputs without compromising anything about the datasets beyond the intersection. The authors present a variant of delegated private set intersection protocol secure in the semi-honest model under RSA assumption as well as an efficient and secure outsourcing computation algorithm for RSA crypto-system. Based on this algorithm, the authors transform a variant of delegated private set intersection protocol into an improved outsourced one, which enables the clients to perform only simple modular multiplication for computing during the execution of the protocol. Compared with the state of the art, the proposed protocol has a great advantage in efficiency. The editor of this special issue would like to thank all authors for their contributions and the reviewers for their constructive and useful feedback to authors. I would like to thank Prof Geoffrey Fox, the Editor-in-Chief of Concurrency and Computation: Practice and Experience, for the opportunity to edit this special issue and his support.

16 - Data duplication using Amazon Web Services cloud storage

An advancement in computing qualities that are both economical and powerful have been made possible by the Internet's dramatic development in processing and storage methods. A new technology that offers internet application access and data storage, cloud computing is becoming more and more popular. Both opportunities and challenges abound in this system. Two extremely important issues that need to be addressed are data security and the growing amount of similar data duplication in cloud. A cloud computing system, supported via distributed & virtual machine technology, offers the most comprehensive services in the most effective way. When using the cloud technology, customers may access their limitless storage space from any location at any time. Cloud service providers are responsive to customer requests to increase data storage capacity by integrating cloud systems with deduplication techniques, while deduplication techniques erase redundant data that is available in cloud environments, according to the company. Preserving data privacy is another significant issue that needs to be acknowledged for its significance. For reducing duplicate data quantity in storage system, the deduplication technique was developed. This study offers a novel method for eliminating duplicate or repetitive information from cloud servers, that might aid in lowering storage space amount and needed bandwidth. It was illustrated that by using testing outcomes, proposed model eliminates the main drawbacks of the existing methods while also offering increased security for data kept in cloud storage

We study the optimal order placement strategy with the presence of a liquidity cost. In this problem, a stock trader wishes to clear her large inventory by a predetermined time horizon T. A trader uses both limit and market orders, and a large market order faces an adverse price movement caused by the liquidity risk. First, we study a single period model where the trader places a limit order and/or a market order at the beginning. We show the behavior of optimal amount of market order, [Formula: see text], and optimal placement of limit order, [Formula: see text], under different market conditions. Next, we extend it to a multi-period model, where the trader makes sequential decisions of limit and market orders at multiple time points.