ORTHRUS: a lightweighted block-level cloud storage system

With the rapid development of computer technology, storage systems become more and more important, the virtual storage technology can simplify storage system and more and more attention. This article briefly describes the concept of virtual storage technology, focusing on the classification of the virtual storage; finally, the application of virtual storage technology in the library was analyzed and discussed.

In the HDFS cloud storage environment, the existing replica strategy is not able to adopt the optimal replica according to the current system environment dynamically, which affects the performance of the Cloud Storage system. This paper aims at the replica selection strategies in the load balancing problem, proposes a Replica Strategy based on grey prediction theory in Cloud Storage and predicts the node load rate of different replicas at a moment, selecting the minimum load node dynamically, eventually to load balance.

In this paper, we propose to use a dynamic load balancing scheme that utilizes fixed relay stations placed in a cellular geographical coverage area to improve the handover performance in cellular networks. To this end, we develop closed-form performance expressions and derive the handover dropping probability in terms of the main system parameters, such as the new call and handover arrival rates, the number of fixed and load-balancing channels per cell, the load balancing probability for new and handover calls, etc. Our results show that by employing a dynamic load balancing scheme, not only the new call blocking probability, but also the handover dropping probability can be improved substantially, without reserving any channels or assigning any priority to handover calls. In fact, employing dynamic load balancing without any guard channels outperforms a conventional system that employs the well-known guard channel method (GCM).

Feng, X.; Wan, M., and Li, S., 2020. The design of marine remote sensing image fusion model based on dynamic load balancing strategy. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 943–946. Coconut Creek (Florida), ISSN 0749-0208.In this work, a model of marine remote sensing image fusion based on dynamic load balancing is proposed. The remote sensing image acquisition system based on Android intelligent device obtains the marine remote sensing image, and enhances and registers the image based on the fuzzy theory and template matching, respectively, to realize the image preprocessing. Based on the preprocessing results, considering the small number of objects in the marine remote sensing image, the target area is divided by the idea of quad tree structure. At the same time, the task allocation strategy of dynamic load balancing and the idea of parallel computing are used to propose the task allocation algorithm processing model of the cluster system for the fusion processing of the image in the target area, so as to achieve the fusion of marine remote sensing image. It can be known that the proposed model takes less time and has superior average gradient and clarity of image.

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).

Today more and more enterprises and individuals are outsourcing their data to cloud storage system. Data deduplication is one of the important technologies to reduce the storage cost of cloud storage system. In a cloud storage system with deduplication technology, the client can outsource the data files to the cloud storage server and pay for them. Fair payment is one of key issues in the cloud deduplication storage system. At present, a variety of secure deduplication encryption schemes have been designed to protect the privacy of client data. However, most existing fair payment solutions use traditional electronic cash systems to generate payment tokens, which requires a trusted authority to prevent double-spending. Trusted authorities will become bottlenecks in the payment system. Faced with this problem, in this paper, we propose a new decentralized fair payment protocol for cloud deduplication storage system by utilizing ethereum blockchain technology. The new protocol takes advantage of the decentralization of blockchain technology, allowing direct transactions without the participation of trusted third parties. In the new protocol, if a malicious situation occurs, the system can guarantee fair payment by pre-storing penalty money in the smart contract. Safety analysis and experimental analysis show that our new protocol is feasible.

Multi-authority proxy re-encryption based on CPABE for cloud storage systems

PLUM : Parallel Load Balancing for Adaptive Unstructured Meshes

Large scale computing requires parallelization in order to arrive at solution at reasonable time. Today parallelization is a standard in fluid problems simulation. On the other hand adaptation is a technique that allows for dynamic modification of the mesh as the need for locally higher resolution arises. Adaptation used during parallel simulation leads to unbalanced numerical load. This in turn decreases the efficiency of parallelization. Dynamic load balancing strategies should be applied in order to ensure proper parallelization efficiency. The paper presents the potential benefits of applying the dynamic load balancing to adaptive flow problems simulated in parallel environments.

We present an analysis of the feasibility of executing a parallel bioinspired model of laser dynamics, based on cellular automata (CA), on the usual target platform of this kind of applications: a heterogeneous non-dedicated cluster. As this model employs a synchronous CA, using the single program, multiple data (SPMD) paradigm, it is not clear in advance if an appropriate efficiency can be obtained on this kind of platform. We have evaluated its performance including artificial load to simulate other tasks or jobs submitted by other users. A dynamic load balancing strategy with two main differences from most previous implementations of CA based models has been used. First, it is possible to migrate load to cluster nodes initially not belonging to the pool. Second, a modular approach is taken in which the model is executed on top of a dynamic load balancing tool – the Dynamite system – gaining flexibility. Very satisfactory results have been obtained, with performance increases from 60% to 80%.

In this paper, we present a novel fountain code-based hybrid storage system that combines cloud storage with P2P storage. In the cloud storage system, all the data are consistently stored at server clusters but the data may be exposed to others accessing server clusters. On the other hand, in the P2P storage system, the data are distributed to a group of participating peers but the data retrievability may be significantly degraded. The proposed hybrid system is designed to provide the desired data retrievability with a short upload time and maintain the privacy to avoid other users from reading the contents.

Cloud storage system can reshape the way how kinds of contents are generated, stored and distributed on today's Internet. It is intriguing to study the service provision of cloud storage system for the sake of providing global users with satisfactory service. This paper conducts modeling analysis of a cloud storage system, and proposes a Markov decision process modeling framework to analyze the reliability of the storage system. Based on the modeling analysis, the optimal strategy can be got after executing two solution algorithms. By incorporating these techniques into cloud storage system design, the authors can significantly improve the replication performance and reliability of the system in this paper. Our numerical results further demonstrate the effectiveness and superiority of our technique against traditional static approaches.

This research paper is a survey of the present state of design and implementation of distributed file systems. In a distributed file system, one or more central servers store files that can be accessed, with proper authorization rights. It consists of four parts: an Introduction of Distributed file system, implementation, architecture and component of distributed file system and their design goals for good distributed file system i.e. transparency – the main goal of good DFS. In this paper we also discuss some distributed file systems like NFS, CIFS, OwFS and HDFS. We explain features, architecture of above file system. This paper also include the criteria for choosing right Distributed file system of our need and recommendation foe right Distributed File System .The above file systems are also compared on the basis of File size, Analytic functionality, TCO and their selection criteria. This research also included the future aspect of distributed file systems like the problem we based in current DFS like transparency and at last the conclusion of the whole paper on the basis of above survey.

This paper presents a novel distributed file system TLDFS, which consists of two layers. One is the block device layer, which makes the distributed file system manage the consistency of data easily by choosing an appropriate granularity of lock. We implement the distributed interfaces and the new data organization at the block level, which is different from most distributed file systems. The other is the file system layer that consists of three parts: interconnecting module, locking module and distributed file system module. Our prototype system proves the feasibility of the two-layered structure and the experiments show several advantages of TLDFS.

Handling of Big Data and cloud computing are the two important prime concerns which have become more and more popular in recent years. Due to tremendous hike in data production, the need for the efficient processing, transaction storage or retrieval, and management of the structured and unstructured data have become one of the important issues of the IT industry. In this paper, we examine the concept of evolution of various distributed file systems, advantages, and limitations with respect to the cloud computing paradigm. Distributed File System is a client-based application which permits its users to access, process and modify the data which is stored on a remote server as if it existed on their local systems. The Google file system (GFS), a proprietary distributed file system was developed by Google to meet its own growing big data need, which involved the utilization of commodity hardware in managing the massive generation of raw data. Inspired by GFS, Hadoop Distributed File System (HDFS) was developed, which is an open-source community project. It is designed to store large chunks of data sets consistently as well as providing high bandwidth sets for streaming data on client based applications. In this paper, we describe the GFS and HDFS models in detail and relate these two distributed file systems on the basis of their properties and characteristic behavior in different environments. We also discuss several techniques and modifications to prevent and mitigate the limitations of these file systems.