• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3870-3884
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• 2020

The proposed framework of Four Layer Robust Storage in Cloud (FLRSC) architecture involves host server, local host and edge devices in addition to Virtual Machine Monitoring (VMM). The goal is to protect the privacy of stored data at edge devices. The computational intelligence (CI) part of our algorithm distributes blocks of data to three different layers by partially encoded and forwarded for decoding to the next layer using hash and greed Solomon algorithms. VMM monitoring uses snapshot algorithm to detect intrusion. The proposed system is compared with Tiang Wang method to validate efficiency of data transfer with security. Hence, security is proven against the indexed efficiency. It is an important study to integrate communication between local host software and nearer edge devices through different channels by verifying snapshot using lamport mechanism to ensure integrity and security at software level thereby reducing the latency. It also provides thorough knowledge and understanding about data communication at software level with VMM. The performance evaluation and feasibility study of security in FLRSC against three-layered approach is proven over 2³² blocks of data with 98% accuracy. Practical implications and contributions to the growing knowledge base are highlighted along with directions for further research.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.95-102
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• 2024

In distributed computing, accessible encryption strategy over Auditable data is a hot research field. Be that as it may, most existing system on encoded look and auditable over outsourced cloud information and disregard customized seek goal. Distributed storage space get to manage is imperative for the security of given information, where information security is executed just for the encoded content. It is a smaller amount secure in light of the fact that the Intruder has been endeavored to separate the scrambled records or Information. To determine this issue we have actualize (CBC) figure piece fastening. It is tied in with adding XOR each plaintext piece to the figure content square that was already delivered. We propose a novel heterogeneous structure to evaluate the issue of single-point execution bottleneck and give a more proficient access control plot with a reviewing component. In the interim, in our plan, a CA (Central Authority) is acquainted with create mystery keys for authenticity confirmed clients. Not at all like other multi specialist get to control plots, each of the experts in our plan deals with the entire trait set independently. Keywords: Cloud storage, Access control, Auditing, CBC.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.19-29
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• 2012

Currently, cloud computing technology is being supplied in various service forms and it is becoming a ground breaking service which provides usage of storage service, data and software while user is not involved in technical background such as physical location of service or system environment. cloud computing technology has advantages that it can use easily as many IT resources as it wants freely regardless of hardware issues required by a variety of systems and service level required by infrastructure. Also, since it has a strength that it can choose usage of resource about business model due to various internet-based technologies, provisioning technology and virtualization technology are being paid attention as main technologies. These technologies are ones of important technology elements which help web-based users approach freely and install according to user environment. Therefore, this thesis introduces software-related technologies and architectures in an aspect of grid for building up high availability cloud computing environment by analysis about cloud computing technology trend.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.339-346
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• 2016

1 person media is becoming the leading trend among several media in the Internet era exploiting the individual desire of showing off. But, the vast accessibility of Internet produces the worry about privacy risk, which concludes in the increasement of closed SNS. In this paper, we propose a system based on PWW (Person Wide Web) where a person is producing a media and share it with other persons. PWW is an information system which consists of a smart-phone, mobile anchors, a standard web document, and his own cloud storage. An information consumer gets the link using his smart-phone from the mobile anchors attached on the objects in the field. The web browser in the smart-phone obtains the web documents designated by the link and presents it. We also explains the 1 person media system based on PWW and presents the example utilized in the field. We compared and analyzed the security factor of the system based on between WWW and PWW, and concluded that PWW is better than WWW in the aspect of security.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.347-357
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• 2019

The usefulness of processing and analysis systems of GIS-based agricultural climate data is affected by the reliability and availability of computing infrastructures such as cloud, on-premises, and hybrid. Cloud technology has grown in popularity. However, various reference cases accumulated over the years of operational experiences point out important features that make on-premises technology compatible with cloud technology. Both cloud and on-premises technologies have their advantages and disadvantages in terms of operational time and cost, reliability, and security depending on cases of applications. In this study, we have described characteristics of four general computing platforms including cloud, on-premises with hardware-level virtualization, on-premises with operating system-level virtualization and hybrid environments, and compared them in terms of advantages and disadvantages when a huge amount of GIS-based agricultural climate data were stored and processed to provide public services of agro-meteorological and climate information at high spatial and temporal resolutions. It was found that migrating high-resolution agricultural climate data to public cloud would not be reasonable due to high cost for storing a large amount data that may be of no use in the future. Therefore, we recommended hybrid systems that the on-premises and the cloud environments are combined for data storage and backup systems that incur a major cost, and data analysis, processing and presentation that need operational flexibility, respectively.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.59-65
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• 2016

With the recent growth in cloud computing, big data processing and collaboration between businesses are emerging as new paradigms in the IT industry. In an environment where a large amount of data is generated in real time, such as SNS, big data processing techniques are useful in extracting the valid data. MapReduce is a good example of such a programming model used in big data extraction. With the growing collaboration between companies, problems of duplication and heterogeneity among data due to the integration of old and new information storage systems have arisen. These problems arise because of the differences in existing databases across the various companies. However, these problems can be negated by implementing the MapReduce technique. This paper proposes a collaboration system based on Database as a Service, or DBaaS, to solve problems in data integration for collaboration between companies. The proposed system can reduce the overhead in data integration, while being applied to structured and unstructured data.

• Journal of KIISE
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• v.44 no.8
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• pp.767-773
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• 2017

In a distributed system environment, such as a cloud database, the tail latency needs to be kept short to ensure uniform quality of service. In this paper, through experiments on a Cassandra database, we show that long tail latency is caused by a lack of memory space because the database cannot receive any request until free space is reclaimed by writing the buffered data to the storage device. We observed that, since the performance of the storage device determines the amount of time required for writing the buffered data, the performance degradation of Solid State Drive (SSD) due to garbage collection results in a longer tail latency. We propose a garbage collection synchronization technique, called SyncGC, that simultaneously performs garbage collection in the java virtual machine and in the garbage collection in SSD concurrently, thus hiding garbage collection overheads in the SSD. Our evaluations on real SSDs show that SyncGC reduces the tail latency of $99.9^{th}$ and, $99.9^{th}-percentile$ by 31% and 36%, respectively.