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@article{165541,
        author = {RAJENDRA BALESHWAR and RAMESH VISHWAKARMA},
        title = {Scalable Data Partitioning Strategies for Hybrid Cloud Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {1139-1146},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165541},
        abstract = {When it comes to storage and analytics, private clouds are preferred by enterprises that deal with very sensitive data. Due to the fact that private cloud is designed for usage by a single business, the danger to the organization's security and privacy is significantly reduced. Despite this, private cloud computing does have some restrictions when it comes to the processing of data. The scalability concern is the first limitation that must be considered. The private cloud that is run on-premises does not take into consideration the flood of data, and as a result, it has limited scalability. For the purpose of constructing private clouds that are highly scalable, it is necessary to make a substantial initial expenditure with the intention of acquiring and installing computing resources. When it comes to precisely planning the storage limit of a private cloud, however, the dynamic properties of big data, such as increasing volume, velocity, and diversity, result in issues. As a consequence of this, the capacity of private clouds is perpetually either fully or partially provided. Developing a private cloud that has restricted scalability is being done primarily for the purpose of lowering the amount of money that is being invested in its creation. For the purpose of storing and processing large amounts of data, many cloud computing models, including private cloud, public cloud, and hybrid cloud, are utilized, depending on the requirements.},
        keywords = {Private cloud, Public cloud, Hybrid cloud, Big data},
        month = {},
        }