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@article{151934,
        author = {Saurabh Desale and Varad Gujar and Atharva Raut and Satej Patil and Vitthal Gutte},
        title = {Methods for cloud workload analysis and cloud cost forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {1278-1285},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151934},
        abstract = {Cloud Computing has become the information technology (IT) backbone for all types and size of businesses. Organizations, as well as individual users, are extensively using cloud services and resources for their everyday business transactions to individual IT needs. Most typical cloud computing costing models are based on pay peruse. While such costing models are suitable for many businesses, the challenge it imposes to end consumers is the difficulty to plan the budget as the cost is known only post the cloud resource usage. This paper address this very challenge with cloud storage resource for manifestation, in this paper, we have analyzed workload costing of the ubiquitously consumed cloud storage resource based on the block type of storage known as a standard persistent disk. Based on cloud storage as a resource we simulated storage workload (derived from the publicly available stats of amazon's e-commerce website monthly traffic) using file input-output (fio) simulation tool on a Google Cloud Platform's E2 compute machine. The simulation data is recorded in a time series format that contains cloud storage consumption and cost for each month. This paper then analyses and presents ARIMA time series model which is trained on the simulated data to forecast the cloud storage cost for the next upcoming months.},
        keywords = {Cloud Computing, Forecasting, Workload analysis, cloud cost, Time Series model.},
        month = {},
        }