Abstract
A Horizontal scaling is a Cloud architectural strategy by which the number of nodes or computers increased to meet the demand of continuously increasing workload. The cost of compute instances increases with increased workload & the research is aimed to bring an optimization of the reserved Cloud instances using principles of Inventory theory applied to IoT datasets with variable stochastic nature. With a structured solution architecture laid down for the business problem to understand the checkpoints of compute instances – the range of approximate reserved compute instances have been optimized & pinpointed by analysing the probability distribution curves of the IoT datasets. The Inventory theory applied to the distribution curves of the data provides the optimized number of compute instances required taking the range prescribed from the solution architecture. The solution would help Cloud solution architects & Project sponsors in planning the compute power required in AWS® Cloud platform in any business situation where ingestion & processing data of stochastic nature is a business need.
Highlights
The background of doing the research lies in the works of Sidney Brown et al [1] in his paper concerned with (r, q) inventory model interpreted where demand accumulates continuously the demand rate at any instant is determined by an underlying stochastic process.Andrea Nodari [2] in his master’s degree thesis has aimed to answer the few research questions, one of them being the modelling the cost optimization in Cloud Computing with Inventory theory
The technical challenge to the business problem lies in interpreting IoT data probability distribution curves on which Inventory theory has been applied & interpreted of the distribution to optimize the compute instance
The value adds, I proposed will support the contextual solution architecture approximation of compute resources assume a range of compute instances at the very high level of solution based on assumptions to streamline & pinpoint the number of instances by interpretation of actual data response applying Inventory theory applied to their probability distributions
Summary
The background of doing the research lies in the works of Sidney Brown et al [1] in his paper concerned with (r, q) inventory model interpreted where demand accumulates continuously the demand rate at any instant is determined by an underlying stochastic process. My objective in the paper has been to understand the background of the solution architecture required to ingest, process & store IoT datasets into AWS® platform & optimize the same by interpreting the distribution curves of IoT data captured on Cloud. The technical challenge to the business problem lies in interpreting IoT data probability distribution curves on which Inventory theory has been applied & interpreted of the distribution to optimize the compute instance (e.g., uniform distribution, Gaussian distribution curves being interpreted in my work with the data captured from IoT devices). My aim was to perform a validation exercise using a dataset emanated from an IoT device(s) and capture the many iterations of the data in a database to validate the stochastic nature of the source of data & understanding the distribution of the data & I believe this research will provide a foundation on which a dynamically time continuous stochastic data ecosystem can be assessed and provided with the optimized plan for instances.
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