Strategies for Cost-Effective Big Data Management in the Cloud: Leveraging Auto-Scaling and Spot Instances

Abstract

This paper explores advanced strategies for managing large-scale datasets in cloud environments with a focus on minimizing operational costs while ensuring system responsiveness and reliability. Through extensive theoretical modeling and empirical reasoning, a framework for orchestrating big data workflows that leverages auto-scaling capabilities and spot instances is presented. The approach centers on dynamically allocating computational and storage resources in response to fluctuating workloads, guided by stochastic optimization methods. The objective is to address unpredictable spikes in demand that can arise in data analytics pipelines while capitalizing on reduced prices offered through transient, preemptible instances. Mathematical formulations detail the trade-offs between acquiring on-demand resources for guaranteed availability and utilizing cost-effective, potentially volatile spot instances. Performance metrics are derived to quantify the probability of service interruption, expected queuing delays, and overall system throughput. The technique draws on large-scale parallel processing paradigms, including distributed file systems and containerized execution engines, to accommodate multi-terabyte data streams. Results highlight the significance of accurate predictive models and real-time usage monitoring for resource scaling triggers. It is shown that the judicious blend of elastic resource provisioning and ephemeral infrastructure can significantly lower expenditures while maintaining robust service-level guarantees. The paper concludes with future directions for extending these models to more diverse big data scenarios and evolving cloud pricing mechanisms.