Cloud Cost Optimization in High-Throughput Distributed Systems

Author(s)	Saurabh Atri
Country	United States
Abstract	As organizations scale their distributed systems to handle billions of transactions, cloud infrastructure costs often spiral out of control. These expenses stem not only from compute but also from data transfer, storage, and orchestration overhead. With the rise of AI workloads, real-time analytics, and IoT event streams, cost optimization has become a critical design dimension. This paper explores end-to-end strategies to optimize cloud spending in high-throughput environments by combining architectural, operational, and algorithmic techniques. We focus on three major pillars: (1) compute economics: contrasting serverless and containerized models; (2) data locality: reducing transfer and replication costs; and (3) cost-aware load balancing: routing traffic based on both latency and financial metrics. The paper provides actionable insights for engineers designing cost-efficient systems that maintain throughput and reliability. [1][2][3]
Keywords	Cloud Cost Optimization, Serverless, Containers, Data Locality, Cost-Aware Load Balancing, FinOps, Distributed Systems, Cloud Architecture.
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-21
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9551
Short DOI	https://doi.org/hbb8f6

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International Journal on Science and Technology