Abstract

Elasticity and auto-scaling are fundamental strategies for achieving efficient and cost-effective big data processing in cloud environments, enabling dynamic resource allocation to match fluctuating workload demands. This paper examines elasticity and auto-scaling strategies tailored for big data processing in cloud environments. We explore the principles and mechanisms of elasticity, including horizontal and vertical scaling, as well as auto-scaling policies and triggers based on performance metrics and workload characteristics. Additionally, we review existing auto-scaling frameworks and technologies specifically designed for big data processing platforms in the cloud. Through empirical evaluation and case studies, we assess the effectiveness and scalability of different elasticity and auto-scaling strategies in optimizing resource utilization and reducing operational costs in real-world big data processing scenarios. Furthermore, we discuss challenges, trade-offs, and best practices in implementing and managing elasticity and auto-scaling for big data processing in cloud environments, considering factors such as data locality, task parallelism, and cost optimization. This study aims to provide practical insights and guidelines for practitioners and researchers in effectively leveraging elasticity and auto-scaling strategies to enhance the performance, scalability, and cost-effectiveness of big data processing in cloud environments.

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