Current Volume 9
Abstract
Serverless computing is a revolutionary model of cloud computing that provides developers with the capability to run and deploy code without worrying about infrastructure management. Its pay-as-you-go model, scalability, and speedy deployment have resulted in its increasing popularity as a go-to option for contemporary cloud-native applications. Yet, as firms embrace multi-cloud deployments to ensure vendor independence, resiliency, and taking advantage of the strengths of multiple cloud vendors, there is substantial performance difficulty when serverless systems are hosted on multiple cloud platforms. This study seeks to investigate and apply methods for optimizing the performance of serverless systems in multi-cloud environments. It examines how functions may be optimally allocated, scheduled, and tuned across various providers like AWS Lambda, Google Cloud Functions, and Azure Functions. The paper names important performance bottlenecks like cold starts, latency caused by inter-cloud communications, irregular load balancing, and restrictions in observability and monitoring. We propose a hybrid architecture that combines edge computing, container-based execution environments, and AI-orchestration to solve the above issues. With the help of simulation and case studies, the work illustrates how function pre-warming, caching, and adaptive scaling techniques effectively decrease execution latency and enhance throughput with cost-effectiveness. A performance evaluation framework is also presented to compare the proposed solution with traditional serverless models running in single-cloud and naïve multi-cloud scenarios. This research adds to the existing literature on clouds by providing implementable, scalable, and provider-independent methods for serverless performance optimization in complex deployment scenarios. The results of this research are especially important for enterprises that develop highly available and robust systems where performance and agility are paramount. Future research can investigate adding quantum-safe security, green computing metrics, and decentralized registries of functions to further increase the robustness and efficiency of multi-cloud serverless platforms.
Keywords
Serverless Computing, Multi-Cloud Architecture, Performance Optimisation, Cold Start, Function Orchestration, Edge Computing, AI-Driven Scheduling, Cloud Scalability, Function-as-a-Service (FaaS), Cloud Latency, Cloud-Native Applications, Load Balancing, Inter-Cloud Communication, Cloud Monitoring, Hybrid Cloud
Citations
IRE Journals:
Prabhdeep Singh
"Enhancing Performance of Serverless Architectures in Multi-Cloud Environments" Iconic Research And Engineering Journals Volume 8 Issue 12 2025 Page 1757-1765
IEEE:
Prabhdeep Singh
"Enhancing Performance of Serverless Architectures in Multi-Cloud Environments" Iconic Research And Engineering Journals, 8(12)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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