10th International Congress on Information and Communication Technology

Authors - Aayush Kulkarni, Mangesh bedekar, Shamla Mantri
Abstract - This paper proposes a novel serverless computing model that addresses critical challenges in current architectures, namely cold start latency, resource inefficiency, and scalability limitations. The research integrates advanced caching mechanisms, intelligent load balancing, and quantum computing techniques to enhance serverless platform performance. Advanced distributed caching with coherence protocols is implemented to mitigate cold start issues. An AI-driven load balancer dynamically allocates resources based on real-time metrics, optimizing resource utilization. The integration of quantum computing algorithms aims to accelerate specific serverless workloads. Simulations and comparative tests demonstrate significant improvements in latency reduction, cost efficiency, scalability, and throughput compared to traditional serverless models. While quantum integration remains largely theoretical, early results suggest potential for substantial performance gains in tasks like function lookups and complex data processing. This research contributes to the evolving landscape of cloud computing, offering insights into optimizing serverless architectures for future applications in edge computing, AI, and data-intensive fields. The proposed model sets a foundation for more efficient, responsive, and scalable cloud solutions.