Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions

ACM Computing Surveys, Journal Year: 2024, Volume and Issue: 57(3), P. 1 - 36

Published: Oct. 17, 2024

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability an economic model. In users only pay for time they actually use resources, enabling zero scaling optimise cost resource utilisation. However, this approach also introduces cold start problem. Researchers developed various solutions address problem, yet it remains unresolved research area. article, we propose a systematic literature review on latency in computing. Furthermore, create detailed taxonomy of approaches latency, investigate existing techniques reducing frequency. We classified current studies into several categories such as caching application-level optimisation-based solutions, well Artificial Intelligence/Machine Learning-based solutions. Moreover, analyzed impact quality service, explored mitigation methods, datasets, implementation platforms, them based their common characteristics features. Finally, outline open challenges highlight possible future directions.

Language: Английский

RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications

Electronics, Journal Year: 2024, Volume and Issue: 13(22), P. 4462 - 4462

Published: Nov. 14, 2024

Artificial Intelligence (AI) applications are rapidly growing, and more joining the market competition. As a result, AI-as-a-service (AIaaS) model is experiencing rapid growth. Many of these AIaaS-based not properly optimized initially. Once they start large volume traffic, different challenges revealing themselves. One maintaining profit margin for sustainability AIaaS application-based business model, which depends on proper utilization computing resources. This paper introduces resource award predictive (RAP) cost optimization called RAP-Optimizer. It developed by combining deep neural network (DNN) with simulated annealing algorithm. designed to reduce underutilization minimize number active hosts in cloud environments. dynamically allocates resources handles API requests efficiently. The RAP-Optimizer reduces physical an average 5 per day, leading 45% decrease server costs. impact was observed over 12-month period. observational data show significant improvement utilization. effectively operational costs from USD 2600 1250 month. Furthermore, increases 179%, 600 1675 inclusion dynamic dropout control (DDC) algorithm DNN training process mitigates overfitting, achieving 97.48% validation accuracy loss 2.82%. These results indicate that enhances management cost-efficiency applications, making it valuable solution modern

Language: Английский