Energy-Efficient and Cost-Optimized Serverless Computing
summary
Claims concrete savings and implemented techniques: energy-aware autoscaling using ML reduces energy waste by up to 30%, ARM-based processors and hardware accelerators cut power by ~40%, and function chaining optimization halves inter-function overhead. Case results include a retailer handling 2.5 million transactions per hour with 60% lower server energy and $2.1M annual savings, healthcare processing 50,000 records/min with 40% energy cut and $800k saved, and a finance platform at 100,000 tx/s with 30% energy and 45% cost reductions. Carbon-aware scheduling and AI-driven allocation drive the claimed 25–40% cost and energy drops.
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chapters (9)
- 0:00Introduction to Serverless Computing
- 0:43Why Serverless Computing is Trending
- 2:00Addressing Energy Footprints in Serverless
- 3:07Emerging Energy-Aware Pricing Models
- 3:52Real-World Case Studies and Success Stories
- 5:18Challenges and Opportunities in Sustainable Serverless
- 6:28Future Directions for Sustainable Serverless
- 7:32The Future of Serverless: A New Era of Sustainability
- 8:49Conclusion: A Sustainable Path Forward
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