Faster and Cheaper Serverless Computing on Harvested Resources - Zhang, Yanqi; Goiri, Íñigo; Chaudhry, Gohar Irfan; Fonseca, Rodrigo; Elnikety, Sameh; Delimitrou, Christina; Bianchini, Ricardo (Proceedings of the International Symposium on Operating Systems Principles (SOSP), 2021)
Serverless computing is becoming increasingly popular due to its ease of programming, fast elasticity, and fine-grained billing. However, the serverless provider still needs to provision, manage, and pay the IaaS provider for the virtual machines (VMs) hosting its platform. This ties the cost of the serverless platform to the cost of the underlying VMs. One way to significantly reduce cost is to use spare resources, which cloud providers rent at a massive discount. Harvest VMs offer such cheap resources: they grow and shrink to harvest all the unallocated CPU cores in their host servers, but may be evicted to make room for more expensive VMs. Thus, using Harvest VMs to run the serverless platform comes with two main challenges that must be carefully managed: VM evictions and dynamically varying resources in each VM. In this work, we explore the challenges and benefits of hosting serverless (Function as a Service or simply FaaS) platforms on Harvest VMs. We characterize the serverless workloads and Harvest VMs of Microsoft Azure, and design a serverless load balancer that is aware of evictions and resource variations in Harvest VMs. We modify OpenWhisk, a widely-used open-source serverless platform, to monitor harvested resources and balance the load accordingly, and evaluate it experimentally. Our results show that adopting harvested resources improves efficiency and reduces cost. Under the same cost budget, running serverless platforms on harvested resources achieves 2.2× to 9.0× higher throughput compared to using dedicated resources. When using the same amount of resources, running serverless platforms on harvested resources achieves 48% to 89% cost savings with lower latency due to better load balancing.