FaaSCom: Mitigate Cold Start Problem in FaaS via Function Community

Function as a Service (FaaS) has become a popular computing paradigm. FaaS shifts the burden of resource management to cloud providers and enables users to focus only on the logic of the code. At the same time, FaaS has thrived due to its scalability and billing strategies. However, cold start has been always a major challenge for FaaS which causes additional delay. Existing cold start mitigation methods ignore dependencies among functions resulting in bloated function combinations and consequently unnecessary resource consumption. In this paper, we propose FaaSCom, a FaaS scheduler which mitigates cold start problem from the perspective of preallocating containers. We mine causality among functions and divide the function dependency graph into communities. By scheduling containers according to the function communities, we can reduce the number of containers provisioned for functions to decrease resource consumption while not increasing cold start occurrences. Besides, we design an extended histogram policy to mitigate cold starts for long-time interval functions. We evaluate our method on an industrial serverless dataset. Compared with baseline methods, FaaSCom reduces the cold start rate by 8.4% while saving 50% memory consumption.