Reinforcement Learning Based Memory Configuration for Linear Dynamic Function Chains

Serverless applications based on microservices typically comprise dozens or hundreds of loosely coupled functions. Each request in these applications triggers a function chain, which invokes a subset of functions. However, the invocation paths may not be predetermined in dynamic function chains. In addition, there are multiple memory configurations for each function in a chain. Different memory configuration combinations bring different execution times and costs. The configuration space grows exponentially as the length of the function chain increases. Therefore, it is challenging to determine the memory configurations for functions in a dynamic function chain to minimize the execution time and cost with an uncertain invocation path and a large configuration space. In this paper, a memory configuration problem of linear dynamic function chains is investigated to minimize the cost while meeting a specified SLO (service level objective). RLMC (Reinforcement Learning-based Memory Configuration Algorithm) is adopted to make memory configuration decisions dynamically. States, actions, and rewards are specially designed for the problem under study. In addition, a punishment factor adjustment strategy is developed to accommodate different SLOs. The proposed algorithm is evaluated and compared to existing algorithms over a comprehensive set of randomly generated serverless workflow applications. Experimental results demonstrate that RLMC significantly reduces the cost of dynamic function chains while meeting SLOs and outperforms other algorithms.