The growing popularity of microservices has led to the proliferation of online cloud service-based applications, which are typically modelled as Directed Acyclic Graphs (DAGs) comprising of tens to hundreds of microservices. The vast majority of these applications are user-facing, and hence, have stringent SLO requirements. Serverless functions, having short resource provisioning times and instant scalability, are suitable candidates for developing such latency-critical applications. However, existing serverless providers are unaware of the workflow characteristics of application DAGs, leading to container over-provisioning in many cases. This is further exacerbated in the case of dynamic DAGs, where the function chain for an application is not known a priori. Motivated by these observations, we propose Kraken, a workflow-aware resource management framework that minimizes the number of containers provisioned for an application DAG while ensuring SLO-compliance. We design and implement Kraken on OpenFaaS and evaluate it on a multi-node Kubernetes-managed cluster. Our extensive experimental evaluation using DeathStarbench workload suite and real-world traces demonstrates that Kraken spawns up to 76% fewer containers, thereby improving container utilization and saving cluster-wide energy by up to 4x and 48%, respectively, when compared to state-of-the art schedulers employed in serverless platforms.