Deadline-aware Dynamic Resource Management in Serverless Computing Environments

Serverless computing enables rapid application development and deployment by composing loosely coupled microservices at a scale. This emerging paradigm greatly unburdens the users of cloud environments, from the need to provision and manage the underlying cloud resources. With this shift in responsibility, the cloud provider faces the challenge of providing acceptable performance to the user without compromising on reliability, while having minimal knowledge of the application requirements. Sub-optimal resource allocations, specifically the CPU resources, could result in the violation of performance requirements of applications. Further, the fine-grained serverless billing model only charges for resource usage in terms of function execution time. At the same time, the provider has to maintain the underlying infrastructure in always-on mode to facilitate asynchronous function calls. Thus, achieving optimum utilization of cloud resources without compromising on application requirements is of high importance to the provider. Most of the current works only focus on minimizing function execution times caused by delays in infrastructure set up and reducing resource costs for the end-user. However, in this paper, we focus on both the provider and user's perspective and propose a function placement policy and a dynamic resource management policy for applications deployed in serverless computing environments. The policies minimize the resource consumption cost for the service provider while meeting the user's application requirement, i.e., deadline. The proposed solutions are sensitive to deadline and efficiently increase the resource utilization for the provider, while dynamically managing resources to improve function response times. We implement and evaluate our approach through simulation using ContainerCloudSim toolkit. The proposed function placement policy when compared with baseline scheduling techniques can reduce resource consumption by up to three times. The dynamic resource allocation policy when evaluated with a fixed resource allocation policy and a proportional CPU-shares policy shows improvements of up to 25% in meeting the required function deadlines.