# GPU-Enabled Serverless Workflows for Efficient Multimedia Processing
## 2 Related Work  
There are previous works in the literature that have adopted serverless for scientific computing. Indeed, pioneers in this area started to adopt AWS Lambda as a general computing platform aimed at scientific computing in order to take advantage of the massive elasticity of this service. This is the case of [PERSON] et al. [6], by introducing PyWren to execute distributed Python code across multiple Lambda function invocations to achieve a virtualized supercomputer. In addition, the work by [PERSON] et al. [7] used AWS Lambda as the computing platform on which to execute MapReduce jobs for increased elasticity without preprovisioning a Hadoop cluster.  
The usage of serverless computing for the execution of workflows has initially started to be explored. For example, the work by [PERSON] et al. [8] evaluates the aplicability of serverless computing for compute and data intensive scientific workflows through the creation of a prototype. This is in line with the work by [PERSON] et al. [9] which integrates a combination of Functions as a Service (FaaS)/local clusters execution approach for Montage-based workflows. The work by [PERSON] et al. [10] describes a service that processes large collections of scientific files to extract metadata from diverse file types, relying on Function as a Service models to enable scalability. Furthermore, the work by [PERSON] et al. [11] proposes funcX, a high-performance FaaS platform for flexible, efficient,and scalable, remote function execution on infrastructures such as clouds, clusters, and supercomputers. The work by [PERSON] et al. [12] focuses on high-performance serverless computing by introducing a serverless computing system with enhanced capabilities such as application-level sandboxing and a hierarchical message bus in order to achieve better resource usage and more efficient startup.  
The adoption of cloud computing for multimedia processing is certainly another active field of study. Indeed, the paper by [PERSON] et al. [13] already addressed the application of scientific workflows for multimedia content processing, leveraging the Wings [14] framework to efficiently analyse large-scale multimedia content across the Pegasus engine [15], which operates over cloud infrastructures. Another example of the adoption of cloud technologies in this area is the study conducted by [PERSON] et al. [16], where they propose the development of a workflow scheduling system for cloudlets based on Blockchain, ensuring the QoS of multimedia applications in these small-scale data centres near the edge. Finally, the study carried out by [PERSON] et al. [17] specifically focuses on cost-effective serverless video processing. They quantify the influence of different implementation schemes on the execution duration and economic cost from the perspective of the developer. Nonetheless, support for GPU-based processing within the serverless computing paradigm is an open issue nowadays.  
The main contribution of this paper to the state of the art is the development of an open-source tool to support serverless computing for mixed data-driven workflows that involve disparate computing requirements for the different phases, being able to simultaneously harness GPU and CPU computing and the highly elasticity provided by AWS Lambda. To the best of the authors' knowledge this has not been addressed in the past.