Enhanced Heterogeneous Cloud: Transparent Acceleration and Elasticity This paper presents ORIAN, a fullymanaged PlatformasaService (PaaS) for deploying highlevel applications onto largescale heterogeneous cloud infrastructures. We aim to make specialised, accelerator resources in the cloud accessible to software developers by extending the traditional homogeneous PaaS execution model to support automatic runtime management of heterogeneous compute resources such as CPUs and FPGAs. In particular, we focus on two mechanisms: transparent acceleration, which automatically maps jobs to the most suitable resource configuration, and heterogeneous elasticity, which performs automatic vertical (type) and horizontal (quantity) scaling of provisioned resources to guarantee QoS (Quality of Service) objectives while minimising cost. We develop a prototype to validate our approach, targeting a hardware platform with combined computational capacity of 28 FPGAs and 36 CPU cores, and evaluate it using case studies in three application domains: machine learning, bioinformatics, and physics. Our transparent acceleration decisions achieve on average 96% of the maximum manually identified static configuration throughput for large workloads, while removing the burden of determining configuration from the user; an elastic ORIAN resource group provides a 2.3 times cost reduction compared to an overprovisioned group for nonuniform, peaked job sequences while guaranteeing QoS objectives; and our malleable architecture extends to support a new, more suitable resource type, automatically reducing the cost by half while maintaining throughput, and achieving a 23% throughput increase while fulfilling resource constraints.

Accelerated serverless computing based on GPU virtualization Abstract   This paper introduces a platform to support serverless computing for scalable eventdriven data processing that features a multilevel elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic Kubernetes cluster whose size automatically grows and shrinks depending on the number of files to be processed. To accelerate the processing time of each file, several approaches involving virtualized access to GPUs, either locally or remote, have been evaluated. A use case that involves the inference based on deep learning techniques on transtoracic echocardiography imaging has been carried out to assess the benefits and limitations of the platform. The results indicate that the combination of serverless computing and GPU virtualization introduce an efficient and costeffective eventdriven accelerated computing approach that can be applied for a wide variety of scientific applications.