“Cloud-native” is the umbrella adjective describing the standard approach for developing applications that exploit cloud infrastructures’ scalability and elasticity at their best. As the application complexity and user-bases grow, designing for performance becomes a first-class engineering concern. As an answer to these needs, heterogeneous computing platforms gained widespread attention as powerful tools to continue meeting SLAs for compute-intensive cloud-native workloads. We propose BlastFunction, an FPGA-as-a-Service full-stack framework to ease FPGAs’ adoption for cloud-native workloads, integrating with the vast spectrum of fundamental cloud models. At the IaaS level, BlastFunction time-shares FPGA-based accelerators to provide multi-tenant access to accelerated resources without any code rewriting. At the PaaS level, BlastFunction accelerates functionalities leveraging the serverless model and scales functions proactively, depending on the workload’s performance. Further lowering the FPGAs’ adoption barrier, an accelerators’ registry hosts accelerated functions ready to be used within cloud-native applications, bringing the simplicity of a SaaS-like approach to the developers. After an extensive experimental campaign against state-of-the-art cloud scenarios, we show how BlastFunction leads to higher performance metrics (utilization and throughput) against native execution, with minimal latency and overhead differences. Moreover, the scaling scheme we propose outperforms the main serverless autoscaling algorithms in workload performance and scaling operation amount.

Heterogeneous computing platforms are now a valuable solution to continue to meet Service Level Agreements (SLAs) for compute intensive cloud workloads. Field Programmable Gate Arrays (FPGAs) effectively accelerate cloud workloads, however, these workloads have a spiky behavior as well as long periods of underutilization. Sharing the FPGA with multiple tenants then helps to increase the board's time utilization. In this paper we present BlastFunction, a distributed FPGA sharing system for the acceleration of microservices and serverless applications in cloud environments. BlastFunction includes a Remote OpenCL Library to access the shared devices transparently; multiple Device Managers to time-share and monitor the FPGAs and a central Accelerators Registry to allocate the available devices. BlastFunction reaches higher utilization and throughput w.r.t. a native execution thanks to device sharing, with minimal differences in latency given by the concurrent accesses.

Microservices changed cloud computing by moving the applications’ complexity from one monolithic executable to thousands of network interactions between small components. Given the increasing deployment sizes, the architectural exploitation challenges, and the impact on data-centers’ power consumption, we need to efficiently track this complexity. Within this article, we propose a black-box monitoring approach to track microservices at scale, focusing on architectural metrics, power consumption, application performance, and network performance. The proposed approach is transparent w.r.t. the monitored applications, generates less overhead w.r.t. black-box approaches available in the state-of-the-art, and provides fine-grain accurate metrics.

Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfill its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze beyond its sensors. Unfortunately, the simplicity of agents and the complexity of modeling do not fit together, thus demanding for a third party to bridge the gap. Given the opportunities in the field, the main contributions of this work are twofold: (1) we propose a general methodology to model resource consumption trends and (2) we implemented it into MARC, a Cloud-service platform that produces Models-as-a-Service, thus relieving self-aware agents from the burden of building their custom modeling framework. In order to validate the proposed methodology, we set up a custom simulator to generate a wide spectrum of controlled traces: this allowed us to verify the correctness of our framework from a general and comprehensive point of view.

Multi-tenant virtualized infrastructures allow cloud providers to minimize costs through workload consolidation. One of the largest costs is power consumption, which is challenging to understand in heterogeneous environments. We propose a power modeling methodology that tackles this complexity using a divide-and-conquer approach. Our results outperform previous research work, achieving a relative error of 2% on average and under 4% in almost all cases. Models are portable across similar architectures, enabling predictions of power consumption before migrating a tenant to a different hardware platform. Moreover, we show the models allow us to evaluate colocations of tenants to reduce overall consumption.