The Serverless Computing Survey: A Technical Primer for Design Architecture

Abstract

The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has recently attracted more and more attention in both industry and academia. Due to its inherent scalability and flexibility, serverless computing becomes attractive and more pervasive for ever-growing Internet services. Despite the momentum in the cloud-native community, the existing challenges and compromises still wait for more advanced research and solutions to further explore the potentials of the serverless computing model. As a contribution to this knowledge, this article surveys and elaborates the research domains in the serverless context by decoupling the architecture into four stack layers: Virtualization, Encapsule, System Orchestration, and System Coordination. Inspired by the security model, we highlight the key implications and limitations of these works in each layer, and make suggestions for potential challenges to the field of future serverless computing.

FaaSFlow: enable efficient workflow execution for function-as-a-service

“…Cold startups and prewarm strategies. In serverless computing or FaaS-based workflows, it is a critical problem that functions need to experience the cold startup for the first invocation, which has been a significant topic for researchers to reduce the performance degradation due to cold startups [29,39,46,47,53,58]. Shahrad et al [53] propose a dynamic resource management policy for the private prewarmed container pool.…”

“…In this scenario, the serverless functions run in user pre-defined logic order, representing the workflow's control-plane. These serverless workflows usually contain parallel branches and data dependencies, and are logically defined through DAG (Direct Acyclic Graph) [18,23,24,39,57,65].…”

“…On the other hand, the engine łrandomlyž distributes the triggered functions to worker nodes for load balance, and cloud vendors impose quotas on the input and output data size of functions to avoid serious consumption of network bandwidth [10ś12, 31]. In production serverless platforms, users often rely on additional database storage services for temporary data storage and delivery, suffering significant data movement overhead [36,37,39,55,56].…”

Locality-aware Load-Balancing For Serverless Clusters

“…The initialization overheads of serverless functions and their repeated invocations have spawned a great deal of research into optimizing their resource management. Recent surveys [14,18,23,25,29] provide an overview of the challenges and solutions in this very active research area.…”

BriskChain: Decentralized Function Composition for High-performance Serverless Computing

“…Users often have to make use of additional database storage services for temporary data storage and delivery 2 . Therefore, this pattern is not very suitable for applications that require intensive interactions between smaller tasks 3 .…”