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.
Serverless computing (Function-as-a-Service) provides fine-grain resource sharing by running functions (or Lambdas) in containers. Data-dependent functions are required to be invoked following a pre-defined logic, which is known as serverless workflows. However, our investigation shows that the traditional master-worker based workflow execution architecture performs poorly in serverless context. One significant overhead results from the master-side workflow schedule pattern, with which the functions are triggered in the master node and assigned to worker nodes for execution. Besides, the data movement between workers also reduces the throughput.To this end, we present a worker-side workflow schedule pattern for serverless workflow execution. Following the design, we implement FaaSFlow to enable efficient workflow execution in the serverless context. Besides, we propose an adaptive storage library FaaStore that enables fast data transfer between functions on the same node without through the database. Experiment results show that FaaSFlow effectively mitigates the workflow scheduling overhead by 74.6% on average and data transmission overhead by 95% at most. When the network bandwidth fluctuates, FaaSFlow-FaaStore reduces the throughput degradation by 23.0%, and is able to multiply the utilization of network bandwidth by 1.5đť‘‹ -4đť‘‹ .
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