Towards Enhanced State Management for Serverless Computation

Identifier:  TDX:4161

Handle: https://hdl.handle.net/20.500.11797/TDX4161

Authors:  Barcelona Pons, Daniel

Abstract:
Cloud computing is becoming more accessible and fitting for many applications. In this line, and with fervent interest from research and industry, there is the concept of serverless. Serverless services hide the existence of servers from users, so that they can focus on their applications. In the trend to exploit the elasticity of serverless computing, distributed parallel computations such as analytics, machine learning, and data processing have special attention from researchers. However, current serverless services are unfitted out-of-the-box to support parallel applications. Broadly, the issues come from a lack of state management, coordination, and predictability. This context opens several research lines; we discuss three: 1) It is unclear how well can current serverless services support distributed parallel computing, which requires execution simultaneity and performance consistency. Current platforms do not ensure that, and users may find extreme variability between services. 2) Current serverless computing exclusively offers stateless ephemeral workers with no direct communication. Applications built on top of these services struggle to manage their mutable global state and coordinate their execution. 3) Intermediate data generated during data processing workloads is forcibly transferred between workers and disaggregated storage. This data-shipping model generates very expensive data movement. In this thesis, we present tree novel contributions to tackle these challenges. First, we study the architectural design of the main serverless platforms and empirically evaluate them to find the platforms that best fit parallel applications. Second, we explore novel methods to code stateful distributed applications in serverless. We present the cloud thread abstraction and build a shared objects layer that allows serverless workers to share and mutate global state and coordinate their execution. Finally, we study a solution to serverless data-shipping through in-storage ephemeral stateful computation. We build a storage system that allows to offload data-bound tasks from serverless workers and significantly reduce data movement.