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We consider the setting of serverless Function-as-a-Service (FaaS) platforms, where storage services are disaggregated from the machines that support function execution. FaaS applications consist of compositions of functions, each of which may run on a separate machine and access remote storage. The challenge we address is improving I/O latency in this setting while also providing application-wide consistency. Previous work has explored providing causal consistency for individual I/Os by carefully managing the versions stored in a client-side data cache. In our setting, a single application may execute multiple functions across different nodes, and therefore issue interrelated I/Os to multiple distinct caches. This raises the challenge of Multisite Transactional Causal Consistency (MTCC): the ability to provide causal consistency for all I/Os within a given transaction even if it runs across multiple physical sites. We present protocols for MTCC implemented in a system called HYDROCACHE. Our evaluation demonstrates orders-of-magnitude performance improvements due to caching, while also protecting against consistency anomalies that otherwise arise frequently. 

Serverless computing has grown in popularity in recent years, with an increasing number of applications being built on Functions-as-a-Service (FaaS) platforms. By default, FaaS platforms support retry-based fault tolerance, but this is insufficient for programs that modify shared state, as they can unwittingly persist partial sets of updates in case of failures. To address this challenge, we would like atomic visibility of the updates made by a FaaS application. In this paper, we present aft, an atomic fault tolerance shim for serverless applications. aft interposes between a commodity FaaS platform and storage engine and ensures atomic visibility of updates by enforcing the read atomic isolation guarantee. aft supports new protocols to guarantee read atomic isolation in the serverless setting. We demonstrate that aft introduces minimal overhead relative to existing storage engines and scales smoothly to thousands of requests per second, while preventing a significant number of consistency anomalies.