![]() Beyond classical parallel tasks (e.g., a Monte Carlo simulation), these applications include representative ML algorithms such as k -means and logistic regression. We validate Crucial with the help of micro-benchmarks and by considering various stateful applications. Crucial allows to port effortlessly a multi-threaded code base to serverless, where it can benefit from the scalability and pay-per-use model of FaaS platforms. Accordingly, a distributed shared memory layer is the natural answer to the needs for fine-grained state management and synchronization. It is built upon the key insight that FaaS resembles to concurrent programming at the scale of a datacenter. Crucial retains the simplicity of serverless computing. We present Crucial, a system to program highly-parallel stateful serverless applications. In this work, we aim at bridging this gap. Unfortunately, applications that require fine-grained support for mutable state and synchronization, such as machine learning (ML) and scientific computing, are notoriously hard to build with this new paradigm. In particular, Function-as-a-Service (FaaS) platforms enable programmers to develop applications as individual functions that can run and scale independently. Serverless computing greatly simplifies the use of cloud resources. In addition to offering more features and stronger guarantees than existing FaaS platforms, Apiary outperforms them by 2-68x on microservice workloads by greatly reducing communication and coordination overhead and using cluster resources more efficiently. Apiary augments the DBMS with scheduling and tracing layers, providing a workflow programming interface for composing functions into larger programs with end-to-end exactly-once semantics, cross-function transactions, and advanced observability capabilities. ![]() Apiary tightly integrates application logic and data management, building a unified runtime for function execution and data management by wrapping a distributed database engine and its stored procedures. ![]() We present Apiary, a novel DBMS-backed transactional FaaS framework for data-centric applications. This separation harms performance and makes it difficult to efficiently provide transactional guarantees. Unfortunately, existing and recently proposed FaaS platforms support these applications poorly because they separate application logic, executed in cloud functions, from data management, done in interactive transactions accessing remote storage. Empirical results on benchmarks and diverse applications show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.ĭevelopers are increasingly using function-as-a-service (FaaS) platforms for data-centric applications that primarily perform low-latency and transactional operations on data, such as for microservices or web serving workloads. To this end, Cloudburst provides a combination of lattice-encapsulated state and new definitions and protocols for distributed session consistency. Performant cache consistency emerges as a key challenge in this architecture. Cloudburst accomplishes this by leveraging Anna, an autoscaling key-value store, for state sharing and overlay routing combined with mutable caches co-located with function executors for data locality. We present the design and implementation of Cloudburst, a stateful FaaS platform that provides familiar Python programming with low-latency mutable state and communication, while maintaining the autoscaling benefits of serverless computing. We argue that the benefits of serverless computing can be extended to a broader range of applications and algorithms while maintaining the key benefits of existing FaaS offerings. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. ![]() Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular due to ease-of-use and operational simplicity.
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