Function-as-a-Service execution model in serverless computing has been successful in running large-scale computations like MapReduce, linear algebra, and machine learning. However, little attention has been given to executing highly-dynamic parallel applications with unbalanced and irregular workloads. These algorithms are difficult to execute with good parallel efficiency due to the challenge of provisioning the required computing resources in time, leading to resource over- and under-provisioning in clusters of static size. We propose that the elasticity and fine-grained “pay-as-you-go model” of the FaaS model can be a key enabler for effectively running these algorithms in the cloud. We use a simple serverless executor pool abstraction, and evaluate it using three algorithms with unbalanced and irregular workloads. Results show that their serverless implementation can outperform a static Spark cluster of large virtual machines by up to 55% with the same cost, and can even outperform a single large virtual machine running locally.

Serverless computing greatly simplifies the use of cloud resources. In particular, Function-as-a-Service (FaaS) platforms enable programmers to develop applications as individual functions that can run and scale independently. 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 this work, we aim at bridging this gap. We present Crucial , a system to program highly-parallel stateful serverless applications. Crucial retains the simplicity of serverless computing. It is built upon the key insight that FaaS resembles to concurrent programming at the scale of a datacenter. Accordingly, a distributed shared memory layer is the natural answer to the needs for fine-grained state management and synchronization. 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. We validate Crucial with the help of micro-benchmarks and by considering various stateful applications. Beyond classical parallel tasks (e.g., a Monte Carlo simulation), these applications include representative ML algorithms such as k -means and logistic regression. Our evaluation shows that Crucial obtains superior or comparable performance to Apache Spark at similar cost (18%–40% faster). We also use Crucial to port (part of) a state-of-the-art multi-threaded ML library to serverless. The ported application is up to 30% faster than with a dedicated high-end server. Finally, we attest that Crucial can rival in performance with a single-machine, multi-threaded implementation of a complex coordination problem. Overall, Crucial delivers all these benefits with less than 6% of changes in the code bases of the evaluated applications.

In recent years, serverless computing and, in particular the Function-as-a-Service (Faas) execution model, has proven to be efficient for running parallel computing tasks. However, little attention has been paid to highly-parallel applications with unbalanced and irregular workloads. The main challenge of executing this type of algorithms in the cloud is the difficulty to account for the computing requirements beforehand. This places a burden on scientific users who very often make bad decisions by either overprovisioning resources or inadvertently limiting the parallelism of these algorithms due to resource contention. Our hypothesis is that the elasticity and ease of management of serverless computing can help users avoid such decisions, which may lead to undesirable cost-performance consequences for unbalanced problem spaces. In this work, we show that with a simple serverless executor pool abstraction one can achieve a better cost-performance trade-off than a Spark cluster of static size and large EC2 VMs. To support this conclusion, we evaluate two unbalanced algorithms: the Unbalanced Tree Search (UTS) and the Mandelbrot Set using the Mariani-Silver algorithm. For instance, our serverless implementation of UTS is able to outperform Spark by up to 55% with the same cost. This provides the first concrete evidence that highly-parallel, irregular workloads can be efficiently executed using purely stateless functions with almost zero burden on users - i.e., no need for users to understand non-obvious system-level parameters and optimizations.

Serverless computing is an emerging paradigm that greatly simplifies the usage of cloud resources and suits well to many tasks. Most notably, Function-as-a-Service (FaaS) enables programmers to develop cloud applications as individual functions that can run and scale independently. Yet, due to the disaggregation of storage and compute resources in FaaS, applications that require fine-grained support for mutable state and synchronization, such as machine learning and scientific computing, are hard to build.

Traditionally, active storage techniques have been proposed to move computation tasks to storage nodes in order to exploit data locality. However, we argue in this paper that active storage is ill-suited for cloud storage for two reasons: 1. Lack of elasticity: Computing can only scale out with the number of storage nodes; and 2. Resource Contention: Sharing compute resources can produce interferences in the storage system. Serverless computing is now emerging as a promising alternative for ensuring painless scalability, and also, for simplifying the development of disaggregated computing tasks.