Gianluigi Zavattaro

APP is a declarative language for the definition of custom function scheduling on the worker nodes available in serverless Function as a Service (FaaS) platforms. Current APP platforms assume a central control point that users can access to issue the execution of functions. We propose dAPP, a decentralised version of APP where multiple control points tackle both scaling and resilience issues of existing implementations. We formalise the semantics of dAPP and use it to show that despite APP and dAPP are not equivalent according to a trace-based semantics, they produce the same distributions of functions over the available workers. We also present an implementation of dAPP using the FunLess FaaS platform – tailored for private edge-cloud and multi-cloud environments – and show initial empirical evidence of performance improvements in setups where both the platform and function invocations are spread across multiple locations.

We study a theory of asynchronous session types ensuring that well-typed processes terminate under a suitable fairness assumption. Fair termination entails starvation freedom and orphan message freedom namely that all messages, including those that are produced early taking advantage of asynchrony, are eventually consumed. The theory is based on a novel fair asynchronous subtyping relation for session types that is coarser than the existing ones. The type system is also the first of its kind that is firmly rooted in linear logic: fair asynchronous subtyping is incorporated as a natural generalization of the cut and axiom rules of linear logic and asynchronous communication is modeled through a suitable set of commuting conversions and of deep cut reductions in linear logic proofs.

Cloud-Edge application deployment involves placing multiple software components on infrastructural topologies of heterogeneous nodes, ranging from Cloud servers to Internet-of-Things (IoT) edge devices. When multiple versions (or “ flavours ”) of a component are available, application managers must select a flavour for each deployed component, and assign these components to specific nodes, all while considering constraints such as dependencies, quality of service (QoS), budget, operational costs, and carbon emissions. In complex scenarios, finding the optimal deployment is often infeasible for human operators without automated tools to systematically explore the solution space. To address this challenge, we introduce FREEDA, a first constraint optimisation approach for deploying constrained and multi-flavoured applications on Cloud-Edge infrastructure topologies. We demonstrate the practical feasibility of FREEDA through experiments on a variety of realistic Cloud-Edge infrastructural topologies and component architectures. Furthermore, we benchmark FREEDA against Zephyrus, a comparable tool employing the same underlying solving technology. Empirical results show that FREEDA achieves strong scalability across a broad spectrum of realistic configurations and consistently outperforms Zephyrus.

The Adaptable TeaStore has recently been proposed as a reference model for adaptable microservice architectures. It includes different configurations, as well as scenarios requiring to transition between them. We describe an implementation of the Adaptable TeaStore based on AIOCJ, a choreographic language that allows one to program multiparty systems that can adapt at runtime to different conditions. Following the choreographic tradition, AIOCJ ensures by-construction correctness of communications (e.g., no deadlocks) before, during, and after adaptation. Adaptation is dynamic, and the adaptation scenarios need to be fully specified only at runtime. Using AIOCJ to model the Adaptable TeaStore, we showcase the strengths of the approach and its current limitations, providing suggestions for future directions for refining the paradigm (and the AIOCJ language, in particular), to better align it with real-world Cloud architectures.

FunLess is a Function-as-a-Service (FaaS) platform tailored for private edge cloud systems. FunLess leverages WebAssembly as its runtime environment for performance, function isolation, and support for heterogeneous devices, crucial for extending the coverage of serverless computing to private edge cloud systems. We benchmark FunLess against three production-ready, widely adopted open-source FaaS platforms—OpenFaaS, Fission, and Knative—under different deployment scenarios, characterised by the presence/absence of constrained-resource devices (Raspberry Pi 3B+) and the (in)accessibility of container orchestration technologies—Kubernetes. Our results confirm that FunLess is a suitable solution for FaaS private edge cloud systems since it achieves performance comparable to the considered FaaS alternatives while it is the only fully-deployable alternative on constrained-resource devices.