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Functions-as-a-Service (FaaS) is a Serverless Cloud paradigm where a platform manages the scheduling (e.g., resource allocation, runtime environments) of stateless functions. Recent developments show the benefits of using domain-specific languages to express per-function policies, e.g., policies can enforce the allocation of functions on nodes that enjoy lower data-access latencies thanks to proximity and connection pooling. Here, we focus on affinity-aware scenarios, i.e., where, for performance and functional requirements, the allocation of a function depends on the presence/absence of other functions on nodes. We first present aAPP, an affinity-aware extension of a declarative, platform-agnostic language for defining custom function scheduling policies. We implement a prototype supporting this scheduling language by extending the popular Apache OpenWhisk FaaS platform and show that using aAPP in affinity-aware scenarios leads to an appreciable reduction in latency without noticeable overhead for scenarios without affinity constraints., Comment: 16, 6 figures, 1 listing. arXiv admin note: substantial text overlap with arXiv:2407.14159

Functions-as-a-Service (FaaS) is a Serverless Cloud paradigm where a platform manages the execution scheduling (e.g., resource allocation, runtime environments) of stateless functions. Recent developments demonstrate the benefits of using domain-specific languages to express per-function scheduling policies, e.g., enforcing the allocation of functions on nodes that enjoy low data-access latencies thanks to proximity and connection pooling. We present aAPP, an affinity-aware extension of a platform-agnostic function scheduling language. We formalise its scheduling semantics and then study the complexity of statically checking reachability properties, e.g., useful to verify that trusted and untrusted functions cannot be co-located. Analysing different fragments of aAPP, we show that checking reachability of policies without affinity has linear complexity, while affinity makes the problem PSpace., Comment: 26 pages, 3 figures, 2 listing, appendix

Choreographic Programming (CP) is a language paradigm whereby software artefacts, called choreographies, specify the behaviour of communicating participants. CP is famous for its correctness-by-construction approach to the development of concurrent, distributed systems. In this paper, we illustrate FaaSChal, a proposal for a CP language tailored for the case of serverless Function-as-a-Service (FaaS). In FaaS, developers define a distributed architecture as a collection of stateless functions, leaving to the serverless platform the management of deployment and scaling. We provide a first account of a CP language tailored for the FaaS case via examples that present some of its relevant features, including projection. In addition, we showcase a novel application of CP. We use the choreography as a source to extract information on the infrastructural relations among functions so that we can synthesise policies that strive to minimise their latency while guaranteeing the respect of user-defined constraints.

We present FunLess, a Function-as-a-Service (FaaS) platform tailored for the private edge cloud system. FunLess responds to recent trends that advocate for extending the coverage of serverless computing to private edge cloud systems and enhancing latency, security, and privacy while improving resource usage. Unlike existing solutions that rely on containers for function invocation, FunLess leverages WebAssembly (Wasm) as its runtime environment. Wasm's lightweight, sandboxed runtime is crucial to have functions run on constrained devices at the edge. Moreover, the advantages of using Wasm in FunLess include a consistent development and deployment environment for users and function portability (write once, run everywhere) We validate FunLess 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. We compare FunLess with three production-ready, widely adopted open-source FaaS platforms - OpenFaaS, Fission, and Knative. Our benchmarks confirm that FunLess is a proper 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, thanks to its small memory footprint.

Current proprietary and open-source serverless platforms follow opinionated, hardcoded scheduling policies to deploy the functions to be executed over the available workers. Such policies may decrease the performance and the security of the application due to locality issues (e.g., functions executed by workers far from the databases to be accessed). These limitations are partially overcome by the adoption of APP, a new platform-agnostic declarative language that allows serverless platforms to support multiple scheduling logics. Defining the "right" scheduling policy in APP is far from being a trivial task since it often requires rounds of refinement involving knowledge of the underlying infrastructure, guesswork, and empirical testing. In this paper, we start investigating how information derived from static analysis could be incorporated into APP scheduling function policies to help users select the best-performing workers at function allocation. We substantiate our proposal by presenting a pipeline able to extract cost equations from functions' code, synthesising cost expressions through the usage of off-the-shelf solvers, and extending APP allocation policies to consider this information., Comment: In Proceedings TiCSA 2023, arXiv:2310.18720

Radiotherapy with heavy ions, in particular, 12C beams, is one of the most advanced forms of cancer treatment. Sharp dose gradients and high biological effectiveness in the target region make them an ideal tool to treat deep-seated and radioresistant tumors, however, at the same time, sensitive to small errors in the range prediction. Safety margins are added to the tumor volume to mitigate these uncertainties and ensure its uniform coverage, but during the irradiation they lead to unavoidable damage to the surrounding healthy tissue. To fully exploit the benefits of a sharp Bragg peak, a large effort is put into establishing precise range verification methods for the so-called image-guided radiotherapy. Despite positron emission tomography being widely in use for this purpose in 12C ion therapy, the low count rates, biological washout, and broad shape of the activity distribution still limit its precision to a few millimeters. Instead, radioactive beams used directly for treatment would yield an improved signal and a closer match with the dose fall-off, potentially enabling precise in vivo beam range monitoring. We have performed a treatment planning study to estimate the possible impact of the reduced range uncertainties, enabled by radioactive 11C beams treatments, on sparing critical organs in the tumor proximity. We demonstrate that (i) annihilation maps for 11C ions can in principle reflect even millimeter shifts in dose distributions in the patient, (ii) outcomes of treatment planning with 11C beams are significantly improved in terms of meeting the constraints for the organs at risk compared to 12C plans, and (iii) less severe toxicities for serial and parallel critical organs can be expected following 11C treatment with reduced range uncertainties, compared to 12C treatments.

Cloud-edge serverless applications or serverless deployments spanning multiple regions introduce the need to govern the scheduling of functions to satisfy their functional constraints or avoid performance degradation. For instance, functions may require to be allocated to specific private (edge) nodes that have access to specialised resources or to nodes with low latency to access a certain database to decrease the overall latency of the application. State-of-the-art serverless platforms do not support directly the implementation of topological constraints on the scheduling of functions. We address this problem by presenting a declarative language for defining topology-aware, function-specific serverless scheduling policies, called tAPP. Given a tAPP script, a compatible serverless scheduler can enforce different, co-existing topological constraints without requiring ad-hoc platform deployments. We prove our approach feasible by implementing a tAPP-based serverless platform as an extension of the Apache OpenWhisk serverless platform. We show that, compared to vanilla OpenWhisk, our extension does not negatively impact the performance of generic, non-topology-bound serverless scenarios, while it increases the performance of topology-bound ones.