The Function-as-a-Service (FaaS) paradigm has emerged as an evolution of traditional cloud computing services, promising easier development and operations, finer-grained pricing and seamless scalability. With the proliferation of computational capacity at the network edge and across the cloud-to-edge continuum, FaaS adoption has expanded beyond the borders of traditional cloud data centers. However, in such dynamic, distributed and heterogeneous environments, additional challenges arise, including how to deal with load peaks through computational offloading and how to execute functions in a carbon and energy-aware manner. In this paper, we tackle these challenges by introducing an approach to carbon- and Quality of Service (QoS)-aware function offloading based on spatial workload shifting and adaptive selection of function variants (i.e., multiple implementations trading off computational demand, accuracy and energy consumption). Our solution targets a FaaS system spanning the cloud-to-edge continuum hosting users belonging to multiple service classes with diverse QoS requirements. By solving a linear programming problem at run time, our approach determines how to allocate the available resources to optimize the trade-off between carbon emissions and QoS satisfaction. Extensive simulated experiments show that, under the same monetary budget, our approach allows 30% more requests to meet QoS requirements on average compared to a state-of-the-art baseline, with 20% less carbon emissions due to function execution.

Machine Learning (ML) emerged as a powerful tool for predicting task execution times across the variety of VM types offered by Infrastructure-as-a-Service (IaaS) clouds. However, training ML models to ensure accurate predictions can often become uneconomical for users due to the high costs—in terms of both time and money—for collecting samples, especially when an IaaS cloud offers a wide choice of VM types. This paper investigates a ML model bootstrapping technique that leverages analytical modeling to reduce the cost of collecting training samples while maintaining robust performance predictions. Complementarily, the technique can be used to improve the accuracy of ML models in the case of limited availability of training samples. Experimental results highlighted the potential of the proposed technique with various workloads and with a large set of VM types, paving the way for more cost-effective ML-based performance prediction in IaaS clouds.

Function-as-a-Service (FaaS) is a serverless computing model that enables applications to be composed of self-contained functions triggered by events. The edge-cloud con-tinuum extends this paradigm by allowing function deployment closer to users and loT devices, reducing communication latency. However, large-scale FaaS deployment at the edge demands energy-efficient resource management to ensure cost reduction and sustainability. To address this challenge, we present$E^{2}FIS$: Energy-Efficient Function Invocation Scheduling, a framework that optimizes resource management and minimizes energy consumption in edge FaaS platforms.$E^{2}FIS$formulates function scheduling as a Mixed Integer Linear Programming (MILP) problem, minimizing energy consumption by consolidating work-loads while ensuring execution deadlines are met. Through simu-lations with real-world traces and experiments on the Serverledge FaaS platform,$E^{2}FIS$demonstrates to outperform the Earliest Deadline First (EDF) baseline, reducing energy consumption up to 92 % while maintaining timely function execution.

Serverless computing and, in particular, Function-as-a-Service (FaaS) have emerged as valuable paradigms to deploy applications without the burden of managing the computing infrastructure. While initially limited to the execution of stateless functions in the cloud, serverless computing is steadily evolving. The paradigm has been increasingly adopted at the edge of the network to support latency-sensitive services. Moreover, it is not limited to stateless applications, with functions often recurring to external data stores to exchange partial computation outcomes or to persist their internal state. To the best of our knowledge, several policies to schedule function instances to distributed host have been proposed, but they do not explicitly model the data dependency of functions and its impact on performance.

Function-as-a-Service (FaaS) paradigm is increasingly attractive to bring the benefits of serverless computing to the edge of the network, besides traditional Cloud data centers. However, FaaS adoption in the emerging Cloud-to-Edge Continuum is challenging, mostly due to geographical distribution and heterogeneous resource availability. This emerging landscape calls for effective strategies to trade off low latency at the edge of the network with Cloud resource richness, taking into account the needs of different functions and users. In this paper, we present QoS-aware offloading policies for serverless functions running in the Cloud-to-Edge continuum. We consider heterogeneous functions and service classes, and aim to maximize utility given a monetary budget for resource usage. Specifically, we introduce a two-level approach, where (i) FaaS nodes rely on a randomized policy to schedule every incoming request according to a set of probability values, and (ii) periodically, a linear programming model is solved to determine the probabilities to use for scheduling. We show by extensive simulation that our approach outperforms alternative approaches in terms of generated utility across multiple scenarios. Moreover, we demonstrate that our solution is computationally efficient and can be adopted in large-scale systems. We also demonstrate the functionality of our approach through a proof-of-concept experiment on an open-source FaaS framework.

Function-as-a-Service (FaaS) has emerged as an evolution of traditional Cloud service models, allowing users to define and execute pieces of codes (i.e., functions) in a serverless manner, with the provider taking care of most operational issues. With the unending growth of resource availability in the Edge-to-Cloud Continuum, there is increasing interest in adopting FaaS near the Edge as well, to better support geo-distributed and pervasive applications. However, as the existing FaaS frameworks have mostly been designed with Cloud in mind, new architectures are necessary to cope with the additional challenges of the Continuum, such as higher heterogeneity, network latencies, limited computing capacity. In this paper, we present an extended version of Serverledge, a FaaS framework designed to span Edge and Cloud computing landscapes. Serverledge relies on a decentralized architecture, where each FaaS node is able to autonomously schedule and execute functions. To take advantage of the computational capacity of the infrastructure, Serverledge nodes also rely on horizontal and vertical function offloading mechanisms. In this work we particularly focus on the design of mechanisms for function offloading and live function migration across nodes. We implement these mechanisms in Serverledge and evaluate their impact and performance considering different scenarios and functions.

The Function-as-a-Service (FaaS) paradigm is increasingly adopted for the development of Cloud-native applications, which especially benefit from the seamless scalability and attractive pricing models of serverless deployments. With the continuous emergence of latency-sensitive applications and services, including Internet-of-Things and augmented reality, it is now natural to wonder whether and how the FaaS paradigm can be efficiently exploited in the Cloud-Edge Continuum, where serverless functions may benefit from reduced network delay between their invoking users and the FaaS platform. In this paper, we illustrate the key challenges that must be faced to effectively deploy serverless functions in the Cloud-Edge Continuum and review recent contributions proposed by the research community towards overcoming those challenges. We also discuss the key issues that currently remain unsolved and highlight a few research opportunities for better support of FaaS in the Compute Continuum.

As the Function-as-a-Service (FaaS) paradigm enjoys growing popularity within Cloud-based systems, there is increasing interest in moving serverless functions towards the Edge, to better support geo-distributed and pervasive applications. However, enjoying both the reduced latency of Edge and the scalability of FaaS requires new architectures and implementations to cope with typical Edge challenges (e.g., nodes with limited computational capacity). While first solutions have been proposed for Edge-based FaaS, including light function sandboxing techniques, we lack a platform with the ability to span both Edge and Cloud and adaptively exploit both. In this paper, we present Serverledge, a FaaS platform designed for the Edge-to-Cloud continuum. Serverledge adopts a decentralized architecture, where function invocation requests can be fully served within Edge nodes. To cope with load peaks, Serverledge also supports vertical (i.e., from Edge to Cloud) and horizontal (i.e., among Edge nodes) computation offloading. Our evaluation shows that Serverledge outperforms Apache OpenWhisk in an Edge-like scenario and has competitive performance with state-of-the-art frameworks optimized for the Edge, with the advantage of built-in support for vertical and horizontal offloading.

Data Stream Processing (DSP) applications analyze data flows in near real-time by means of operators, which process and transform incoming data. Operators handle high data rates running parallel replicas across multiple processors and hosts. To guarantee consistent performance without wasting resources in the face of variable workloads, auto-scaling techniques have been studied to adapt operator parallelism at run-time. However, most of the effort has been spent under the assumption of homogeneous computing infrastructures, neglecting the complexity of modern environments. We consider the problem of deciding both how many operator replicas should be executed and which types of computing nodes should be acquired. We devise heterogeneity-aware policies by means of a two-layered hierarchy of controllers. While application-level components steer the adaptation process for whole applications, aiming to guarantee user-specified requirements, lower-layer components control auto-scaling of single operators. We tackle the fundamental challenge of performance and workload uncertainty, exploiting Bayesian optimization (BO) and reinforcement learning (RL) to devise policies. The evaluation shows that our approach is able to meet users’ requirements in terms of response time and adaptation overhead, while minimizing the cost due to resource usage, outperforming state-of-the-art baselines. We also demonstrate how partial model information is exploited to reduce training time for learning-based controllers.

The unpredictable variability of Data Stream Processing (DSP) application workloads calls for advanced mechanisms and policies for elastically scaling the processing capacity of DSP operators. Whilst many different approaches have been used to devise policies, most of the solutions have focused on data arrival rate and operator resource utilization as key metrics for auto-scaling. We here show that, under burstiness in the data flows, overly simple characterizations of the input stream can yet lead to very inaccurate performance estimations that affect such policies, resulting in sub-optimal resource allocation.We then present MEAD, a vertical auto-scaling solution that relies on online state-based representation of burstiness to drive resource allocation. We use in particular Markovian Arrival Processes (MAPs), which are composable with analytical queueing models, allowing us to efficiently predict performance at run-time under burstiness. We integrate MEAD in Apache Flink, and evaluate its benefits over simpler yet popular auto-scaling solutions, using both synthetic and real-world workloads. Differently from existing approaches, MEAD satisfies response time requirements under burstiness, while saving up to 50% CPU resources with respect to a static allocation.

Summary Processing data in a timely manner, data stream processing (DSP) applications are receiving an increasing interest for building new pervasive services. Due to the unpredictability of data sources, these applications often operate in dynamic environments; therefore, they require the ability to elastically scale in response to workload variations. In this paper, we deal with a key problem for the effective runtime management of a DSP application in geo‐distributed environments: We investigate the placement and replication decisions while considering the application and resource heterogeneity and the migration overhead, so to select the optimal adaptation strategy that can minimize migration costs while satisfying the application quality of service (QoS) requirements. We present elastic DSP replication and placement (EDRP), a unified framework for the QoS‐aware initial deployment and runtime elasticity management of DSP applications. In EDRP, the deployment and runtime decisions are driven by the solution of a suitable integer linear programming problem, whose objective function captures the relative importance between QoS goals and reconfiguration costs. We also present the implementation of EDRP and the related mechanisms on Apache Storm. We conduct a thorough experimental evaluation, both numerical and prototype‐based, that shows the benefits achieved by EDRP on the application performance.