The edge–cloud continuum combines heterogeneous resources, which are complex to manage. Serverless edge computing is a suitable candidate to manage the continuum by abstracting away the underlying infrastructure, improving developers’ experiences, and optimizing overall resource utilization. However, understanding and overcoming programming support, reliability, and performance engineering challenges are essential for the success of serverless edge computing. In this article, we review and evaluate the maturity of serverless approaches for the edge–cloud continuum. Our review includes commercial, community-driven offerings and approaches from academia. We identify several maturity levels of serverless edge computing and use them as criteria to evaluate the maturity of current state-of-the-art serverless approaches with a special focus on the programming, reliability, and performance challenges. Finally, we lay a road map toward the next generation of serverless edge computing systems.

Recent technological advances are creating possibilities for novel forms of interaction, collaboration and organization of labor in Smart Cities. In this paper we present a reward-driven, Blockchain-backed platform acting as the technological enabler for enactment of ad-hoc, decentralized neighborhood-scale co-creation, collaboration and citizen engagement activities.

Recently, emerging IoT cloud systems create numerous opportunities for a variety of stakeholders in terms of optimizing their existing business processes, as well as developing novel cross-organization and cross-domain applications. However, developers of such systems face a plethora of challenges, mainly due to complex dependencies between the application business logic and the underlying IoT cloud infrastructure, as well as difficulties to provision and govern vast, geographically distributed IoT cloud resources. In this paper, we introduce SDG-Pro – a novel programming framework for software-defined IoT cloud systems. The main features of our framework include programming abstractions: Software-Defined Gateways, Intents, Intent Scopes, and Data and Control Points, as well as provisioning and governance APIs that allow for programmatic management of software-defined gateways throughout their entire lifecycle. The SDG-Pro framework enables easier, efficient and more intuitive development of IoT cloud applications. It promotes the everything-as-code paradigm for IoT cloud applications in order to provide a uniform, programmatic view on the entire development process. To illustrate the feasibility of our framework to support development of IoT cloud applications, we evaluate it using a real-world case study on managing fleets of electric vehicles.

Serverless computing has ushered in a transformative paradigm, with a promise to alleviate developers from the intricacies of infrastructure management. However, current serverless platforms predominantly offer only serverless compute capabilities. As a consequence, the application developers are once again tasked to explicitly provision and manage the backend services (BaaS), such as object stores or API gateways, the infrastructure, and the configuration models. This violates the main promise of serverless computing and erases much of the practical benefits of the serverless paradigm. It also introduces the challenges of managing the application execution environment, which includes maintaining provisioning and deployment scripts, configuring and managing access permissions, and scaling the services during runtime. To address these challenges, in this paper we introduce a novel paradigm for the next generation of serverless computing, called self-provisioning infrastructure. The self-provisioning infrastructure is an infrastructure that is capable to automatically and autonomously (with zero-configuration and zero-touch) provision serverless functions, their infrastructure, and their supporting BaaS services. To achieve this vision, we introduce novel design principles, models, and mechanisms that are formalized vianovel programming, function, and system models. With this novel paradigm, we intend to fortify the core design principles of serverless computing but also extend themto the entire application execution environment. By doing so, we aim to enable the next-generation serverless computing in the Edge-Cloud continuum.

Serverless Computing brings advantages to the Edge-Cloud continuum, like simplified programming and infrastructure management. In composed workflows, where serverless functions need to exchange data constantly, serverless platforms rely on remote services such as object storage and key-value stores as a common approach to exchange data. In WebAssembly, functions leverage WebAssembly System Interface to connect to the network and exchange data via remote services. As a consequence, co-located serverless functions need remote services to exchange data, increasing latency and adding network overhead. To mitigate this problem, in this paper, we introduce CWASI: a WebAssembly OCI-compliant runtime shim that determines the best inter-function data exchange approach based on the serverless function locality. CWASI introduces a three-mode communication model for the Serverless Edge-Cloud continuum. This communication model enables CWASI Shim to optimize inter-function communication for co-located functions by leveraging the function host mechanisms. Experimental results show that CWASI reduces the communication latency between the co-located serverless functions by up to 95% and increases the communication throughput by up to 30x.

Serverless computing promises to be a cost effective form of on demand computing. To fully utilize its cost saving potential, workflows must be configured with the appropriate amount of resources to meet their response time Service Level Objective (SLO), while keeping costs at a minimum. Since determining and updating these configuration models manually is a nontrivial and error prone task, researchers have developed solutions for automatically finding configurations that meet the aforementioned requirements. However, our initial experiments show that even when following best practices and using state-of-the-art configuration tools, resources may still be considerably over- or underprovisioned, depending on the size of functions’ input payload. In this paper we present ChunkFunc, an SLO- and input data-aware framework for tuning serverless workflows. Our main contributions include: i) an SLO- and input size-aware function performance model for optimized configurations in serverless workflows, ii) ChunkFunc Profiler, an auto-tuned, Bayesian Optimization-guided profiling mechanism for profiling serverless functions with typical input data sizes to build a performance model, and iii) ChunkFunc Workflow Optimizer, which uses these models to determine an input size dependent configuration for each serverless function in a workflow to meet the SLO, while keeping costs to a minimum. We evaluate ChunkFunc on real-life serverless workflows and compare it to two state-of-the-art solutions, showing that it increases SLO adherence by a factor of 1.04 to 2.78, depending on the workflow, and reduces costs by up to 61% .