Serverless computing offers elastic scaling and pay-per-use execution, making it well-suited for AI workloads. As these workloads run in heterogeneous environments such as the Edge-Cloud-Space 3D Continuum, they often require intensive parallel computation, which GPUs can perform far more efficiently than CPUs. However, current platforms struggle to manage hardware acceleration effectively, as static user-device assignments fail to ensure SLO compliance under varying loads or placements, and one-time dynamic selections often lead to suboptimal or cost-inefficient configurations.

Serverless computing provides infrastructure management and elastic auto-scaling, therefore reducing operational overhead. By design serverless functions are stateless, which means they typically leverage external remote services to store and exchange data. Transferring data over a network typically involves serialization and deserialization. These operations usually require multiple data copies and transitions between user and kernel space, resulting in overhead from context switching and memory allocation, contributing significantly to increased latency and resource consumption.

Due to the high scalability, infrastructure management, and pay-per-use pricing model, serverless computing has been adopted in a wide range of applications such as real-time data processing, IoT, and AI-related workflows. However, deploying serverless functions across dynamic and heterogeneous environments such as the 3D (Edge-Cloud-Space) Continuum introduces additional complexity. Each layer of the 3D Continuum shows different performance capabilities and costs according to workload characteristics. Cloud services alone often show significant differences in performance and pricing for similar functions, further complicating cost management. Additionally, serverless workflows consist of functions with diverse character-istics, requiring a granular understanding of performance and cost trade-offs across different infrastructure layers to be able to address them individually. In this paper, we present Cosmos, a cost- and a performance-cost-tradeoff model for serverless workflows that identifies key factors that affect cost changes across different workloads and cloud providers. We present a case study analyzing the main drivers that influence the costs of serverless workflows. We demonstrate how to classify the costs of serverless workflows in leading cloud providers AWS and GCP. Our results show that for data-intensive functions, data transfer and state management costs contribute to up to 75% of the costs in AWS and 52% in GCP. For compute-intensive functions such as AI inference, the cost results show that BaaS services are the largest cost driver, reaching up to 83 % in AWS and 97 % in GCP.

Serverless computing allows for dynamic and flexible execution of FaaS functions while simplifying infrastructure management. Typically, serverless functions rely on remote storage services for managing state, which can result in increased latency and network communication overhead. In a dynamic environment such as the 3D (Edge-Cloud-Space) Compute Continuum, serverless functions face additional challenges due to frequent changes in network topology. As satellites move in and out of the range of ground stations, functions must make multiple hops to access cloud services, leading to high-latency state access and unnecessary data transfers. In this paper, we present Databelt, a state management framework for serverless workflows designed for the dynamic environment of the 3D Compute Continuum. Databelt introduces an SLO-aware state propagation mechanism that enables the function state to move continuously in orbit. Databelt proactively offloads function states to the most suitable node, such that when functions execute, the data is already present on the execution node or nearby, thus minimizing state access latency and reducing the number of network hops. Additionally, Databelt introduces a function state fusion mechanism that abstracts state management for functions sharing the same serverless runtime. When functions are fused, Databelt seamlessly retrieves their state as a group, reducing redundant network and storage operations and improving overall workflow efficiency. Our experimental results show that Databelt reduces workflow execution time by up to 66% and increases throughput by 50% compared to the baselines. Furthermore, our results show that Databelt function state fusion reduces storage operations latency by up to 20%, by reducing repetitive storage requests for functions within the same runtime, ensuring efficient execution of serverless workflows in highly dynamic network environments such as the 3D Continuum.

Serverless computing has emerged as a flexible model for deploying applications in multi-tenant environments, where small, isolated functions often share the same host and compete for local resources. This co-location can lead to resource contention, making performance isolation a fundamental challenge, particularly given the variability of workloads, infrastructure, and fine-grained resource sharing. Although existing surveys address performance isolation in cloud systems, they do not account for the unique characteristics of serverless computing, such as cold starts, fine-grained scaling, and function-level isolation. Therefore, in this paper, we provide insights into state-of-the-art methods that deal with the challenges of performance isolation for serverless functions. The selected approaches are evaluated based on multiple criteria, including the technique used to achieve isolation, the virtualization level at which isolation is enforced, the decision-making approach, and the primary isolation technique. We analyze and classify existing performance isolation techniques, organizing them into runtime, provisioning, and hybrid approaches. Building on this classification, we outline performance and reliability engineering mechanisms applicable to serverless computing that isolate functions while addressing serverless-specific challenges. Our findings show that i) Isolation is often treated as a secondary goal, primarily to reduce latency or SLO violations, rather than a primary objective. ii) Existing solutions frequently focus on CPU or memory contention while overlooking other critical shared components, such as the network, and iii) offer limited ways to tune the trade-off between isolation and performance, iv) unpredictability of serverless functions is the main challenge to performance isolation, v) novel metrics are required to monitor and quantify performance interference. Addressing these gaps will require more comprehensive and adaptive hybrids that unify multiple aspects of performance isolation. By consolidating and structuring these techniques in the context of serverless computing, this paper lays the foundation for developing resilient, efficient, and interference-tolerant serverless platforms.

The number of Low Earth Orbit (LEO) satellites has grown enormously in the past years. Their abundance and low orbits allow for low latency communication with a satellite almost anywhere on Earth, and high-speed inter-satellite laser links (ISLs) enable a quick exchange of large amounts of data among satellites. As the computational capabilities of LEO satellites grow, they are becoming eligible as general-purpose compute nodes. In the 3D continuum, which combines Cloud and Edge nodes on Earth and satellites in space into a seamless computing fabric, workloads can be executed on any of the aforementioned compute nodes, depending on where it is most beneficial. However, scheduling on LEO satellites moving at approx. 27,000 km/h requires picking the satellite with the lowest latency to all data sources (ground and, possibly, earth observation satellites). Dissipating heat from onboard hardware is challenging when facing the sun and workloads must not drain the satellite's batteries. These factors make meeting SLOs more challenging than in the Edge-Cloud continuum, i.e., on Earth alone. We present HyperDrive, an SLOaware scheduler for serverless functions specifically designed for the 3D continuum. It places functions on Cloud, Edge, or Space compute nodes, based on their availability and ability to meet the SLO requirements of the workflow. We evaluate HyperDrive using a wildfire disaster response use case with high Earth Observation data processing requirements and stringent SLOs, showing that it enables the design and execution of such next-generation 3D scenarios with 71% lower network latency than the best baseline scheduler.

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.