arXiv subjects

Stefan Nastic

Publications and source records attributed to Stefan Nastic.

25 records · Page 2Linked to original sources

arXiv2025

CWASI: A WebAssembly Runtime Shim for Inter-function Communication in the Serverless 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.

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arXiv2025

Formal and Empirical Study of Metadata-Based Profiling for Resource Management in the Computing Continuum

We present and formalize a general approach for profiling workload by leveraging only a priori available static metadata to supply appropriate resource needs. Understanding the requirements and characteristics of a workload's runtime is essential. Profiles are essential for the platform (or infrastructure) provider because they want to ensure that Service Level Agreements and their objectives (SLOs) are fulfilled and, at the same time, avoid allocating too many resources to the workload. When the infrastructure to manage is the computing continuum (i.e., from IoT to Edge to Cloud nodes), there is a big problem of placement and tradeoff or distribution and performance. Still, existing techniques either rely on static predictions or runtime profiling, which are proven to deliver poor performance in runtime environments or require laborious mechanisms to produce fast and reliable evaluations. We want to propose a new approach for it. Our profile combines the information from past execution traces with the related workload metadata, equipping an infrastructure orchestrator with a fast and precise association of newly submitted workloads. We differentiate from previous works because we extract the profile group metadata saliency from the groups generated by grouping similar runtime behavior. We first formalize its functioning and its main components. Subsequently, we implement and empirically analyze our proposed technique on two public data sources: Alibaba cloud machine learning workloads and Google cluster data. Despite relying on partially anonymized or obscured information, the approach provides accurate estimates of workload runtime behavior in real-time.

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arXiv2025

FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting

Privacy-preserving distributed model training is crucial for modern machine learning applications, yet existing Federated Learning approaches struggle with heterogeneous data distributions and varying computational capabilities. Traditional solutions either treat all participants uniformly or require costly dynamic clustering during training, leading to reduced efficiency and delayed model specialization. We present FedCCL (Federated Clustered Continual Learning), a framework specifically designed for environments with static organizational characteristics but dynamic client availability. By combining static pre-training clustering with an adapted asynchronous FedAvg algorithm, FedCCL enables new clients to immediately profit from specialized models without prior exposure to their data distribution, while maintaining reduced coordination overhead and resilience to client disconnections. Our approach implements an asynchronous Federated Learning protocol with a three-tier model topology - global, cluster-specific, and local models - that efficiently manages knowledge sharing across heterogeneous participants. Evaluation using photovoltaic installations across central Europe demonstrates that FedCCL's location-based clustering achieves an energy prediction error of 3.93% (+-0.21%), while maintaining data privacy and showing that the framework maintains stability for population-independent deployments, with 0.14 percentage point degradation in performance for new installations. The results demonstrate that FedCCL offers an effective framework for privacy-preserving distributed learning, maintaining high accuracy and adaptability even with dynamic participant populations.

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arXiv2024

GoldFish: Serverless Actors with Short-Term Memory State for the Edge-Cloud Continuum

Serverless Computing is a computing paradigm that provides efficient infrastructure management and elastic scalability. Serverless functions scale up or down based on demand, which means that functions are not directly addressable and rely on platform-managed invocation. Serverless stateless nature requires functions to leverage external services, such as object storage and KVS, to exchange data. Serverless actors have emerged as a solution to these issues. However, the state-of-the-art serverless lifecycle and event-trigger invocation force actors to leverage remote services to manage their state and exchange data, which impacts the performance and incurs additional costs and dependency on third-party services. To address these issues, in this paper, we introduce a novel serverless lifecycle model that allows short-term stateful actors, enabling actors to maintain their state between executions. Additionally, we propose a novel serverless Invocation Model that enables serverless actors to influence the processing of future messages. We present GoldFish, a lightweight WebAssembly short-term stateful serverless actor platform that provides a novel serverless actor lifecycle and invocation model. GoldFish leverages WebAssembly to provide the actors with lightweight sandbox isolation, making them suitable for the Edge-Cloud Continuum, where computational resources are limited. Experimental results show that GoldFish optimizes the data exchange latency by up to 92% and increases the throughput by up to 10x compared to OpenFaaS and Spin.

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arXiv2024

Truffle: Efficient Data Passing for Data-Intensive Serverless Workflows in the Edge-Cloud Continuum

Serverless computing promises a scalable, reliable, and cost-effective solution for running data-intensive applications and workflows in the heterogeneous and limited-resource environment of the Edge-Cloud Continuum. However, building and running data-intensive serverless workflows also brings new challenges that can significantly degrade the application performance. Cold start remains one of the main challenges that impact the total function execution time. Further, since the serverless functions are not directly addressable, Serverless workflows need to rely on external (storage) services to pass the input data to the downstream functions. Empirical evidence from our experiments shows that the cold start and the function data passing take up the most time in the function execution lifecycle. In this paper, we introduce Truffle - a novel model and architecture that enables efficient inter-function data passing in the Edge-Cloud Continuum by introducing mechanisms that separate computation and I/O, allowing serverless functions to leverage the cold starts to their advantage. Truffle introduces Smart Data Prefetch (SDP) mechanism that abstracts the retrieval of input data for the serverless functions by triggering the data retrieval from the external storage during the function's startup. Truffle's Cold Start Pass (CSP) mechanism optimizes inter-function data passing and data exchange within serverless workflows in the Edge-Cloud Continuum by hooking into the functions' scheduling lifecycle to trigger early data passing during the function's cold start. Experimental results show that by leveraging the data prefetching and cold-start data passing, Truffle reduces the IO latency impact on the total function execution time by up to 77%, improving the function execution time by up to 46% compared to the state-of-the-art data passing approaches.

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arXiv2024

Towards Adaptive Asynchronous Federated Learning for Human Activity Recognition

In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized Machine Learning. Solving this issue is very important in IoT scenarios, where data coming from various sources, collected by heterogeneous devices, serve the learning of a distributed ML model through Federated Learning (FL). Specifically, we focus on the combination of FL applied to Human Activity Recognition HAR), where the task is to detect which kind of movements or actions individuals perform. In this case, transitioning from centralized learning (CL) to federated learning is non-trivial as HAR displays heterogeneity in action and devices, leading to significant skews in label and feature distributions. We address this scenario by presenting concrete solutions and tools for transitioning from centralized to FL for non-IID scenarios, outlining the main design decisions that need to be taken. Leveraging an open-sourced HAR dataset, we experimentally evaluate the effects that data augmentation, scaling, optimizer, learning rate, and batch size choices have on the performance of resulting machine learning models. Some of our main findings include using SGD-m as an optimizer, global feature scaling across clients, and persistent feature skew in the presence of heterogeneous HAR data. Finally, we provide an open-source extension of the Flower framework that enables asynchronous FL.

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arXiv2024

HyperDrive: Scheduling Serverless Functions in the Edge-Cloud-Space 3D Continuum

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 SLO-aware 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.

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