{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:30:59Z","timestamp":1772548259729,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Nowadays, the emergence of Foundation Models as a Service enables mobile users to access powerful capabilities such as inference and fine-tuning on demand and without incurring local computational overhead. This paper introduces a software-aware offloading framework for FMaaS that allows edge nodes to forecast software aging and prevent service degradation. Each node employs a lightweight Echo State Network to predict its software age, with tasks dynamically assigned based on communication cost, inference delay, and forecast reliability. Simulation results including ablation studies confirm the effectiveness of software age forecasting in reducing task failures and improving session continuity.<\/jats:p>","DOI":"10.3390\/computers15030158","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T09:45:12Z","timestamp":1772531112000},"page":"158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Staying Young at the Edge: A Software Aging Perspective for Foundation Models as a Service"],"prefix":"10.3390","volume":"15","author":[{"given":"Benedetta","family":"Picano","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5934-3321","authenticated-orcid":false,"given":"Romano","family":"Fantacci","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1109\/LWC.2024.3520995","article-title":"Generative ai as a service in 6g edge-cloud: Generation task offloading by in-context learning","volume":"14","author":"Zhou","year":"2025","journal-title":"IEEE Wirel. 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