Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services
Open accessAbstract
Edge computing services are exposed to infrastructural failures due to geographical dispersion, ad hoc deployment, and rudimentary support systems. Two unique characteristics of the edge computing paradigm necessitate a novel failure resilience approach. First, edge servers, contrary to cloud counterparts with reliable data center networks, are typically connected via ad hoc networks. Thus, link failures need more attention to ensure truly resilient services. Second, network delay is a critical factor for the deployment of edge computing services. This restricts replication decisions to geographical proximity and necessitates joint consideration of delay and resilience. In this article, we propose a novel machine learning based mechanism that evaluates the failure resilience of a service deployed redundantly on the edge infrastructure. Our approach learns the spatiotemporal dependencies between edge server failures and combines them with the topological information to incorporate link failures. Ultimately, we infer the probability that a certain set of servers fails or disconnects concurrently during service runtime. Furthermore, we introduce Dependency- and Topology-aware Failure Resilience (DTFR), a two-stage scheduler that minimizes either failure probability or redundancy cost, while maintaining low network delay. Extensive evaluation with various real-world failure traces and workload configurations demonstrate superior performance in terms of availability, number of failures, network delay, and cost with respect to the state-of-the-art schedulers.
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ACR [7], DTFR [19], and PureEdge [20] rely primarily on simulation-based validation.
“ACR [7], DTFR [19], and PureEdge [20], rely primarily on simulation-based validation”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [19]
DTFR reports inference latencies in the range of 0.5-1.6 ms.
“DTFR [19] reports inference latencies in the range of 0.5–1.6 ms”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [19]
DTFR models availability-delay trade-offs.
“DTFR [19] models availability–delay trade-offs”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [19]
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- Atakan Aral (Member, IEEE) received the first MSc degree in computer science and engineering from Politecnico di Milano, in 2011, and second MSc degree in computer science and engineering from Istanbul Technical University (ITU), in 2012, and the PhD degree in computer engineering from Istanbul Technical University, in 2016. He is a postdoctoral research fellow with the Vienna University of Technology. His research interests include resource management for geo-distributed and virtualized computing systems such as inter- cloud and edge computing, as well as the optimization of edge comput- ing architecture for AI services.
- Ivona Brandi c (Member, IEEE) received the PhD degree from the Vienna University of Tech- nology, in 2007. She is a professor with the Vienna University of Technology. In 2015, she was awarded FWF START prize, the highest Austrian Award for early career researchers. In 2011, she received the Distinguished Young Sci- entist Award from the Vienna University of Tech- nology for her project on the Holistic Energy Efficient Hybrid Clouds. Her main research inter- ests include cloud computing, large scale distrib- uted systems, energy efficiency, quality of service, and autonomic computing.
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Sustainable and Trustworthy Edge Machine Learning
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Proactive SLA-aware Application Placement in the Computing Continuum
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ChainNet: A Customized Graph Neural Network Model for Loss-Aware Edge AI Service Deployment
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Fault Tolerance Infrastructure for Mission-Critical Mobile Edge Cloud Applications
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ACRE: Actor Critic Reinforcement Learning for Failure-Aware Edge Computing Migrations
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2021 Index IEEE Transactions on Parallel and Distributed Systems Vol. 32
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- Authors
- Aral, A., & Brandic, I.
- Affiliations
- TU Wien (AT)
- Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria
- Venue
- IEEE Transactions on Parallel and Distributed Systems
- Published
- 2021-07-01
- Volume
- 32
- Issue
- 7
- Pages
- 1578-1590
- ISSN
- 1045-9219 (ISSN-L)
- 1558-2183
- 2161-9883
- OpenAlex
- W3114550872
- Cited by
- 68
- References
- 60
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 12 · 2023: 16 · 2022: 8 · 2021: 8
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Parallel and Distributed Systems (publishedVersion, cc-by)
- Funders
- Rucon project
- Austrian Science Fund
- City of Vienna
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- edge computing, failure resilience, link failure, ad hoc networks, network latency, topology-aware scheduling, service availability
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