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Many IoT applications have increasingly adopted machine learning (ML) techniques, such as classification and detection, to enhance automation and decision-making processes. With advances in hardware accelerators such as Nvidia’s Jetson embedded GPUs, the computational capabilities of end devices, particularly for ML inference workloads, have significantly improved in recent years. These advances have opened opportunities for distributing computation across the edge network, enabling optimal resource utilization and reducing request latency. Previous research has demonstrated promising results in collaborative inference, where processing units in the edge network, such as end devices and edge servers, collaboratively execute an inference request to minimize latency.This paper explores approaches for implementing collaborative inference on a single model in resource-constrained edge networks, including on-device, device-edge, and edge-edge collaboration. We present preliminary results from proof-of-concept experiments to support each case. We discuss dynamic factors that can impact the performance of these inference execution strategies, such as network variability, thermal constraints, and workload fluctuations. Finally, we outline potential directions for future research.
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Enhancing Resilience in Distributed ML Inference Pipelines for Edge Computing
As edge computing and sensing devices continue to proliferate, distributed machine learning (ML) inference pipelines are becoming popular for enabling low-latency, real-time decision-making at scale. However, the geographically dispersed and often resource-constrained nature of edge devices makes them susceptible to various failures, such as hardware malfunctions, network disruptions, and device overloading. These edge failures can significantly affect the performance and availability of inference pipelines and the sensing-to-decision-making loops they enable. In addition, the complexity of task dependencies amplifies the difficulty of maintaining performant and reliable ML operations. To address these challenges and minimize the impact of edge failures on inference pipelines, this paper presents several fault-tolerant approaches, including sensing redundancy, structural resilience, failover replication, and pipeline reconfiguration. For each approach, we explain the key techniques and highlight their effectiveness and tradeoffs. Finally, we discuss the challenges associated with these approaches and outline future directions.
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- Award ID(s):
- 2325956
- PAR ID:
- 10591318
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7423-0
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Washington, DC, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MILCOM61039.2024.10773652
