Reliable Transactions in Serverless-Edge Architecture

Gupta, Suyash; Rahnama, Sajjad; Linsenmayer, Erik; Nawab, Faisal; Sadoghi, Mohammad

doi:10.1109/icde55515.2023.00030

Cited by 4 publications

(1 citation statement)

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“…4) Hybrid Solutions: Several studies opt to mitigate the challenges of serverless by unifying serverless and serverful components [60]- [62]. Gupta et al [63] address the integration of edge devices with multi-cloud FaaS instances and secure data stores in a fault-tolerant manner. In contrast, our work addresses a fully serverless design objective.…”

Section: Related Work a Serverless Computingmentioning

confidence: 99%

FSD-Inference: Fully Serverless Distributed Inference with Scalable Cloud Communication

Oakley,

Ferhatosmanoglu

2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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Serverless computing offers attractive scalability, elasticity and cost-effectiveness. However, constraints on memory, CPU and function runtime have hindered its adoption for dataintensive applications and machine learning (ML) workloads. Traditional 'server-ful' platforms enable distributed computation via fast networks and well-established inter-process communication (IPC) mechanisms such as MPI and shared memory. In the absence of such solutions in the serverless domain, parallel computation with significant IPC requirements is challenging. We present FSD-Inference, the first fully serverless and highly scalable system for distributed ML inference. We explore potential communication channels, in conjunction with Function-as-a-Service (FaaS) compute, to design a state-of-the-art solution for distributed ML within the context of serverless data-intensive computing. We introduce novel fully serverless communication schemes for ML inference workloads, leveraging both cloudbased publish-subscribe/queueing and object storage offerings. We demonstrate how publish-subscribe/queueing services can be adapted for FaaS IPC with comparable performance to object storage, while offering significantly reduced cost at high parallelism levels. We conduct in-depth experiments on benchmark DNNs of various sizes. The results show that when compared to server-based alternatives, FSD-Inference is significantly more cost-effective and scalable, and can even achieve competitive performance against optimized HPC solutions. Experiments also confirm that our serverless solution can handle large distributed workloads and leverage high degrees of FaaS parallelism.

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Section: Related Work a Serverless Computingmentioning

confidence: 99%