Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services

Aral, Atakan; Brandić, Ivona

doi:10.1109/tpds.2020.3046188

Cited by 38 publications

(16 citation statements)

References 53 publications

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“…A machine learning based approach is proposed that learns the spatio-temporal dependencies between edge server failures and combines them with the topological information to incorporate link failures [34]. Reliable services in an edge environment are considered by using a fault tolerance protocol taking into account the characteristics of the execution environment for stateful IoT applications [35].…”

Section: B Resultsmentioning

confidence: 99%

NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

Majeed¹,

Kilpatrick²,

Spence³

et al. 2021

Preprint

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Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational conditions such as network speed. When operational conditions change DNNs will need to be repartitioned to maintain the overall performance. However, repartitioning using existing approaches, such as Pause and Resume, will incur a service downtime on the edge. This paper presents the NEUKONFIG framework that identifies the service downtime incurred when repartitioning DNNs and proposes approaches for reducing edge service downtime. The proposed approaches are based on 'Dynamic Switching' in which, when the network speed changes and given an existing edge-cloud pipeline, a new edge-cloud pipeline is initialised with new DNN partitions. Incoming inference requests are switched to the new pipeline for processing data. Two dynamic switching scenarios are considered: when a second edgecloud pipeline is always running and when a second pipeline is only initialised when the network speed changes. Experimental studies are carried out on a lab-based testbed to demonstrate that Dynamic Switching reduces the downtime by at least an order of magnitude when compared to a baseline using Pause and Resume that has a downtime of 6 seconds. A trade-off in the edge service downtime and memory required is noted. The Dynamic Switching approach that requires the same amount of memory as the baseline reduces the edge service downtime to 0.6 seconds and to less than 1 millisecond in the best case when twice the amount of memory as the baseline is available.

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Section: B Resultsmentioning

confidence: 99%

NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

Majeed¹,

Kilpatrick²,

Spence³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To solve the ADR model (27), we first need to convert each robust constraint into a solvable form. Specifically, we employ LP duality to reformulate each robust constraint into an equivalent set of linear equations.…”

Section: Affine Decision Rule (Adr) Approachmentioning

confidence: 99%

“…In [27], the authors evaluate the failure resilience of a service deployed redundantly on the edge infrastructure by learning the spatio-temporal dependencies between edge server failures and exploiting the topological information to incorporate link failures. Then, they propose a dependencyand topology-aware failure resilience algorithm to minimize either redundancy cost or failure probability, while maintaining low network latency.…”

Section: Related Workmentioning

confidence: 99%

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Resilient Edge Service Placement under Demand and Node Failure Uncertainties

Cheng¹,

Nguyen²,

Bhargava³

2021

Preprint

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In this paper, we study an optimal service placement and workload allocation problem for a service provider (SP), who can procure resources from numerous edge nodes to serve its users. The SP aims to improve the user experience while minimizing its cost, considering various system uncertainties. To tackle this challenging problem, we propose a novel resilience-aware edge service placement and workload allocation model that jointly captures the uncertainties of resource demand and node failures. The first-stage decisions include the optimal service placement and resource procurement, while the optimal workload allocation is determined in the second stage after the uncertainties are disclosed. The salient feature of the proposed model is that it produces a placement and procurement solution that is robust against any possible realization of the uncertainties. By leveraging the column-and-constraint generation method, we introduce two iterative algorithms that can converge to an exact optimal solution within a finite number of iterations. We further suggest an affine decision rule approximation approach for solving large-scale problem instances in a reasonable time. Extensive numerical results are shown to demonstrate the advantages of the proposed model and solutions.

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“…Although several IoT applications and use cases already exist in research (e.g., [1,12,17,26,29]), none of them could directly be used or adapted as a FaaS application. Thus, we designed our benchmark application around typical IoT patterns and implemented a use case based on a smart traffic control scenario, mostly inspired by [1,12], and TU Vienna's InTraSafEd5G project 7 .…”

Section: Iot Application (Smart Traffic Light)mentioning

confidence: 99%

BeFaaS: An Application-Centric Benchmarking Framework for FaaS Platforms

Grambow¹,

Pfandzelter²,

Burchard³

et al. 2021

Preprint

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Following the increasing interest and adoption of FaaS systems, benchmarking frameworks for determining non-functional properties have also emerged. While existing (microbenchmark) frameworks only evaluate single aspects of FaaS platforms, a more holistic, application-driven approach is still missing.In this paper, we design and present BeFaaS, an application-centric benchmarking framework for FaaS environments that focuses on the evaluation with realistic and typical use cases for FaaS applications. BeFaaS comes with two built-in benchmarks (an e-commerce and an IoT application), is extensible for new workload profiles and new platforms, supports federated benchmark runs in which the benchmark application is distributed over multiple providers, and supports a fine-grained result analysis.Our evaluation compares three major FaaS providers in single cloud provider setups and analyzes the traces of a federated fog setup. It shows that BeFaaS is capable of running each benchmark automatically with minimal configuration effort and providing detailed insights for each interaction.

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Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services

Cited by 38 publications

References 53 publications

NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

Resilient Edge Service Placement under Demand and Node Failure Uncertainties

BeFaaS: An Application-Centric Benchmarking Framework for FaaS Platforms

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