Towards a Computing Platform for the LEO Edge

Pfandzelter, Tobias; Hasenburg, Jonathan; Bermbach, David

doi:10.1145/3434770.3459736

Cited by 27 publications

(11 citation statements)

References 20 publications

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“…Bhosale et al [12] designed a loosely coupled orchestration tool for joint path prediction, satellite selection, and task hand-off. Pfandzelter et al [13] analyzed the applicability of different application organization paradigms of the virtual machine, container, and serverless function according to the characteristics and requirements of satellite edge computing. Based on the previous work, they put forth the design principle for satellite simulation tools and designed a satellite edge computing testbed based on microVM [14].…”

Section: Introductionmentioning

confidence: 99%

“…Pfandzelter et al [13] analyzed the applicability of different application organization paradigms of the virtual machine, container, and serverless function according to the characteristics and requirements of satellite edge computing. Based on the previous work, they put forth the design principle for satellite simulation tools and designed a satellite edge computing testbed based on microVM [14]. Besides, the existing work mainly focuses on resource allocation [15], computing offloading [16], [17], service placement [18], etc., which contribute to the improvement of satellite computing ecology from the theoretical perspective.…”

Section: Introductionmentioning

confidence: 99%

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Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogenous oscillators on a complex network in order for improving the synchronization performance. Whereas methods have been proposed to address this question in literature, the methods are based on accurate models describing the system dynamics, which, however, are normally unavailable in realistic situations. Here we show that this question can be addressed by the model-free technique of feed-forward neural network (FNN) in machine learning. Specifically, we measure the synchronization performance of a number of allocation schemes and use the measured data to train a machine. It is found that the trained machine is able to not only infer the synchronization performance of any new allocation scheme, but also find from a huge amount of candidates the optimal allocation scheme for synchronization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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“…Thus, a benchmarking framework must also consider these links and support external services. Furthermore, applications today are often distributed over cloud, edge, and fog resources, possibly even to the LEO edge [45,46,47]. Here, for example, edge servers can keep sensitive functions on premises while noncritical functions are hosted in a public cloud; similar setups exist for edge and fog computing use cases [48,49,50,9].…”

Section: R3 -Support For Modern Deploymentsmentioning

confidence: 99%

BeFaaS: An Application-Centric Benchmarking Framework for FaaS Platforms

Grambow

Pfandzelter

Burchard

et al. 2021

2021 IEEE International Conference on Cloud Engineering (IC2E)

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Due to the popularity of the FaaS programming model, there is now a wide variety of commercial and open-source FaaS systems. Hence, for comparison of different FaaS systems and their configuration options, FaaS application developers rely on FaaS benchmarking frameworks. Existing frameworks, however, tend to evaluate only single isolated aspects, a more holistic application-centric benchmarking framework is still missing.In previous work, we proposed BeFaaS, an extensible application-centric benchmarking framework for FaaS environments that focuses on the evaluation of FaaS platforms through realistic and typical examples of FaaS applications. In this extended paper, we (i) enhance our benchmarking framework with additional features for distributed FaaS setups, (ii) design application benchmarks reflecting typical FaaS use cases, and (iii) use them to run extensive experiments with commercial cloud FaaS platforms (AWS Lambda, Azure Functions, Google Cloud Functions) and the tinyFaaS edge serverless platform. BeFaaS now includes four FaaS application-centric benchmarks, is extensible for additional workload profiles and platforms, and supports federated benchmark runs in which the benchmark application is distributed over multiple FaaS systems while collecting fine-grained measurement results for drill-down analysis.Our experiment results show that (i) network transmission is a major contributor to response latency for function chains, (ii) this effect is exacer- * This work extends [1]. bated in hybrid edge-cloud deployments, (iii) the trigger delay between a published event and the start of the triggered function ranges from about 100ms for AWS Lambda to 800ms for Google Cloud Functions, and (iv) Azure Functions shows the best cold start behavior for our workloads.

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“…In order to leverage the capabilities of the fog, it has been proposed to deploy applications on FaaS platforms that run on the fog infrastructure. 10,12,[21][22][23][24][25] Open source platforms such as OpenWhisk or OpenLambda 15 are a good fit for cloud nodes and larger intermediary nodes, yet their larger deployment overheads make them unsuitable for smaller intermediaries or the edge. 26 On more constrained nodes, Lean OpenWhisk and tinyFaaS 10 are possible options for function deployment, whereas NanoLambda is an option for on-device FaaS deployment.…”

Section: Fog-based Faas Platformsmentioning

confidence: 99%

AuctionWhisk: Using an auction‐inspired approach for function placement in serverless fog platforms

et al. 2021

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The Function-as-a-Service (FaaS) paradigm has a lot of potential as a computing model for fog environments comprising both cloud and edge nodes, as compute requests can be scheduled across the entire fog continuum in a fine-grained manner. When the request rate exceeds capacity limits at the resource-constrained edge, some functions need to be offloaded toward the cloud. In this article, we present an auction-inspired approach in which application developers bid on resources while fog nodes decide locally which functions to execute and which to offload in order to maximize revenue. Unlike many current approaches to function placement in the fog, our approach can work in an online and decentralized manner. We also present our proof-of-concept prototype AuctionWhisk that illustrates how such an approach can be implemented in a real FaaS platform. Through a number of simulation runs and system experiments, we show that revenue for overloaded nodes can be maximized without dropping function requests.

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Towards a Computing Platform for the LEO Edge

Cited by 27 publications

References 20 publications

Beijing Language and Culture University, China

Beijing Language and Culture University, China

BeFaaS: An Application-Centric Benchmarking Framework for FaaS Platforms

AuctionWhisk: Using an auction‐inspired approach for function placement in serverless fog platforms

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