Edge‐adaptable serverless acceleration for machine learning Internet of Things applications

Zhang, Michael; Krintz, Chandra; Wolski, Rich

doi:10.1002/spe.2944

Cited by 17 publications

(13 citation statements)

References 24 publications

(24 reference statements)

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“…Linear Regression is used in [59] for function execution time prediction which is then used in a Least Slack First (LSF) algorithm to select a request for execution from the queue. Zhang et al use regression techniques to determine the total latency of executing a batch of serverless tasks over the edge and cloud runtime environments, in order to determine the runtime with the least latency [141]. In their work, median sliding window time series modelling technique is used to predict runtime deployment time while Bayesian Ridge regression technique is used for processing time estimation.…”

Section: Elements Of Resource Managementmentioning

confidence: 99%

“…When an application takes the form of a DAG, it could either be scheduled as a single unit or each individual function could be scheduled separately. In addition, the scheduler could decide to queue requests and schedule them in batches [141]. The scheduling granularity could be speciied as a requirement of the developer or decided by a serverless platform, aiming for better resource eiciency.…”

Section: Application Modelmentioning

confidence: 99%

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A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

2022

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Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include rapid auto-scaling, strong isolation, fine-grained billing options and access to a massive service ecosystem which autonomously handles resource management decisions. This model is increasingly being explored for deployments in geographically distributed edge and fog computing networks as well, due to these characteristics. Effective management of computing resources has always gained a lot of attention among researchers. The need to automate the entire process of resource provisioning, allocation, scheduling, monitoring and scaling, has resulted in the need for specialized focus on resource management under the serverless model. In this article, we identify the major aspects covering the broader concept of resource management in serverless environments and propose a taxonomy of elements which influence these aspects, encompassing characteristics of system design, workload attributes and stakeholder expectations. We take a holistic view on serverless environments deployed across edge, fog and cloud computing networks. We also analyse existing works discussing aspects of serverless resource management using this taxonomy. This article further identifies gaps in literature and highlights future research directions for improving capabilities of this computing model.

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Section: Elements Of Resource Managementmentioning

confidence: 99%

Section: Application Modelmentioning

confidence: 99%

A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

2022

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“…When an application takes the form of a DAG, it could either be scheduled as a single unit or each individual function could be scheduled separately. In addition, the scheduler could decide to queue requests and schedule them in batches [Zhang et al 2020]. The scheduling granularity could be specified as a requirement of the developer or decided by a serverless platform, aiming for better resource efficiency.…”

Section: Application Modelmentioning

confidence: 99%

A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

Mampage¹,

Karunasekera²,

Buyya³

2021

Preprint

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Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include, rapid auto-scaling, strong isolation, fine-grained billing options and access to a massive service ecosystem which autonomously handles resource management decisions. This model is increasingly being explored for deployments in geographically distributed edge and fog computing networks as well, due to these characteristics. Effective management of computing resources has always gained a lot of attention among researchers. The need to automate the entire process of resource provisioning, allocation, scheduling, monitoring and scaling, has resulted in the need for specialized focus on resource management under the serverless model. In this article, we identify the major aspects covering the broader concept of resource management in serverless environments and propose a taxonomy of elements which influence these aspects, encompassing characteristics of system design, workload attributes and stakeholder expectations. We take a holistic view on serverless environments deployed across edge, fog and cloud computing networks. We also analyse existing works discussing aspects of serverless resource management using this taxonomy. This article further identifies gaps in literature and highlights future research directions for improving capabilities of this computing model. CCS Concepts: • General and reference → Surveys and overviews; • Computer systems organization → Distributed architectures.

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“…Although the serverless paradigm was designed for cloud environments, it has been adapted to edge domains in order to incorporate its advantages, such as deleting always-on services, which provoke high electricity usage, even though its cloud-driven design may pose drawbacks [135]. On the other hand, the serverless edge-based IoT deployments integrated with the cloud for offloading purposes may succeed in reducing overall execution times and obtaining classification accuracy [136]. Additionally, if a warm-start deployment mode is used, then the FaaS platform always has available resources; whereas, a cold-start deployment mode's modules are deleted after its execution, thus bringing resource and cost savings [137].…”

Section: Edge Computing Applicationsmentioning

confidence: 99%

Modeling of a Generic Edge Computing Application Design

Roig

Alcaraz

Gilly

et al. 2021

Sensors

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Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.

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Edge‐adaptable serverless acceleration for machine learning Internet of Things applications

Cited by 17 publications

References 24 publications

A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

A Holistic View on Resource Management in Serverless Computing Environments: Taxonomy and Future Directions

Modeling of a Generic Edge Computing Application Design

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