A Unified Model for the Mobile-Edge-Cloud Continuum

Baresi, Luciano; Mendonça, Danilo Filgueira; Garriga, Martín; Guinea, Sam; Quattrocchi, Giovanni

doi:10.1145/3226644

Cited by 67 publications

(27 citation statements)

References 28 publications

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“…They focus on stream-oriented workflows to filter data near the sources but they do not use a serverless approach and no open-source implementation is provided. The work by Baresi et al [12] introduces the A3-E unified model for the Mobile-Edge-Cloud continuum which exploits the FaaS model to bring computation to the continuum. It uses Apache Open-Whisk to support the implementation together with AWS Lambda.…”

Section: Related Workmentioning

confidence: 99%

Serverless Workflows for Containerised Applications in the Cloud Continuum

et al. 2021

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This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.

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Section: Related Workmentioning

confidence: 99%

Serverless Workflows for Containerised Applications in the Cloud Continuum

et al. 2021

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“…Continuum describes a hierarchical execution environment comprised of edge, fog, and cloud resources, working in tandem with dynamic workload migration between them. Many serverless edge platforms are not limited to only running at the edge, instead their aim is to develop versatile products that can be run anywhere, at either the edge, fog, or cloud, offering the same function syntax across the whole network [68,75]. When coupled with intelligent scheduling algorithms that can automatically determine the optimal execution location, as opposed to relying on the administrator to make the right decision [85], a true edge-fog-cloud continuum [28] can be established.…”

Section: Classification Of Existing Literaturementioning

confidence: 99%

IoT Serverless Computing at the Edge: A Systematic Mapping Review

2021

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Serverless computing is a new concept allowing developers to focus on the core functionality of their code, while abstracting away the underlying infrastructure. Even though there are existing commercial serverless cloud providers and open-source solutions, dealing with the explosive growth of new Internet of Things (IoT) devices requires more efficient bandwidth utilization, reduced latency, and data preprocessing closer to the source, thus reducing the overall data volume and meeting privacy regulations. Moving serverless computing to the edge of the network is a topic that is actively being researched with the aim of solving these issues. This study presents a systematic mapping review of current progress made to this effect, analyzing work published between 01.01.2015 and 01.09.2021. Using a document selection methodology which emphasizes the quality of the papers obtained through querying several popular databases with relevant search terms, we have included 64 entries, which we then further categorized into eight main categories. Results show that there is an increasing interest in this area with rapid progress being made to solve the remaining open issues, which have also been summarized in this paper. Special attention is paid to open-source efforts, as well as open-access contributions.

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“…By processing the tasks within edge datacenters, we benefit from reduced energy utilization in comparison to excessive consumption in traditional cloud datacenters. The drawback of edge datacenters is that they have less processing power than cloud datacenters [14], and hence, the scheduling strategy is vital for tasks with highly variable sizes.…”

Section: Task Schedulingmentioning

confidence: 99%

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

Stan

Băjenaru

Negru

et al. 2021

Sensors

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This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

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A Unified Model for the Mobile-Edge-Cloud Continuum

Cited by 67 publications

References 28 publications

Serverless Workflows for Containerised Applications in the Cloud Continuum

Serverless Workflows for Containerised Applications in the Cloud Continuum

IoT Serverless Computing at the Edge: A Systematic Mapping Review

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

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