SRDS2019: Abeona: an Architecture for Energy-Aware Task Migrations from the Edge to the Cloud
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This paper presents our preliminary results with ABEONA, an edge-to-cloud architecture that allows migrating tasks from low-energy, resource-constrained devices on the edge up to the cloud. Our preliminary results on artificial and real world datasets show that it is possible to execute workloads in a more efficient manner energy-wise by scaling horizontally at the edge, without negatively affecting the execution runtime.
![The LEGaTO project has received funding from the European Union’s Horizon 2020 research and
innovation programme under the grant agreement No 780681. www.legato-project.eu
1 Introduction
• Goal: use task metrics to leverage architecture
heterogeneity improving energy and performance
• Method: analize effect of different placement
and migration criteria for task-based workloads
• Challenge: orchestrate applications deployed
across a layered architecture
2 Model
• Advantages of 3-layer hierarchical infrastructure:
• combines advantage of each layer
• increases opportunities for migrations
• optimizes overall energy consumption while
still respecting tasks’ requirements
3 Use cases
• Energy Management: collect and analyze energy
consumption of a given place
• Smart Mirror: display data on a looking-glass
4 Architecture
5 Preliminary Results
• Fog layer consisting of Kubernetes cluster of
Raspberry Pis as worker nodes
• Runtime reduction up to 48%
• Energy reduction up to 46%
6 Future Work
• Increase diversity of nodes and workloads
• Leverage a mix of Intel SGX and ARM TrustZone
for application operating on private data
• CPU frequency scaling
Abeona: an Architecture for Energy-Aware Task
Migrations from the Edge to the Cloud
Isabelly Rocha, Pascal Felber,
Marcelo Pasin, Valerio Schiavoni
University of Neuchâtel, Switzerland
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PageRank AES
Runtime[s]
Energy[kJ]
#nodes
Runtime [s] Energy [kJ]
Controller Metrics ProbeScheduler
Metrics ProbeSchedulerManager
Metrics ProbeScheduler
Metrics
Analyzer
Migration
Manager
Scheduler
Tasks
CloudFogEdge
Higher
Performance
LatencyLowerCloud
Data
Centers
Fog
Nodes
Edge
Devices
Gabriel Vinha, Andrey Brito
Federal University of Campina Grande, Brazil
SRDS’19: The 38th International Symposium on Reliable Distributed Systems](https://image.slidesharecdn.com/poster-191004083032/85/SRDS2019-Abeona-an-Architecture-for-Energy-Aware-Task-Migrations-from-the-Edge-to-the-Cloud-1-320.jpg)
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SRDS2019: Abeona: an Architecture for Energy-Aware Task Migrations from the Edge to the Cloud
- 1. The LEGaTO project has received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 780681. www.legato-project.eu 1 Introduction • Goal: use task metrics to leverage architecture heterogeneity improving energy and performance • Method: analize effect of different placement and migration criteria for task-based workloads • Challenge: orchestrate applications deployed across a layered architecture 2 Model • Advantages of 3-layer hierarchical infrastructure: • combines advantage of each layer • increases opportunities for migrations • optimizes overall energy consumption while still respecting tasks’ requirements 3 Use cases • Energy Management: collect and analyze energy consumption of a given place • Smart Mirror: display data on a looking-glass 4 Architecture 5 Preliminary Results • Fog layer consisting of Kubernetes cluster of Raspberry Pis as worker nodes • Runtime reduction up to 48% • Energy reduction up to 46% 6 Future Work • Increase diversity of nodes and workloads • Leverage a mix of Intel SGX and ARM TrustZone for application operating on private data • CPU frequency scaling Abeona: an Architecture for Energy-Aware Task Migrations from the Edge to the Cloud Isabelly Rocha, Pascal Felber, Marcelo Pasin, Valerio Schiavoni University of Neuchâtel, Switzerland 0 1000 2000 3000 4000 5000 6000 1 2 3 1 2 3 0 10 20 30 40 50 60 PageRank AES Runtime[s] Energy[kJ] #nodes Runtime [s] Energy [kJ] Controller Metrics ProbeScheduler Metrics ProbeSchedulerManager Metrics ProbeScheduler Metrics Analyzer Migration Manager Scheduler Tasks CloudFogEdge Higher Performance LatencyLowerCloud Data Centers Fog Nodes Edge Devices Gabriel Vinha, Andrey Brito Federal University of Campina Grande, Brazil SRDS’19: The 38th International Symposium on Reliable Distributed Systems