Exploring the Impact of Workload Distribution in a Hybrid Edge and - - PowerPoint PPT Presentation

Exploring the Impact of Workload Distribution in a Hybrid Edge and Cloud Application for Smart Grids

Ot´ avio Carvalho, Manuel Garcia, Eduardo Roloff, Phillipe O. A. Navaux

Federal University of Rio Grande do Sul - Parallel and Distributed Processing Group (GPPD)

Table of contents

• 1. Introduction
• 2. Architecture and Implementation
• 3. Evaluation
• 4. Conclusion and Future Works

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Introduction - Motivation

• Smart Grids potential to save billions of dollars in energy spending

for both producers and consumers.

• Internet of Things potential economic impact.
• Technologies created for IoT are driving computing toward

dispersion.

• Edge Computing
• Cloudlets
• Micro-datacenters
• Fog Nodes

3

Introduction - Main goals

• Explore the potential performance improvements of moving

computation from cloud to edge in a Smart Grid application.

• 1. What are the boundaries of our application architecture in terms of

latency and throughput?

• 2. To what extent is it possible to move our workload from cloud to

edge nodes?

• 3. Which strategies can be used to reduce the amount of data that is

sent to the cloud?

4

Architecture and Implementation

• Three-layered architecture:
• Cloud-layer
• High latency processing.
• Receives aggregated data from multiple edge nodes.
• Composed by applications running on Linux VMs on Windows Azure.
• Edge-layer
• Low latency processing.
• Receives data from multiple sensors and perform local processing.
• Reduces the amount of data that needs to be sent to Cloud-layer.
• Composed by ARM nodes (Raspberry Pi Zero W) connected to

Wi-Fi.

• Sensor-layer
• Measurements only.
• Produces a high amount of measurements that should be sent to

Edge-layer for aggregation.

• For evaluation purposes, our sensor measurements are pre-loaded into
• ur Edge-layer nodes.

5

Architecture and Implementation

Figure 1: Architecture overview: Three-layered architecture

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Evaluation - Communication

5000 10000 15000 50th 90th 99th

Percentiles (ms) Latency (ms)

32KB 64KB 128KB 256KB 512KB 1024KB

Figure 2: PingPong: Latency Percentiles by Message Sizes (32KB to 1MB)

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Evaluation - Communication

0.0 0.5 1.0 1.5 32KB 64KB 128KB 256KB 512KB 1024KB

Size (KB) Throughput (QPS)

32KB 64KB 128KB 256KB 512KB 1024KB

Figure 3: PingPong: Maximum Throughput by Message Size (32KB to 1MB)

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Evaluation - Application concurrency

2000 4000 6000 8000 1 10 100 Concurrency (Number of Goroutines) Throughput (QPS) Edge Cloud

Figure 4: Concurrency Analysis: Impact of Goroutines usage on throughput (Edge and Cloud nodes)

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Evaluation - Application scalability

500 1000 1500 2000 1 2 4 Number of Edge Nodes Throughput (QPS) 1 2 4

Figure 5: Scalability Analysis: Throughput with multiple consumers (1 to 4 edge nodes)

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Evaluation - Workload windowing

200000 400000 600000 800000 1 2 4 Number of Edge Nodes Throughput (QPS) 1 10 100 1000

Figure 6: Windowing Analysis: Windowing impact on throughput (1 to 1000 messages per request)

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Conclusion and Future Works

• Conclusion
• The application was able to achieve a higher throughput by

leveraging processing on edge nodes.

• We were able to reduce communication with the cloud by

aggregating data at edge level.

• Future Works
• Study how other communication protocols (such as MQTT) would

behave in this application context.

• Explore techniques and models for adaptive workload scheduling.
• Evolve the application architecture to a general framework for IoT.

12

Thanks! Questions?

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