With billions of users and millions of apps, the 3rd platform presents traffic congestion challenges to traditional cloud computing services. Data and workloads generated from IoT devices are typically not required outside of the IoT device locality yet sending the data to remote cloud servers increases latency, jitter, and the risk for dropped packets as a result of increased traffic load. The Fog Computing paradigm aims to alleviate these concerns by bringing the cloud closer to the device. This talk looks at the optimisation methods used for deciding the ideal placement location of IoT workloads within a Fog Node environment. Youtube video available at : https://youtu.be/jnqOgWuNm30

1. Towards optimal IoT workload placement
in a fog computing environment
Kenneth Moore
7th November 2019
vmworld VMTN5105E

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Background
Kenneth Moore
 Data Scientist & PhD Student @ Dell EMC Open HCI Solutions
 Researching workload modelling and simulation of Converged
Infrastructure SDDC workloads for large-scale IoT deployments

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What considerations are required for
Placing IoT workloads on Fog / Edge nodes?

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Fog Computing Architecture
Source: OpenFog Consortium
Higher
Lower
Higher
Lower

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Fog
Computing
Challenges
Mobility
Task
Placement
Security &
Privacy
Interoperability
Resource
Management
Energy
Management
QoS & QoE
Fog
Computing
Challenges
Mobility
Task
Placement
Security &
Privacy
Interoperability
Resource
Management
Energy
Management
QoS & QoE
Fog/Edge Research Topics

7. Workload Placement Locations

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Workload Placement Locations
Broker/Auction
Approach
Fog Edge Node offering
best solution to a desired
optimisation costing will
process the workload ?
? ??

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Workload Placement Locations
Distributed
Approach
Grouped Fog Edge Nodes
best suited to a desired
optimisation costing
technique share workload
processing
?
? ??

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Workload Placement Locations
Hierarchical
Approach
Workload offloaded to
next level Fog/Cloud
node infrastructure
?
? ??

11. Workload Placement Owner

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Who makes the placement decision?

13. Optimisation Costing Methods

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Optimisation Costing Methods
Resource
Awareness
• Cost: Maximise resource usage
• CPU (# cores or % utilisation)
• Memory (capacity)
• Disk (capacity)
• Benefit: Maximum throughput
ensuring more workloads can be
processed closer to the IoT device

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Optimisation Costing Methods
QoS/QoE
Awareness
• Cost: Minimise QoS/QoE metrics
• Latency
• Jitter
• Delay
• Benefit: IoT device service
requirements achieved

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Optimisation Costing Methods
Energy
Awareness
• Cost: Minimise energy usage
• Benefit: reduced energy
consumption
• Reduced energy costs
• Reduced carbon footprint
• Battery capacity reserves

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Optimisation Costing Methods
Infrastructure Centric IoT Device Centric Environment Centric

18. Further Study

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Questions