Energy-Optimal Sampling for Edge Computing
Feedback Systems: Aperiodic Case
Vishnu Narayanan Moothedath,
Division of Information Science and Engineering
KTH Royal Institute of Technology, Sweden
Contents
• Introduction and applicability
• The sampling challenge
• Related works and our work on optimum periodic sampling
• System Model
• Aperiodic Sampling:
• Results
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Introduction
• Edge Systems: Faster than local nearer than cloud
• Reasons to go for edge
–
–
–
–
Low processing power at the device
power sensitive device
Size of device
Required node-cooperation
• Relevance in modern systems
Eg. URLLC
– Needs ms/sub-ms latency
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Edge-Based Event Detection with Feedback
• Wearable Cognitive Assistants
WCA Terminal
Edge Device
Cloud
Human User
• Requirement: Capture Essential Events ASAP
WCA Task Instructions
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The Sampling Challenge
• High frequency sampling
* Communication
* Processing
⇒ Unnecessary communication, processing
⇒ Large amount of data in the pipeline
• Low frequency sampling
⇒ Reduced system responsiveness
⇒ Increase in task execution time.*
⇒ Idle energy wastage
• Energy/Responsiveness suffers on both extremes
• Aim: Find the optimum sampling interval.
* M. Olguín Muñoz, R. Klatzky, J. Wang, P. Pillai, M. Satyanarayanan and J. Gross.
"Impact of delayed response on wearable cognitive assistance", PLOS ONE, vol. 16, no. 3, p. e0248690, 2021.
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* Detection Delay
* Idle Energy
Sampling Interval
Related Works
• Full/ Partial Offloading  offload energy usage
• Event detection works with energy consideration
1. Optimising sensor technologies
2. Node cooperation
3. Selective sensor activation
 Our work -- optimal sample generation that reduces total amount of data in the
pipeline.
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Periodic Sampling: A quick look
• V. N. Moothedath, J. P. V.
Champati and J. Gross, "Energy
Efficient Sampling Policies for
Edge Computing Feedback
Systems," in IEEE Transactions on
Mobile Computing.
• V. N. Moothedath, J. P. Champati
and J. Gross, "Energy-Optimal
Sampling of Edge-Based Feedback
Systems," 2021 IEEE International
Conference on Communications
Workshops.
Parameters chosen from previous experimental
data, works on WCA and/or Google Glass.
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System Model : Aperiodic Sampling
• TTE: Time to Event
• TTF: Time to Feedback
• TTE is Random.
– What distribution?
• Deterministic communication, processing
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Objective: Energy Minimisation
• Energy is a function of
1. Number of samples
2. Wait time
• Relevant part is called Energy Penalty
• Aim: Minimise energy penalty for a given
distribution
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TTE Distribution: Logic behind the choices
• VAS (Video Analytics system)
– Rayleigh/Exp-Gaussian/Log-Normal
– E.g.: WCA, video surveillance
TTE Distribution of a WCA
Image courtesy: M. Olguín Muñoz, R. Klatzky, J. Wang, P. Pillai, M. Satyanarayanan and J. Gross.
"Impact of delayed response on wearable cognitive assistance", PLOS ONE, vol. 16, no. 3, p. e0248690, 2021.
2023-05-12
Aperiodic Sampling
• Conditions of a valid sampling interval
1. Positive
2. Decreasing
- Condition1
- Condition2
• Recursive solution with a given first sampling interval
• How to find optimum first sampling interval?
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Optimum t1 : A visual intuition
{tn} vs. n for different values of t1.
Two of the sequences that are valid upto n=15 are highlighted. The rest are invalid
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Optimum t1 : How to find it.
Design a bisection search to find the optimum t1. At each iteration a simple check for
validity is performed
This is possible because of their proven properties below.
• 1:
Plot ({tn} vs. n) does not cross each other for different n.
• 2:
All {tn} below those violate Condition1 are invalid.
All {tn} above those violate Condition2 are invalid.
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• 3:
Optimum {tn} always lies between two sequences that violates
different conditions
• 4:
The algorithm achieves a result arbitrarily close to the optimum,
with a convergence rate of 2-n
Aperiodic Sampling: Results
Penalty Vs Mean TTE for Optimum periodic policy and Optimum aperiodic policy
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Aperiodic Sampling: Results
Penalty reduction attained by optimum aperiodic policy over optimum periodic policy Vs. System parameters
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Thank You.