AdaM: an Adaptive Monitoring Framework for Sampling and Filtering on IoT Devices IEEE International Conference on Big Data 2015 (BigData 2015) Oct 29 – Nov 01, 2015 @ Santa Clara, CA, USA Demetris Trihinas, George Pallis, Marios D. Dikaiakos University of Cyprus {trihinas, gpallis, mdd}@cs.ucy.ac.cy
27
Embed
AdaM: an Adaptive Monitoring Framework for Sampling and Filtering on IoT Devices
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
AdaM: an Adaptive Monitoring Framework for Sampling and Filtering
on IoT Devices
IEEE International Conference on Big Data 2015 (BigData 2015)
Oct 29 – Nov 01, 2015 @ Santa Clara, CA, USA
Demetris Trihinas, George Pallis, Marios D. DikaiakosUniversity of Cyprus
{trihinas, gpallis, mdd}@cs.ucy.ac.cy
Demetris Trihinas 2
IoT was initially devices sensing and exchanging data streams with humans or other network-enabled devices
IEEE BIGDATA CONFERENCE 2015
Edge-Mining
Demetris Trihinas 3
A term coined to reflect data processing and decision-making on
“smart” devices that sit at the edge of IoT networks
Buy more detergent
…our devices just got a little bit more “smarter”…
IEEE BIGDATA CONFERENCE 2015
The “Big Data” in IoT
Demetris Trihinas 4
• Taming data volume and data velocity with
limited processing and network capabilities
• IoT devices are usually battery-powered which means intense
processing leads to increased energy consumption (less battery-life)
Zhang et al., Usenix HotCloud, 2015
Challenges
[IDC, Big Data in IoT, 2014]
IEEE BIGDATA CONFERENCE 2015
Adaptive Sampling and Filtering
Demetris Trihinas 5IEEE BIGDATA CONFERENCE 2015
Metric Stream
Demetris Trihinas 6
• A metric stream 𝑀 = {𝑠𝑖}𝑖=0𝑛 is a large sequence of collected samples,
denoted as 𝑠𝑖 where 𝑖 = 0, 1, … , 𝑛 and 𝑛 → ∞
• Each sample 𝑠𝑖 is a tuple 𝑡𝑖 , 𝑣𝑖 described by a timestamp 𝑡𝑖 and a
value 𝑣𝑖
𝑠𝑖
𝑠𝑖+1
Metric Stream M
IEEE BIGDATA CONFERENCE 2015
Periodic Sampling
Demetris Trihinas 7
• The process of triggering the collection mechanism of a monitored source every
𝑇 time units such that the 𝑖𝑡ℎ sample is collected at time 𝑡𝑖 = 𝑖 ∙ 𝑇
𝑠𝑖
𝑠𝑖+1
Metric Stream M sampled every T = 1s
Compute resources and energy are wasted while generating large data volumes at a high velocity
Metric Stream M sampled every T = 10s
Sudden events and significant insights are missed
IEEE BIGDATA CONFERENCE 2015
Adaptive Sampling
Demetris Trihinas 8
• Dynamically adjust the sampling period 𝑇𝑖 based on some function,
containing information of the metric stream evolution