Monitoring & Predicting QoS in IoT Services
Citation:
WHITE, GARY, Monitoring & Predicting QoS in IoT Services, Trinity College Dublin.School of Computer Science & Statistics, 2020Download Item:
thesis.pdf (PDF) 11.88Mb
Abstract:
Internet of Things (IoT) applications can be built from a number of heterogeneous
services provided by a range of devices, which are potentially resource constrained and/or
mobile. These device characteristics can lead to the services deployed on them becoming
unreliable as the device may lose power or move out of range. As these services and
applications continue to be more widespread, a key research challenge is how to make
them more reliable. The reliability of an application is influenced by the time to detection
(TTD) of a failure and the time to recovery (TTR) of services in the application after the
failure. TTD and TTR are affected by the accuracy of the prediction and by the time
it takes to receive the prediction. This thesis focuses on reducing TTD by improving
the prediction accuracy and reducing TTR by reducing the time it takes to receive the
prediction.
Accurate short-term forecasts allow dynamic systems to adapt their behaviour when
degradation is detected e.g., transportation forecasting supports alternative routing of
traffic before gridlock and wind power forecasting enables the use of dispatchable energy
such as hydroelectric power to reduce the difference between power consumption and
power generation in the network. This rationale can be applied to service-oriented
computing when creating and managing service applications, where such applications
are composed of available collaborating services. The faster a problem with a service can
be detected, the faster a suitable replacement service can be chosen. Previous approaches
that have focused on QoS forecasting have used traditional time series methods, but these
are not suitable as QoS does not exhibit traditional time series patterns (i.e., sudden
peaks caused by network congestion or a device switching to a power saving mode).
More modern recurrent neural network-based approaches such as GRUs and LSTMs
have been proposed but the long training time means they take longer to incorporate
recent QoS values. This can lead to a reduction in forecasting accuracy in dynamic IoT
environments. This thesis proposes a noisy-echo state network approach that has been
designed to be deployed at the edge of the network. The reduced training time allows
the model to incorporate recent QoS values on devices at the edge. The results show
increased forecasting accuracy compared to current state of the art approaches when
tested on a combined dataset of IoT and web services, reducing TTD.
Once a problem has been detected with one of the services in a composition, the application
needs to be recovered by using a functionally equivalent service with high QoS.
Given that candidate services may not be currently executing, predictions based on a
time series of current QoS values are not appropriate. Recommending possible replacement
services requires a technique that avails of similar users' recent experience of those
services, which is the most up-to-date information available about the services'
Sponsor
Grant Number
Science Foundation Ireland (SFI)
Description:
APPROVED
Author: WHITE, GARY
Advisor:
CLARKE, SIOBHANPublisher:
Trinity College Dublin. School of Computer Science & Statistics. Discipline of Computer ScienceType of material:
ThesisCollections:
Availability:
Full text availableKeywords:
IoT, Prediction, QoS, Monitoring, Machine LearningLicences: