The first "Insights in Technology Conference" was in Schaffhausen on December 16, 2019. The event is organized by the Schaffhausen Institute of Technology SIT. Special guest is Nobel Prize winner Wolfgang Ketterle.
Schaffhausen Institute of Technology website: http://sit.org
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.
Building a model of the normal behavior for each application from collections of pods running the
same application, by relying on fast deep learning techniques (Deep Belief Networks, Deep
Convolutional Neural Networks) trained in a semi-supervised fashion, without relying on faulty data
for training
Improving supervised learning techniques for performance deviation analysis, leveraging userbased
SLA violation as labels for each application , eg. distribution of response times below a
certain threshold
Analyzing distributions of response time at service level and exploit hypothesis testing and
regression techniques to predict behavior and detect deviations from the norm. Salacia will
implement fast algorithms based on standard machine learning techniques for fast and robust nonlinear
regression.
Localising faults by analyzing the relation between the health status at application level and the
application topology retrieved from weave scope as an adjacency list of containers, and issuing
11/30
fault alerts that indicate the culprit application and/or pod, by exploiting the information on the
application topology.
Activating self-healing procedures, which will leverage self-healing functionalities of Kubernetes to
implement self-healing actions on the pods that Salacia localises as responsible for the faulty
behavior at application level.