Skip to main content

Building an Intelligent System of Insights and Action for 5G Network Management

Periodic Reporting for period 1 - CogNet (Building an Intelligent System of Insights and Action for 5G Network Management)

Reporting period: 2015-07-01 to 2016-06-30

The overall objective of the CogNet project is to make a major contribution towards autonomic network management through the use of network information such as state, topology, configuration and various key metric data, and relevant external data which we call situational context, and use this to proactively manage the network, maintain sufficient capacity to meet demand levels and maintain Quality of Service at the levels required by the different types of network users.

At a more detailed level, the following key objectives were identified :

1. Research and develop a system of data collection from network nodes that involves preprocessing data to allow the node classify the data it generates and identify the most important and irregular data for submission to network management while filtering routine and regular data. This is an important step in the development of scalable network management as it dramatically reduces the scale of data required to be processed centrally.

2. While working on the principles of a self organising network, research and develop, within existing policy management frameworks, a system to allow network nodes to self manage based on their available data while escalating higher importance issues to central network management.

3. Apply Machine Learning algorithms to develop a system of service demand prediction and provisioning which allows the network to resize and resource itself, using virtualisation, to serve predicted demand according to parameters such as location, time and specific service demand from specific users or user groups. This is achieved while optimising performance and use of available network and VM resources while minimising overall energy requirements and costs.

4. Apply Machine Learning algorithms to address network resilience issues. This includes using Supervised ML to identify network errors, faults or conditions such as congestion at both a network wide and a local level and automatically taking mitigating actions to minimise overall impact.

5. Use anomaly detection algorithms to identify serious security issues such as unauthorised intrusion or fraud and liaise with autonomic network management & policies to formulate and take appropriate action.

6. Develop a number of demonstrable applications using real-world data gathered via current 4G network nodes which demonstrate the core project innovations, and serve to highlight the exploitation potential of CogNet. The applications will include tests to demonstrate the potential improved performance and capacity that can be achieved by utilising the CogNet algorithms over conventional approaches used in today’s Network Management Systems.


The project goal is to develop some of the solutions that will provide this higher and more intelligent level of automated monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of 5G. To achieve this, the project will conduct research in the areas of network data gathering, machine learning, data analytics and autonomic network management.

The research in CogNet will eventually contribute towards more reliable mobile communications which in turn will allow new applications such as autonomic driving, smart mobile medical technology, richer communications experiences, and a more energy efficient ICT infrastructure.
WorkPackage 1
Prior to official start of the project, the coordinator was responsible for the successful negotiation of the Consortium Agreement during this reporting period. The agreement for this consortium is signed and in place among all of the partners.

The first deliverable of WP1 was the Project Management Handbook which outlined the procedures roadmap for the group, in support of the Grant Agreement and Consortium Agreement. The plan details a focused and effective communications and management structure for all partners. The structure outlined delegates a certain amount of autonomy to Work Package Leaders, but all the time using the Grant Agreement, and regular meetings and calls to ensure WP leaders and all partners are supported and heard.

The second deliverable was the Data Management Plan document which outlined the processes to be used when using or processing data which may give rise to privacy issues. The plan outlined how such issues would be dealt with and included any required notifications of Data Protection Agencies in the jurisdictions concerned. This particular deliverable was particularly important for CogNet as it is certain that CogNet will deal with information that may raise such privacy issues.

WorkPackage 2
The work carried out in the first year in WP2 included:
Analysis of the use cases and scenarios of the CogNet system aiming at representative yet extensible set of deployment domains with a deployment specific set of platform services and components. This objective is directly reflected by Task 2.1 (led by IBM).

Modeling and design of the technical and business requirements for the CogNet system covering the representative set of usage scenarios and arranged in a hierarchy. This objective is directly mapped to Task 2.2 (led by TSSG).

Engineering the high-level architecture of the CogNet system as a harmonious set of services, service components and configurations that meet the requirements in all representative deployment domains, supported by a CogNet information model. This objective is directly mapped to Task 2.3 (led by IBM).

Deliverable D2.1 was finalized at the end of M5. The goal of this deliverable was to identify and define a set of use cases and scenarios and their requirements, related to the 5G network, specifically from a network management perspective. The deliverable introduced six use cases and eleven scenarios pivoting around cognitive network management, in particular leveraging machine learning for addressing the six identified CogNet challenges. Each scenario presented its definition, technical enablers and user story. The scenarios ranged from large scale events prediction to dense urban area congestion prediction.

WorkPackage 3
During the first year, one deliverable, D3.1 has been produced in M8 under the supervision of UNITN, with the contributions by all the WP3 partners and with the internal reviewing by ORANGE. 19 research papers have been published by the WP3 partners or accepted for publication, two of them winning best paper awards. This shows that the work package is progressing according to the research plan and the objectives are being achieved successfully.
After the completion of D3.1 the work package focused on implementing the first prototype of the CogNet Smart Engine, to be reported in Deliverable 3.2 (M16). This work follows two important directions. First, WP3 are implementing machine learning modules for various CogNet scenarios and use cases, as identified in WP2. This is done individually by each partner, in close collaboration with the applied work packages 4 and 5. Secondly, WP3 are designing a common SPARK-based interface for all the modules supported by individual partners. This is a collective effort by the whole WP to be then adopted by each contributor.

WorkPackage 4
Machine Learning techniques have been researched to allow scale up/down NFVi resources (e.g. virtual machines) elastically in order to achieve energy efficiency or sys
"CogNet has achieved a number of steps beyond the state of the art over the course of the first year :

Development of an architecture that successfully applies a Machine Learning infrastructure overlay to a framework for policy based network managenment and a network infrastructire employing NFV and SDN technology.

Identification and refinement of a number of ML algorithm and theoir application to network management use cases such as Large-Scale Events, ""Noisy Neighbour"", Performance Degradation Detection, Geo-Analysis for Demand Prediction, Collaborative Resource Management, User QoS Determination and SLA Management.

A number of proof of concepts have been developed to varying levels of maturity, two of which have been developed to a demonstrator level in WP6.

Publication of a paper “Using Machine Learning to Detect Noisy Neighbors in 5G Networks” in July, 2016 at the EUCNC Workshop “Network Management, Quality of Service and Security for 5G Networks”. To our knowledge this is the first paper that proposes a machine learning approach to detect the Noisy Neighbours problem in a NFV scenario.

Development of an Integration environment for developing and demonstrating the project use cases. This environment brings together an Openstack based infrastructure, using OpenStack ecosystem projects, along with Telecoms Infrastructure OS projects including OpenDayLight, Open Source MANO and OpenBaton and marries it to a Machine Learning data collection and computation infrastructure to provide almost real time insights into the operations of the network.

CogNet has also engaged with external network management experts, in particular those involved in the emerging IETF SUPA standard for policy based management expression. The architecture plans to incorporate this into its overall design and would serve as an ideal test case for the SUPA standard.


The potential impact and societal benefits from this research will include development of techniques to manage the larger and more dynamic network topologies necessary in 5G, facilitate 5G applications which maintain QoS and Security levels such as Network Slicing, and lower the capital and operational costs through improved efficiencies and the use of node, link and function virtualisation. These will eventually contribute towards more reliable mobile communications which in turn will allow new applications such as autonomic driving, smart mobile medical technology, richer communications which may help reduce the need for business travel, and a more energy efficient ICT infrastructure.


"
biglogo.png