An EUREKA Celtic-plus project SENDATE-EXTEND. Adj. prof. Tor Björn Minde, CEO SICS North Swedish ICT AB (Head of Strategy Ericsson Research)

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1 An EUREKA Celtic-plus project SENDATE-EXTEND Adj. prof. Tor Björn Minde, CEO SICS North Swedish ICT AB (Head of Strategy Ericsson Research)

2 MARKET TRENDS

great amount, owned & shared Things connected and intelligent

3 A new industry era and transformation The digitalization looks like the industrialization Digitalization traditional industries Data great amount, owned & shared Things connected and intelligent Communications faster and more Users active and enhanced Functions More and seamless

4 Application characteristics Type Response times Data amount Traffic amount Cache DC location Cold storage seconds Gigabytes Mb/s remote Off-line big data crunching seconds Gigabytes Gb/s remote Chat/IoT type communication 100 th milliseconds kilobytes kb/s remote Web/app rendering 100 th milliseconds Megabytes Mb/s Yes remote Streaming 10 th milliseconds Gigabytes Mb/s Yes mix Real-time conferencing 10 th milliseconds Megabytes Mb/s Yes mix Real-time analytics milliseconds Megabytes Gb/s proximity Transaction based/control loops milliseconds kilobytes kb/s proximity

5 Mega datacenter complexity Source: Nyteknik 2012

6 PROJECT IDEA

7 SENDATE EXTEND SEcure Networking for a DATa center cloud in Europe EXTENded Datacenter solutions Improve efficiency and flexibility of deployment, monitoring, operations, maintenance and management of storage, compute, communication and energy supply infrastructure within datacenters and in a distributed cloud

8 SENDATE EXTEND SEcure Networking for a DATa center cloud in Europe EXTENded Datacenter solutions Improve efficiency and flexibility of operating all different parts within datacenters and in a distributed cloud

9 Building Assumptions Power Assumptions Cooling Assumptions IT Hardware Assumptions Networking Assumptions IT Software Assumptions Application EXTEND Holistic Datacenter Automation Control Control Control Control Control Control Control

10 Building Knowledge Power Knowledge Cooling Knowledge IT Hardware Knowledge Networking Knowledge IT Software Knowledge Application EXTEND Holistic Datacenter Automation Common Policies, Shared Data(base), Common Optimization Criteria Control Control Control Control Control Control Control

11 SICS ICE A PROJECT RESOURCE

12 SICS-ICE Room-in-room module 1 In operation since January GB of metrics data now

13 SICS-ICE room-in-room module 2 Under construction. Ready for use January 2017

14 A FEW USE-CASES

Converged architecture for geographically distributed metro DCs The architecture enables to dynamically establish lightpath between racks in different DCs Impact of various abstraction

15 Converged architecture for geographically distributed metro DCs The architecture enables to dynamically establish lightpath between racks in different DCs Impact of various abstraction policies (for the intra- and inter-dc resources) on the provisioning of ToR to ToR connectivity Protection and restoration strategies of provisioned cloud services based on the concept of VM migration

Fault Localization Fault Detection Root Cause Analytics based on metric correlation RCA

Feature Engineer OAM data from PMs and VMs Cloud Monitoring System Alarm generating approaches:

Anomaly detection Cloud Infrastructure Fault localizer (Localize faulty nodes) Fault localizer

nodes Detecting faulty metrics Fault classification (manually or automated) Calculate Actuation

16 Fault Localization Fault Detection Root Cause Analytics based on metric correlation RCA Analytics Build SLA prediction model SLA violation detection using model Data Pre-processor / Feature Engineer OAM data from PMs and VMs Cloud Monitoring System Alarm generating approaches: Complete GT availability SLA violation detection using predictions SLA violation forecasting Anomaly detection Cloud Infrastructure Fault localizer (Localize faulty nodes) Fault localizer (Localize performance faults in a faulty node) Fault/Anomaly Localization: Detecting faulty nodes Detecting faulty metrics Fault classification (manually or automated) Calculate Actuation parameters Actuation /Orchestration of system resources Resource requirement prediction Source Ericsson

17 Metrics backbone Zafka Defka Other systems Xxfka

18 Maintenance application Metrics database Data collection Failure detection Asset positioning Asset management Light-weight application

IT load Use Case vs Cooling 1 - IT load vs Cooling Method IT load

analysis over 48 hours to present IT load, cooling load and server

Dynamic cooling, flow, T -IT load shifting - Distributed air flow

individual racks and a degree of influence Dynamic cooling control

19 IT load Use Case vs Cooling 1 - IT load vs Cooling Method IT load scheduling analysis over a period to find IT load pattern IT load analysis over 48 hours to present IT load, cooling load and server temperature variations Simulate a number of cooling strategies - Dynamic cooling, flow, T -IT load shifting - Distributed air flow -Shutter flow control A tool to provide relations among CRAHs and individual racks and a degree of influence Dynamic cooling control in zonelevel or rack-level ABB Group Month DD, Year Slide 4 Source ABB

20 PROJECT FACTS

21 Project partners Large enterprises Medium enterprises Small enterprises Academia Financial support

22 Project budget

23 supported by

Knowledge-Defined Network Orchestration in a Hybrid Optical/Electrical Datacenter Network

Knowledge-Defined Network Orchestration in a Hybrid Optical/Electrical Datacenter Network Wei Lu (Postdoctoral Researcher) On behalf of Prof. Zuqing Zhu University of Science and Technology of China, Hefei,