Reliable Capacity Provisioning and Enhanced
Remediation for Distributed Cloud Applications
http://recap-project.eu recap2020
THIS PROJECT HAS RECEIVED FUNDING FROM THE EUROPEAN UNION’S HORIZON 2020
RESEARCH AND INNOVATION PROGRAMME UNDER GRANT AGREEMENT NUMBER 732667
RECAP Presentation for ETSI ENI
Warsaw, April 12th 2019

Introduction of the Presenters
2
Johan Forsman, Tieto
Johan Forsman is a business developer, product manager and principal solution architect at
Tieto Product Development Services (www.tieto.com/pds).
Johan has over 20 years of experience in development of telecommunication mobile systems
and is currently involved in business opportunities introducing NFV, 5G and IoT technologies.
Dr. Jörg Domaschka, Ulm University
Dr. Jörg Domaschka is a senior researcher leading UULM‘s research group on cloud
computing, large-scale architectures, and adaptive middleware platforms.
Jörg has been involved in EC funded research project since 2006 and is the project
coordinator of RECAP.
Dr. Paolo Casari, IMDEA Networks
Dr. Paolo Casari is a Research Assistant Professor at IMDEA Networks Institute, where he
leads the Ubiquitous Wireless Networking group. He holds a PhD in Information
Engineering from the University of Padova, Italy (2008).
He is the Scientific Coordinator of the RECAP Project.

Agenda
• Introduction
• Overview of RECAP
⁃ Introduction to RECAP
⁃ RECAP consortium
• RECAP solution and lessons learned
⁃ Model.centric approach
⁃ A repeatable methodology for generating the models
⁃ Data veracity (telemetry quality)
⁃ Simulation in a closed control loop
⁃ Separation of concerns
• NFV management use case
⁃ Objectives and challenges with the use case
⁃ Utilizing testbed and simulations to build trust
⁃ Traffic generation to train, validate and explore
⁃ Instrumentation and control for the RECAP closed loop
• Summary: lessons learned
3

Overview of RECAP
4

Reliable Capacity Provisioning and Enhanced
Remediation for Distributed Cloud Applications
Next generation of agile and optimized cloud computing systems
• Services are elastically instantiated and provisioned
close to the users that actually need them via
self-configurable cloud computing systems.
• Machine learning and simulation techniques for
provision of cloud services
• Applied to the following use cases
• Telco system for wireless & wireline
• Smart city
• Big data analytics
2017 - 2019
5
THIS PROJECT HAS RECEIVED FUNDING FROM THE EUROPEAN UNION’S HORIZON 2020
RESEARCH AND INNOVATION PROGRAMME UNDER GRANT AGREEMENT NUMBER 732667
https://recap-project.eu/

RECAP Consortium
6
Industry
• Intel Labs (Ireland)
• BT (UK)
• Tieto (Sweden)
• Satec (Spain)
• Linknovate (Spain)
Academic
• ULM University (Germany)
• Umeå University (Sweden)
• Dublin City University (Ireland)
• IMDEA Networks Institute (Spain)
• CERTH (Greece)
Use Case
Providers
THESSALONIKI, GREECE
CERTH

RECAP Solution and Lessons Learned
A reference design
7

RECAP: a model-centric approach
8
Workload
Application
Load Translation
Infrastructure
User
QoS

The RECAP Operational Modes
• The run-time operational mode, consisting of
⁃ operating applications in RECAP
⁃ the RECAP optimisation loops
9
Offline
Online
• The simulation and planning mode, which employs offline
simulation loops that are fed with monitoring data, application
and workload models, and optimisation results.
• The data analytics mode, which employs the offline analysis of
monitoring data, and the training of machine learning models.
Offline
Online

A repeatable methodology for model generation:
Application view
11

A repeatable methodology for model generation:
Infrastructure view
12

RECAP Data Collection, Data Analytics and Modelling
Goal: Data Veracity
⁃ Understanding resource consumption
Challenges:
⁃ Ensuring high-quality data (no errors, no data gaps)
⁃ Collating and correlating information from multiple
sources, formats and semantics
⁃ Prioritizing metrics applicable to the use-case
⁃ Proper probe setting to receive telemetry data
⁃ Correctly fitting models to interpret data
E.g., the following are determined:
• Computing Resources:
o 10% utilized per user
o 10x increase proportional to users
• Memory Resources:
o 5% utilized per user
o 1x increase proportional to users
Data Veracity

RECAP Simulations & Planning
1. Development of two simulators to support
several diverse use case requirements
2. Support to the Infrastructure Optimizer to
validate placement and deployment scenarios
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Simulation in the loop

15
vCDN Infrastructure
Virtual Caches Application
1. Belong to different commercial organisations, even competitors
2. Respond in very different timescales
3. Have different topologies
4. Have different aggregate load patterns
5. Optimisation policies with different priorities
Infrastructure and applications have to be managed and optimised separately
Lessons Learned: Separation of Concerns
Why?

Infrastructure and Network Management
NFV Management Use Case provided by Tieto
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INFRASTRUCTURE AND
NETWORK MANAGEMENT

Introduction to Use Case A
17
Low latency High latency
Low capacity High capacity
Mobility
Throughput
Latency
Availability
Reliability
Energy efficiency
Device cost
Device volume
Integrity
Availability
Reliability
Fundamental Challenges
• Fulfilment of end-to-end QoS requirements
• (Measurement of end-to-end QoS in live networks)
• Increased networks dynamics and system
complexity
• On-demand service provisioning
Main Objectives
• Automated service and infrastructure deployment
• Automated orchestration and optimization of services
• Profile infrastructure & network functions

+ Build trust with real applications and traffic (workload)
+ Measured results in selected scenarios
+ Real-time aspects, failures and tail-response
+ Profiling of infrastructure and applications
+ Prototyping & emulation of entities is possible
- Scale: Selected scope constrained by Lab(s)
- Time: Lead time to get long term results & HW changes
+ Scalable in scope with limited hardware
+ Scalable in time with short lead-time
+ Models for infra, apps & workload
+ Calculated output (simulation results)
- Approximations with selected granularity
+ Live & Real
- Don’t touch
- Proof /trust needed
- Availability of scenarios
- Availability of apps
Utilizing Testbed and Simulations to build Trust
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MME
RCF
UPF
Virtualization
VNFM
VIM
Communication
Service
Management
EMS
EMS
EMS
Hardware
MANOO&M
Radio Resources
Demand
Compute, Networking, Storage
Traffic
Generators
City Simulator
Optimization
Demand
Time
RECAP SimulationLab Network
(Testbed)
Optimization
Demand
Time
Models
&
Characteristics Workload Model
Infrastructure Model
Application Model
KPIs & Metrics KPIs & Metrics
User ModelUser Model
Live Network
Service Utilization Models
(High Level, Anonymous)
XL
KPIs & Metrics
Validation
Validation
Scenarios

2 Input: Realistic Artificial Data Models
• Buildings & roads (Open Street Map)
• Demographic data (Umeå Kommun)
• Household, work, commuting data
• Service usage data
• Radio network models
3 Tool: Data Driven User Simulation
• Mobility behavior
• Service usage behavior
• Service Categories
- eMBB (Best effort web, VoLTE,…)
- mMTC (IoT, …)
- cMTC (emerging use cases)
Confidential
Operational cells in blue
Idle users in red
Connected users within cell in cyan
World Model
Thing Model
Population Model
Mobility Model
Service Model
Radio Network Model
Application Model
Infrastructure Model
Traffic Generation to Train, Validate and Explore
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1 Goal: Explore Scenarios
• Realistic Artificial Telecom Workload
• Disaster scenarios
• Events
• Region expansion

20
NFV
Infrastructure
Application
(RAN/EPC VNFs)
Demand
Traffic
Generators
Workload
Model
Application
Model
Infrastructure
Model
RECAP Models
RECAP Operations
RECAP Management
Analytics
Application Orchestration
Infrastructure Optimization
Elastic
Scaling
Remediation
Simulation
Provisioning
Tieto’s Testbed RECAP Data Science
Regional User
Simulator
Infrastructure Landscape
Data
Collection
Analytics
Infrastructure
Probes
E2E
KPIs
Application
Metrics
Environment
System under Test
VIM
VNFM
Landscape
QoSModel
Instrumentation and control for the RECAP closed loop

Summary: Lessons Learned
• A repeatable methodology for generating the models
⁃ RECAP has developed a framework for this: there are tangible reusable artifacts
⁃ Configuration through the models
• Telemetry quality is important in a model-driven approach
⁃ Ensure right instrumentation of the system
⁃ Ensure telemetry quality
⁃ How do you do the training
⁃ Getting models right is the key
⁃ Very use case-specific è Hard to get a generic application model
• Validate the results of machine learning-based optimization
⁃ Human in the loop
⁃ Simulator in the loop
• DTS (large distributed systems)
• DES (fine-grained data center level)
• Separation of concerns
⁃ Infrastructure operator versus application operator
21

THANK YOU
http://recap-project.eu recap2020
RECAP Project ■ H2020 ■ Grant Agreement #732667
Call: H2020-ICT-2016-2017 ■ Topic: ICT-06-2016
THIS PROJECT HAS RECEIVED FUNDING FROM THE EUROPEAN UNION’S HORIZON 2020
RESEARCH AND INNOVATION PROGRAMME UNDER GRANT AGREEMENT NUMBER 732667
https://recap-project.eu/
https://recap-project.eu/about/public-deliverables/
Public Deliverables