Virtualised e-Learning with Real-Time Guarantees on the IRMOS Platform

IEEE 2010
December 13 – 15, 2010
Perth, Australia

Virtualised e-Learning with Real-Time
Guarantees on the IRMOS Platform

Tommaso Cucinotta
Real-Time Systems Laboratory
Scuola Superiore Sant'Anna
Pisa, Italy

… and 12 others from 6 institutions:

Introduction

Tommaso Cucinotta – Real-Time Systems Lab (RETIS), Pisa, Italy – cucinotta@sssup.it

Introduction
 Towards a new computing paradigm
 Computing, network, storage in the cloud
 Not only batch computing and storage
 but also interactive real-time applications


Web and Clouds
Performance Today
 How much should I replicate my
infrastructure
 to meet desired
average QoS
levels ?
 General-purpose
technologies
 What about
the non-average
cases and interactivity?

The IRMOS Approach
to real-time and stable QoS


IRMOS
 Focus: Interactive Real-time Multimedia
on SOIs

Application Scenarios
SaaS

Framework Services
PaaS
Intelligent Service-Oriented
Networking Infrastructure
IaaS (ISONI)

IRMOS
Two-Phases Approach

Design
Tools
Benchmarking Application
Concretion Discovery
Negotiation

Modeling, Reservation
Mechanisms for
Mechanisms for
Methodology for the
Analysis,
Methodology for the
Planning precise allocationService
precise allocationof of
identification of
identification of resources
resources Instantiation
resource requirements
resource requirements to applications Service
to applications
Component
Configuration

Execution &
Monitoring
Cleanup

Offline 7


IRMOS
Two-Phases Approach

Design
Tools
Benchmarking Application
Concretion Discovery
Negotiation

Modeling, Reservation
Analysis,
Planning
Service
Instantiation

Service
Component
Configuration

Execution &
Monitoring
Cleanup

Offline 8


The IRMOS ISONI
Execution Environment

 Infrastructure for deployment of
 virtualized software components
 with stable and precise QoS guarantees

 Scheduling mechanisms
for temporal isolation
 Computing layer
 IRMOS Real-Time Scheduler for Linux
 Networking layer
 QoS-aware protocols (DiffServ, IntServ, MPLS, …)
 Storage layer

The IRMOS ISONI
Execution Environment


Deployment Problem
See our work @ SOCA'09
 Deployment of VSNs on PNs
 Given computing/network requirements
 Respecting end-to-end timing constraints
Physical Host
Physical Host
Computing Networking
Requirements Requirements Physical
Subnet

Physical
Link

Physical Host
Virtual Service Network

Maximum response-time Physical Host

Case-study: e-Learning


Real-time e-Learning
Synchronising real and virtual
worlds

Venue locations
Access devices
Interactive
Gallery media and communication
terminals Real time synchronization
Mobiles

Festivals
Interactive Virtual Worlds
whiteboards

Learning
Media generation
Remote locations
Professional
Museums
Social
Networking
Personal Classroom
Home

Mobile e-Learning
Architecture and model


Deploying e-Learning
 Goal Scheduling params
(budget, period)
 Deploy the e-Learning server
 With the application-level parameters
specified in the SLA
 Detail level

(i.e., resolution)
 Maximum number

of users
 Respecting the SLA QoS Physical Host
 Statistics on the

response-time of
individual requests mean Resol.
Resol. W x H x fps
W x H x fps
(mean, max, std dev) std-dev Users
Users 10
10
max


Modelling e-Learning
 Non-IRMOS world
 Network performance highly dependent
on traffic of other apps
 Computing performance highly
dependent on workload of other apps
 When deployed in IRMOS/ISONI
 QoS-aware networking and CPU real-time
scheduling limit the interferences
among different application instances
 Applications can be analysed in isolation


Modelling e-Learning
 Network delays: Erlang distributions
 Parameters fitted on benchmark data
 Computing delays
 Strong dependence on application-level
parameters (number of users, resolution)
 Black-box approach → Neural Networks


Benchmarking Data
 The real-time
scheduler
successfully
isolates
performance
of 2 VMUs


Benchmarking Data


Neural Network
Training
 After training, the
ANN successfully
outputs the mean
and standard
deviation of the
SC response time:
prediction error
less than 3%


Conclusions and future work


Conclusions
 The IRMOS/ISONI virtualized
infrastructure facilitates
 benchmarking & modelling
 off-line performance prediction
 on-line performance stability
 allowsfor better server consolidation levels
while meeting the timing constraints
 We showed the IRMOS way to deploy an
e-Learning application with precise
QoS guarantees

Future work
(WiP, actually)

 Apply the methodology to the
VirtualWorld e-Learning platform
 Apply the methodology to the other
IRMOS application scenarios
 Film post-production
 Virtual reality in automotive

 Model how the scheduler affects the
QoS metrics, to reduce the number
of configurations to benchmark

References
 T. Cucinotta, K. Konstanteli, T. Varvarigou, "Advance Reservations for Distributed
Real-TimeWorkflows with Probabilistic Service Guarantees", IEEE International
Conference on Service-Oriented Computing and Applications (SOCA 2009),
December 2009, Taipei, Taiwan
 K. Kostanteli, D. Kyriazis, T. Varvarigou, T. Cucinotta, G. Anastasi, "Real-time
guarantees in flexible advance reservations", 2nd IEEE International Workshop on
Real-Time Service-Oriented Architecture and Applications (RTSOAA 2009),
Seattle, Washington, July 2009
 F. Checconi, T. Cucinotta, D. Faggioli, G. Lipari, "Hierarchical Multiprocessor CPU
Reservations for the Linux Kernel", in 5th International Workshop on Operating
Systems Platforms for Embedded Real-Time Applications (OSPERT 2009), Dublin,
Ireland, June 2009
 T. Cucinotta, G. Anastasi, L. Abeni, "Real-Time Virtual Machines", in 29th Real-
Time System Symposium (RTSS 2008) -- Work in Progress Session, Barcelona,
December 2008
 YouTube Video on e-Learning performance isolation:
 http://www.youtube.com/watch?v=8FbHZ4ytNoQ
 IRMOS YouTube channel:
 http://www.youtube.com/user/irmosproject
 IRMOS Project Website: http://www.irmosproject.eu

Thanks for your attention

Questions ?


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