We investigate how to build secure yet easy-to-use and cost-effective systems. Our research focuses on areas like improving security usability with contextual data, ensuring privacy in cloud services, and designing secure software-defined networking. We evaluate and optimize mobile and distributed systems to save energy and improve user experience of mobile cloud services and applications like crowdsensing, multimedia streaming, and edge/cloud computing. We also research topics in areas like mobile cloud gaming, internet of things, big data analytics, and verification of distributed systems.

1. Antti Ylä-Jääski Feb 12th 2016
Distributed Systems,
Mobile Computing
and Security

2. Secure Systems in a Nutshell
We investigate how to build systems that are simultaneously easy-to-use and
inexpensive to deploy while still guaranteeing sufficient protection.
Examples of research
questions:
• Can contextual data
on user devices help
improve security
usability?
• How can cloud
services ensure user
privacy?
• How can we design
secure software-
defined networking?
Contact: N. Asokan and Tuomas Aura
Usability Deployability/Cost
Security
Research Programs and funding:
Contextual Security (AoF), Cloud Security Services
(AoF), CyberTrust (Tekes), Mobile System Security
(Intel and Huawei)
More info:
Wiki: https://wiki.aalto.fi/display/sesy/Secure%20Systems
Blog: http://blog.se-sy.org/

3. Mobile Computing and Distributed Systems in a Nutshell
We evaluate and optimize the performance of mobile and distributed systems.
We build new applications and services for mobile devices and big data scenarios.
Sample research questions:
• How to save energy on
handsets and data centers
with SW optimisations?
• How to optimize user
experience for mobile
cloud services?
• How to apply mobile
crowdsensing to solve real
life problems (navigation)?
• How to efficiently collect
and utilize data from a
massive number of devices
connected to the Internet?
• How to build large scale
distributed systems for big
data in IoT and health?
Our current focus areas:
• Mobile cloud gaming
• Multimedia streaming
• Indoor navigation
• Crowdsensing
• Internet of Things
• Scientific, cloud, and
mobile edge computing
Contact: Antti Ylä-Jääski
Cloud
(e.g. Amazon EC2)
Mobile Edge
Computing

4. Mobile Cloud Gaming
In Mobile Cloud Gaming the game is
rendered on the cloud data center and
streamed to a mobile phone
• Latency is the main QoE issue in Cloud
Gaming
Virtual machines introduce overhead into
the system
• Linux containers are more light-weight
with native performance
Research questions:
• How to design a distributed mobile cloud
gaming system (server placement
strategy, virtualization)?
• How to model and predict end-to-end
latency with mobile access network?
• What is the effect of latency on gaming
experience?
4.12.2015

5. QoE Optimization of Mobile Video Streaming
4.12.2015
The extra energy expenditure caused by keeping the r
powered on while being idle with inactivity timer runnin
often called tail energy.
The amount of power drawn by the radio when rec
ing or transmitting data is also not const ant . It dep
mainly on the link quality in such a way that when th
ceived signal weakens, the mobile device uses more po
to amplify the transmitted signal. Note that this a↵
the energy consumed not only by data transmission but
by data reception because the mobile device continuo
transmits control information to the base station. We
Monsoon power monitor1
to measure the power consu
tion of a Samsung Galaxy S4 receiving data at di↵erent r
over LTE. The base station to which the device conne
to served no other clients because we used a non-comme
dedicated LTE network. We placed the device in a few l
tions showing di↵erent received signal strength (RSSI).
results plotted in Figure 2 clearly show the large e↵ec
the signal strength on the power.
rx data rate (Mbps)
0 20 40 60 80
powerconsumption(mW)
0
500
1000
1500
2000
2500
3000
-44 dBm
-75 dBm
-87 dBm
-102 dBm
-112 dBm
fitted model
power model:
P(r, s) = 887 + 1605
1+ e0.164∗(95+ s) + 6.51r + 0.2s W
Figure 2: Power drawn by smartphone when receiving
using LTE.
Figure 2 also plots results of a fitted model (dotted lin
• QoE modeling and optimization
• Analyze and (re)design on-demand and live mobile
video streaming systems
• Use adaptive protocols and scalable video coding
• Power modeling and optimization of video
delivery
• Optimal use of radio resources through smart
download scheduling
• No penalty in terms of video quality
HTTP server
Internet

6. Mobile crowd sourcing for indoor navigation
4.12.2015
• iMoon is an indoor navigation system
using sensor-enriched 3D models that
are created & maintained using crowd
sourced photos and sensor data
• iMoon provides image-based
localization and visual navigation
• iMoon user can be located with better
than 2 m position accuracy and 6
degrees facing direction accuracy

7. Internet of Things
• More than 30 billions of smart
objects will be part of the
Internet by 2020
– What are the consequences?
• Efficient data collection and
management are key issues
– User-friendly and scalable methods
to configure smart objects
– Energy-efficient data collection
– Modeling of large-scale networks
of smart objects
4.12.2015
IOT
AHEAD

8. Mobile Edge Computing
• Mobile Edge Computing (MEC) is a new industry initiative targeted to
implement novel services next to the end user in the mobile network
• In practice, an ordinary server component is integrated into the base
station providing cloud based computational and storage capacity
• Nokia’s solution is called RACS, which has been installed at our test lab
• We develop and evaluate performance of potential applications using
this platform like IoT data filtering, content acceleration and video
orchestration
4.12.2015
portion of resources can be reserved for video traffic.
Figure 10: Our solution lies at the network edge and com-
prises scheduler and shaper.

9. Green Big Data
Electricity has become one of the
main costs of computing
In cooperation with CERN we analyze
and improve the energy consumption
of scientific computing and massive
data analysis
• Analyze profiling and log data
• Model and predict power
consumption
• Develop energy-efficient
algorithms and solutions for
distributed computing
4.12.2015

10. Big Data Platforms for IoT and Health
4.12.2015
• Massive data volumes coming
from e.g., IoT, Genomics, Health,
and Social Networks require Big
Data platforms such as Spark
and Hadoop
• Our Hadoop-BAM is becoming
the de facto standard to process
NGS in parallel with Spark &
Hadoop. Library users: Halvade
(Gent), SparkSeq (ETH), SeqPig
(Aalto), SEAL (CNRS4), Adam
(Berkeley) and upcoming
parallized version of GATK
(Broad Institute)
• Health big data piloting with
HUS
IoT backend
architecture
Speedup
on 64
computers
with
Hadoop-
BAM

11. Automated Parallel Testing and Verification
• Traditional ways of testing and simulation do not
scale to validation of large distributed systems
• Model checking and automated testing are used
to find bugs in concurrent systems
• Our speciality: Automated symbolic and
parallelized methods for distributed systems
• Application areas: Safety critical systems (nuclear
automation with VTT), multithreaded programs,
hardware verification
• Organizing hardware model checking competition
2011-2015 with Prof. Armin Biere
• Visiting Professor in 2016: Prof. Roland Meyer
from Univ. Kaiserslautern – “Formal-Methods-
based Analysis of Geo-Replicated Big Data
Applications”
4.12.2015

12. 4.12.2015
Information-Centric Networking (ICN)
ICN
NAP
IP
NAP
ICN
Border
GW
IP-only
Sender
UE
IP (BGP)
IP
ICNF
IP
IP
FN
TM
L2
ICNPR
ICNRT
ICNTP
ICN
NAP
ICNF
IP
IP-only
Receiver
UE
IP-only
Sender &
Receiver
UE
L2
ICNSR
S1
S1
IP
TM : topologymanager
RVZ: rendezvouspoint
FN : forwarding node
S2
SDN
Switch
FN
SDN
Switch
RVZ
SDN
Controller
• In ICN we address information - not hosts
• The main applications of the Internet already
are information-centric by nature
• By making the underlying network information-
centric, we can better support modern
applications (e.g. IPTV) by the extensive use of
multicast and caching, making CDNs obsolete
• We are coordinating our third consecutive
ICN EU-project, the Horizon 2020 POINT,
which is bringing ICN from laboratories
to the real world
• POINT aims to show that current IP
applications can run better over an
information-centric core network