Distributed Systems, Mobile Computing and Security
Download as PPTX, PDF2 likes1,403 views
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.
Distributed Systems, Mobile Computing and Security
- 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