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Managing Internet Protocol Television Data. — An interview with Stefan Arbanowski.

by Roberto V. Zicari on June 25, 2012

” There is a variety of services possible via IPTV. Starting with live/linear TV and Video on Demand (VoD) over interactive broadcast related apps, like shopping or advertisement, up to social TV apps where communities of users have shared TV experience”— Stefan Arbanowski.

The research center Fraunhofer FOKUS (Fraunhofer Institute for Open Communication Systems) in Berlin, has established a “SmartTV Lab” to build an independent development and testing environment for HybridTV technologies and solutions. They did some work on benchmarking databases for Internet Protocol Television Data. I have interviewed Stefan Arbanowski, who leads the Lab.


Q1.What are the main research areas at the Fraunhofer Fokus research center?

Stefan Arbanowski: Be it on your mobile device, TV set or car – the FOKUS Competence Center for Future Media and Applications (FAME) develops future web technologies to offer intelligent services and applications. Our team of visionaries combines creativity and innovation with their technical expertise for the creation of interactive media. These technologies enable smart personalization and support future web functionalities on various platform from diverse domains.

The experts rigorously focus on web-based technologies and strategically use open standards. In the FOKUS Hybrid TV Lab our experts develop future IPTV technologies compliant to current standards with emphasis on advanced functionality, convergence and interoperability. The FOKUS Open IPTV Ecosystem offers one of the first solutions for standardized media services and core components of the various standards.

Q2. At Fraunhofer Fokus, you have experience in using a database for managing and controlling IPTV (Internet Protocol Television) content. What is IPTV? What kind of internet television services can be delivered using IPTV?

Stefan Arbanowski: There is a variety of services possible via IPTV.
Starting with live/linear TV and Video on Demand (VoD) over interactive broadcast related apps, like shopping or advertisement, up to social TV apps where communities of users have shared TV experience.

Q3. What is IPTV data? Could you give a short description of the structure of a typical IPTV data?

Stefan Arbanowski: This is complex: start with page 14 of this doc. 😉

Q4. What are the main requirements for a database to manage such data?

Stefan Arbanowski: There are different challenges. One is the management of different sessions of streams that is used by viewers following a particular service including for instance electronic program guide (EPG) data. Another one is the pure usage data for billing purpose. Requirements are concurrent read/write ops on large (>=1GB) DBs ensuring fast response times.

Q5. How did you evaluate the feasibility of a database technology for managing IPTV data?

Stefan Arbanowski: We did compare Versant ODB (JDO interface) with MySQL Server 5.0 and handling data in RAM. For this we did 3 implementations trying to get most out of the individual technologies.

Q6. Your IPTV benchmark is based on use cases. Why? Could you briefly explain them?

Stefan Arbanowski: It has to be a real world scenario to judge whether a particular technology really helps. We did identify the bottlenecks in current IPTV systems and used them as basis for our use cases.

The objective of the first test case was to handle a demanding number of simultaneous read/write operations and queries with small data objects, typically found in an IPTV Session Control environment.
V/OD performed more than 50% better compared to MySQL in a server side, 3-tier application server architecture. Our results for a traditional client/server architecture showed smaller advantages for the Versant Object Database, performing only approximately 25% better than MySQL, probably because of the network latency of the test environment.

The second test case managed larger sized Broadband Content Guide = Electronic Program Guide (BCG) data in one large transaction. V/OD was more than 8 times
faster compared to MySQL. In our analysis, the test case demonstrated V/OD’s significant advantages when managing complex data structures.

We wrote a white paper for more details.

Q7. What are the main lessons learned in running your benchmark?

Stefan Arbanowski: Comparing databases is never an easy task. Many specific requirements influence the decision making process, for example, the application specific data model and application specific data management tasks. Instead of using a standard database benchmark, such as TPC-C, we chose to develop a couple of benchmarks that are based on our existing IPTV Ecosystem data model and data management requirements, which allowed us to analyze results that are more relevant to the real world requirements found in such systems.

Q8. Anything else you wish to add?

Stefan Arbanowski: Considering these results, we would recommend a V/OD database implementation where performance is mandatory and in particular when the application must manage complex data structures.

Dr. Stefan Arbanowski is head of the Competence Centre Future Applications and Media (FAME) at Fraunhofer Institute for Open Communication Systems FOKUS in Berlin, Germany.
Currently, he is coordinating Fraunhofer FOKUS’ IPTV activities, bundling expertise in the areas of interactive applications, media handling, mobile telecommunications, and next generation networks. FOKUS channels those activities towards networked media environments featuring live, on demand, context-aware, and personalized interactive media.
Beside telecommunications and distributed service platforms, he has published more than 70 papers in respective journals and conferences in the area of personalized service provisioning. He is member of various program committees of international conferences.

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2 Comments Leave one →
  1. In the interview 3 implementations are mentioned, including handling data in RAM, which is not described in the whitepaper. Any information about this would be interesting!

  2. Regarding your white paper:

    Was it intentional to compare with a so old MySQL version?.. there is MySQL 5.5 which is already GA since last year, and MySQL 5.6 which is available as labs release — both are way better than 5.0..

    Then, did you analyze your bottlenecks in MySQL?.. did you try to understand or to tune it? — using just a default settings as it is not a right thing to do when you’re expecting to reach some performance goals (and it’s true for any databases, not only MySQL)..

    As well, from a very few information you’re providing, I may suppose you’ve used MyISAM as storage engine for your tables.. – why did not you use InnoDB, which is high concurrent and truly transactional?..

    And then, if you’re considering in-memory solutions — why you did not try MySQL Cluster which is easily giving today 1M TPS even on a single node?..

    Well, looks very ugly for me as a comparison and its conclusion..


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