My topic is: Comparative performance analysis of SDN-based network and traditional network. So I decided to use mininet and already know how to perform some tests. However, I am wondering what tests would be better to choose (throughput, jitter, packet delivery ratio, latency, end packet delay, etc.) and how/where actually I can do tests for traditional network? NS2? What would be you suggestions? Maybe any useful links/tutorials?
Many thanks,
You should first use the same simulator to simulate the both networks types. The traditional and SDN are almost the same. The only difference is the management view and the flexibility.
You need first to:
set your goals from the study. Why you are performing this study? Has someone did this before? google scholar and check.
If some people have done this then think in some metrics or objectives they were missing and then start to think how to highlight them.
A good start for SDN research is always this paper (http://ieeexplore.ieee.org/document/6994333/).
Try to comment to let us know more in case this is not sufficient. I'm doing my PhD in SDN so I would like to help and exchange knowledge.
Related
Could anyone tell me which identifiers in WebRTC Statistics API are directly related to the quality of the experiences users have during connections?
This depends on the type of session. A videocall where many participants collaborate has different needs than an audio call where one talks any the others mainly listen.
In general the elements that impact the perceived quality are packetsLost, jitter, currentRoundTripTime, framesDropped, pliCount, framesDropped.
You should also consider that the bandwidth estimators adapt the bandwidth (and so the quality) based on the feedback from the other party.
If you search for "Quality of experience estimators for WebRTC" you'll find studies that use the above statistics to estimate the QoE.
I have a task: to determine the sound source location.
I had some experience working with tensorflow, creating predictions on some simple features and datasets. I assume that for this task, there would be necessary to analyze the sound frequences and probably other related data on training and then prediction steps. The sound goes from the headset, so human ear is able to detect the direction.
1) Did somebody already perform that? (unfortunately couldn't find any similar project)
2) What kind of caveats could I meet while trying to achieve that?
3) Am I able to do that using this technology approach? Are there any other sound processing frameworks / technologies / open source projects that could help me ?
I am asking that here, since my research on google, github, stackoverflow didn't show me any relevant results on that specific topic, so any help is highly appreciated!
This is typically done with more traditional DSP with multiple sensors. You might want to look into time difference of arrival(TDOA) and direction of arrival(DOA). Algorithms such as GCC-PHAT and MUSIC will be helpful.
Issues that you might encounter are: DOA accuracy is function of the direct to reverberant ratio of the source, i.e. the more reverberant the environment the harder it is to determine the source location.
Also you might want to consider the number of location dimensions you want to resolve. A point in 3D space is much more difficult than a direction relative to the sensors
Using ML as an approach to this is not entirely without merit but you will have to consider what it is you would be learning, i.e. you probably don't want to learn the test rooms reverberant properties but instead the sensors spatial properties.
i am currently working on an existing infrastructure where i have about a 1000 customer sites connected to about 5 different hubs. A customer site may connect to one or two hubs to ensure reliability but each customer site is connected to at least one hub. I want to ensure if the current system is the best or can be optimised to have better connection from customer sites to hubs, to help improve connectivity and reliability. Can you suggest good Optimisation Algorithms to look into?. Thank you
Sounds like you're doing some variation of the Facility Problem.
This is a well-known problem, and while there are algorithms that can solve for the global optimum (Djiskra's Algorithm, or other variants of Dynamic Programming), they do not scale well (i.e. you run into the curse of dimensionality). You could try this, but 1000 sounds already pretty big (depends on your exact problem formulation though).
I'd recommend taking a look at this coursera mooc Discrete Optimization. You don't have to take the whole course, but in the "Assignments" section of the video lectures, he also explains a variant of the Facility problem, some possible approaches to think about, and once you've decided which one you want to use, you can look deeper into that particular approach.
I am looking for a project idea in distributed processing on Unix based systems. I wish to use only the C programming language. I have to finish the project in 4 months and it's a part of my course work. Can someone help me with an idea?
Cryptography problems
Distributed Ray Tracer
Chess AI (really, AI for any game)
Large Prime Number Search
Web crawler or other search mechanism
Generic Problem Solver (push out problem definition on the fly, followed by problem data).
Note on the last one:
An example would be if you have a gaming website with lots of board games that you were coming out with all the time. You don't want to have to install new clients on all your servers every time you write a new AI for a board game, so you have a program which you can send new AIs to and then after that you can just send the game data and the pushed AI will be used to solve the problem. This is best used for problems which can be broken into smaller chunks.
It is hard to answer without knowing anything about performance, the scale of the project, what you are trying to accomplish, etc. For example, is it one task or multiple tasks? Is the project just totally open?
4 months is pretty short, but maybe some kind of physics problem or math problem. Sorting or some kind of database work might be dull but beneficial.
Check out mapreduce for ideas! I was really motivated by this work, personally.
We used distributed processing here at work, but it's such a broad field..
Yeah.
Why not write a distributed compiler. You may then present an interface for people to compile things on the fly, and it will be passed to your distribute compilenet. Java is probably well-suited, and you'll get to do fun things, like be very mindful of security and so on.
The BOINC project is always looking for help and is very interesting:
http://boinc.berkeley.edu/
If you want to leave your mark and change the way we search the web,
look into B-Trees.
B-Trees and offspring/variants are the working horse of the internet.
Google uses them extensively to index the web.
Database indexes/indices are B-Tree offspring/variants.
Every LAMP system uses a database and indexes/indices.
Also, they are used extensively in distributed VLDB (Very Large DataBases)
Perhaps you can improve existing distributed databases (Cassandra and HBase)
These are lofty goals, but for me, this would leave a lasting mark
in the way Web data is processed, indexed and stored.
Write a distributed, fault tolerant, redundant network B+Tree or B*Tree.
Read Drozdek's book Data Structures and Algorithms in C++.
It's a good survey of B-Trees.
Read about skip trees
http://www.cs.huji.ac.il/~ittaia/papers/AAY-OPODIS05.pdf
Read about Efficient B-tree Based Indexing for Cloud Data Processing
http://www.comp.nus.edu.sg/~ooibc/vldb10-cgindex.pdf
Google search "Network B+Tree"
https://www.google.com/search?rlz=1C1CHKZ_enUS431US431&sourceid=chrome&ie=UTF-8&q=Network+B%2BTree
I keep hearing from associates about grid computing which, from what I can gather, is highly distributed stuff along the lines of SETI#Home.
Is anyone working on these sort of systems for business use? My interest is in figuring out if there's a commercial reason for starting software development in this field.
Rendering Farms such as Pixar
Model Evaluation e.g. weather, financials, military
Architectural Engineering e.g. earthquakes.
To list a few.
Grid computing is really only needed if you have a lot of WORK that needs to be done, like folding proteins, otherwise a simple server farm will likely be plenty.
Obviously Google are major users of Grid Computing; all their search service relies on it, and many others.
Engines such as BigTable are based on using lots of nodes for storage and computation. These are commercially very useful because they're a good alternative to a small number of big servers, providing better redundancy and cost effective scaling.
The downside is that the software is fiendishly difficult to write, but Google seem to manage that one ok :)
So anything which requires big storage and/or lots of computation.
I used to work for these guys. Grid computing is used all over. Anyone who makes computer chips uses them to test designs before getting physical silicon cut. Financial websites use grids to calculate if you qualify for that loan. These days they are starting to replace big iron in a lot of places, as they tend to be cheaper to maintain over the long term.