Network probability problem

In summary, the question asks for the probability that, in a peer-to-peer network with N peers and K replicas, with M simultaneous and independent peer failures, the remaining peers will contain at least one replica of each original peer. For the case where K equals M, the probability can be expressed as 1 minus the combination of N taken M. For the general case, it is a chain of probabilities similar to choosing M balls at random from a bag containing K black balls and N-K white balls.
  • #1
chrisg
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Homework Statement


I have a question that's stumped me for a bit. Suppose we have a peer-to-peer network that's used to store replicas of files (such as p2p networks do) that are replicated randomly. And suppose we have the following:

peer 1 has files 1, 3, 5
peer 2 has files 2, 4, 5
peer 3 has files 3 , 1, 2
peer 4 has files 4 , 2, 1
peer 5 has files 5, 3, 4

In the example above, we have 3 replicas, but for a system of N peers we could have up to N replicas of each peer with each peer containing the same number of replicas as any other peer.

The question is, with N peer, K replicas and M peer failures (suppose they drop out simultaneously but independently), what is the probability that the remaining peers contain at LEAST one replica of all of the original peers?


The Attempt at a Solution


Intuitively, I think that for the simple case with N peers, where the number of replicas (K) == number of failures (M), we can express the probability of having at least one replica of each peer remaining as:
1 - (N/Choose(N,M))

For the general case, however, I'm stumped. I suspect that it's a chain of probabilities, but I'm not positive on that one. Any suggestions would be appreciated.

Thanks!
 
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  • #2
It might be easier to think about just one file being replicated.

In that case, you have K servers that have the file and N-K that do not.

It's the same type of problem as "choosing M balls at random from a bag containg K black balls and N-K white balls"
 

What is a network probability problem?

A network probability problem is a mathematical problem that involves calculating the probability of certain events or outcomes in a network or graph. This network or graph can represent various systems, such as computer networks, transportation systems, social networks, etc.

What are some common applications of network probability problems?

Network probability problems have various applications in different fields, including computer science, engineering, social sciences, and more. Some common applications include predicting network failures, optimizing network performance, analyzing the spread of diseases in a population, and determining the most efficient routes in transportation networks.

What techniques are used to solve network probability problems?

There are various techniques used to solve network probability problems, such as graph theory, Markov chains, and Monte Carlo simulation. These techniques involve representing the network as a mathematical model and using probability theory to analyze and solve the problem.

What are the challenges of solving network probability problems?

One of the main challenges of solving network probability problems is the complexity of the networks involved. As the size and complexity of the network increase, the calculations and analysis become more challenging. Additionally, obtaining reliable data for the network can also be a challenge in some cases.

How can network probability problems be useful in real-life situations?

Network probability problems can be useful in various real-life situations, such as predicting and preventing network failures, optimizing resource allocation, and improving the efficiency of systems. They can also help in understanding the behavior of complex systems and making informed decisions based on the calculated probabilities.

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