ch3 Flashcards

1
Q

Streaming vs. elastic traffic

Know what it is and understand the difference

A

Streaming traffic:

  • Require fixed amount of bandwidth
  • For instance, calls, streaming audio/video
  • Is there enough capacity yes/no
  • No real possibility to slow down
  • blocking probability big thing

Elastic traffic:

  • Data applications
  • Don’t require fixed throughput as they share bandwidth
  • If it gets crowded, it slows down.
  • no real notion of blocking
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2
Q

GSM planning problem

Understand the problem, how it leads to the Erlang-B model

A

How many base stations are needed to get a sufficiently low blocking probability.
We use the Erlang-B model to find the blocking probability

To boost capacity:

  • Denser frequency reuse
  • Smaller, more cells - Cellular densification
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3
Q

Evolution in mobile networks: from CSD to HS-CSD

Understand the translation into single- vs. multi-rate model

A

single-rate model uses 1 type of job/call/class

CSD (Circuit switched data):
- Uses a single channel

HS-CSD (High Speed - Circuit switched data):

  • Uses multiple connections in parallel
  • Large-file transfer, video-conferencing, etc.
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4
Q

Multi-rate model, product-form solution
Know what it is, understand the idea of the PF solution,
understand the scalability problem

A

K types of traffic
We try to get the blocking probabilities for each class
We use the multi-rate model to get these.
The classes may have different:
- mean call duration
- arrival times
- required capacity

We make a 2-dimensional markov chain will all the states in the state space
We can then get the state probabilities by making the balance equations where “rate out” = “rate in”
Solve the system of linear balance equations

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5
Q

Kaufman-Roberts recursion

Understand how it solves the scalability problem of the PF

A

When you have a really large state space, the product form solution becomes really large

Kaufman-Roberts recursion calculates the blocking probabilities
Looks at the total number of channels occupied

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6
Q

Elastic traffic
Understand what it is, how it relates to TCP and how it
naturally leads to Processor Sharing (PS) models

A

Mainly used for data applications
Not very delay-sensitive
No fixed throughput required: the flows share bandwidth

TCP traffic shares capacity in a similar way

Processor sharing models shares capacity equally

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7
Q

M/G/1 PS model

Know what it is, and what the insensitivity property means

A

If k customers in the system, then each of them gets processing speed 1/k

Insensitivity property:
- Result also holds for non-exponential service-time distributions

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