ch3 Flashcards
Streaming vs. elastic traffic
Know what it is and understand the difference
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
GSM planning problem
Understand the problem, how it leads to the Erlang-B model
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
Evolution in mobile networks: from CSD to HS-CSD
Understand the translation into single- vs. multi-rate model
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.
Multi-rate model, product-form solution
Know what it is, understand the idea of the PF solution,
understand the scalability problem
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
Kaufman-Roberts recursion
Understand how it solves the scalability problem of the PF
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
Elastic traffic
Understand what it is, how it relates to TCP and how it
naturally leads to Processor Sharing (PS) models
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
M/G/1 PS model
Know what it is, and what the insensitivity property means
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