1. Cyber Physical Systems (Green Networks) Prof. Nicholas Maxemchuk Manu Dhundi

2. Summary Energy-aware cooperative management of the cellular access networks of the operators that offer service over the same area. Temporal/ Spatial distribution of mobile traffic considered for Switch ON/OFF base stations. OPEX reduction and energy saved are main motives. Minimum QOS maintained. Network Engineers pitch in for “Green” operations. (Traditionally it has been hardware engineers and equipment manufacturers mostly)

3. The System N service providers cover the whole area Same QOS target for operators. Monotonically increasing / decreasing traffic model. Roaming-to-All (more energy saved but problems with switch-off transients) vs Roaming-to-One Energy cost C(i), in terms of kwh/ monetary terms. Equation for fraction of energy saved for switch off patterns

4. Numerical Results Sinusoidal Traffic Model More realistic traffic model (data collected from actual mobile usage) Network Unbalance across operators Different QOS for operators. 2 operators only considered.

5. The System n networks: N = {1,2,.., n} t : [0,2T] where T = 12h

6. The System (cont..)

7. Cost: fixed cost + cost that depends on number of network users. Former dominates over the later. Paper considers only either of the extreme cases at a time.

8. The System (cont..) Roaming: Roaming-to-All: Roaming-to-One:

9. The System (cont..) Switch Off Patterns:  All but 1 stations switched off  The paper has proved that switching off networks in order of increasing number of users gives optimum cost

10. Numerical Results Sinusoidal Traffic: Traffic Imbalance: a = 0 implies equal load, a = 1 implies max imbalance From calculations it has been shown that savings is more when b is greater because of steep traffic fall.

11. Numerical Results

15. Different QOS QOS can be blocking probability in case of circuit switched networks, or average delays in case of packet switched networks. To accommodate roaming, the better QOS (most stringent QOS) is provided by a network to all the users (whether roaming or not). For simplicity, the paper considers only 2 different QOS. Analysis done for average delay in a M/M/1 queues.

16. Numerical Results

17. My Additional Inference Is the paper hinting baby steps towards mobile infrastructure CLOUD ?? (There is a term virtualization used in conclusion)