OFC 2018 Paper on PON Capacity Auctions

We propose an economic-robust auction mechanism for multi-tenant PON’s capacity sharing that operates within the DBA process. We demonstrate that our mechanism improves PON utilization by providing economic sharing incentives across VNOs and infrastructure providers.
Screenshot 2018-11-20 at 12.18.38

We have simulated a multi-tenant PON market considering a10-Gbps symmetrical PON (i.e., XGS-PON). The simulation duration is 6 seconds, which allows us to average our results over 48,000 frames, each of 125μs duration. The PON is shared amongst 10 VNOs, each serving 10 Optical network units (ONUs). Although not reported, we have repeated the same simulations for different numbers of ONUs and VNOs, obtained similar results.

Our results are reported in Fig 2, comparing the three sharing mechanisms described above. Fig2.a shows the network utilization for an unbalanced load scenario (i.e., the mean of the traffic generated by the ONUs is assigned according to a random uniform distribution), confirms that our proposed economic-robust mechanism outperforms the Non-Sharing by achieving higher utilization across all offered loads. The Upper-Bound scenario reflects the case that with no trade reduction and, as a result, it increases the number of trades, leading to higher utilization. It is important to note though that the upper-bound is idealistic since without incentivizing VNOs to report their truthful value, they will likely manipulate their bids to achieve higher utility: the buyer VNOs shading their bids, and the sellers reporting higher untruthful values. This leads to a higher price per item from the sellers and lower offer per item from buyers leading to a natural reduction of trades. However, our results do not account for the manipulative bidding behavior of the VNOs. In Fig 2.b we report, for completeness, the scenario with the balanced load across the ONUs, although this is less realistic. As expected, although the trend is confirmed, the difference between the three mechanisms is much less remarked, as the number and value of the trades are far less when VNOs all have similar traffic. Fig 2.c compares, for the unbalanced load scenario, the average VNOs’ and InP’s utility against the average number of trades conducted during each frame using the proposed mechanism. We define the VNOs’ utility as the difference between their trading price and their valuation for the FU,. i.e. this determines how close is their final payment to their perceived value. The InP’s utility is the difference between the trading price of the seller and buyer VNOs, i.e., this reflects the price gap occurred due to the supply and demand ratio. Both Fig 2.c and Fig 2.d show that as we move to the right along the X-axis, the ratio of the demand to supply increases and, as a natural reaction, the market adapts by raising the price. As the number of trades increases, VNOs and the InP gain more utility. Once the overloading ratio exceeds the factor of 2, the VNOs become more demanding. At the same time, the supply declines and leads to fewer trades and eventually almost no trade when it reaches saturation as all the VNOs are asking for more than their negotiated share. By design, while the supply is higher than the demand the trading price is equal to the base price thus the utility of the InP remains zero. Once the demand grows over the supply, the price rises and the InP’s utility starts to grow. The InP’s utility is at its highest when the number of trades is maximum, and the average price of an FU is high.

Nima Afraz, Amr Elrasad, Marco Ruffini
In OFC 2018, 2018

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