Author Archives: Nima Afraz

ECOC 2018 Demonstration: VNF Implementation of the Virtual DBA

We demonstrate a VNF implementation of a sliceable PON architecture which has been optimised using DPDK data plane acceleration techniques. This gives Virtual Network Operators optimal control over capacity scheduling in a large scale multi-tenant PON environment.

Experimental Setup

Our demonstrator (see figure 1), implements a shared PON scenario with a number of real and virtualised ONUs, each with 3 T-CONTs. The main components within the testbed are: a physical PON, a set of emulated ONUs, a traffic generator and a multi-access edge computing node. The physical PON is based on one OLT and two ONUs (with the ONUs multiplexed into the same physical board), implemented on FPGA development boards13 offering 10Gb/s symmetric capacity. The emulated ONUs, running in software, are used to increase the number of users, and generate typical self-similar traffic. The traffic generator produces both real-time sensitive and best effort traffic flows (such as file transfer and video streaming) through the physical PON. Traffic flows are VLAN-tagged which are then mapped to specific TCONTs at the ONUs. Openstack runs the Network Function Virtualisation (NFV) implementation of the PON, running the virtual DBA and the merging engine. The Merging Engine is the element that merges all virtual bandwidth maps from the different VNOs generating one physical bandwidth Map allocation and the SDN control plane. The virtualisation node is logically composed of the Virtual Network Functions(VNFs), an Openstack virtualization platform, a DPDK Data Plane Acceleration toolset and an Orchestration and Control layer.

We have implemented the Merging Engine (ME) and the vDBA functions for the Virtual Network Operators (VNOs) as Virtual Network Functions (VNFs), allowing these functions to be instantiated and scaled independently. The virtualized infrastructure, shown in figure 1, leverages Single Root Input/Output Virtualization (SRI-OV) technology? and Open vSwitch6 with Data Plane Development Kit (DPDK) enhancements7. The DPDK offers a set of lightweight software libraries and optimized drivers to accelerate packet processing. It utilizes polling threads, huge pages, numa locality, zero copy packet handling, lockless queue and multi core processing to achieve low latencies and a high packet processing rate. Thus, all VNFs leverage the DPDK drivers and libraries to minimize the I/O and packet processing cost. The PCI Special Interest Group8 on I/O Virtualization proposed the Single Root I/O Virtualization standard for scalable device assignment. PCI devices supporting the SRIOV standard present themselves to host software as multiple virtual PCI devices, thus introduce the idea of physical functions (PFs) and virtual functions (VFs). The PFs are the full-featured PCIe functions and represent the physical hardware ports; VFs are the lightweight functions that can be assigned to VMs. The userspace VF driver for the merging engine VNF helps VM to directly access the FPGA interface, thus, provides near line-rate packet I/O performance. The OVS-DPDK replaces the standard OVS kernel data-path with a DPDK-based data-path, creating a user-space vSwitch on the host for faster connectivity between VMs. The OVS-DPDK ports have vHost user interfaces which allow user to fetch/put packets from/to the VMs. Furthermore, all the VNFs in different VMs employs para-virtualized interface that utilizes the DPDK userspace virtio poll mode driver to accelerate packets I/O from OVS-DPDK. Each of the VNFs used for VNOs implements vDBA mechanism, thus, have identical functionality in terms of packet processing. The VM running merging engine VNF has two interfaces – VF interface for packets I/O with FPGA interface, and the second one is virtio interface to communicate packets with OVS-DPDK switch. There are two directions in which traffic flows in this virtualized system: North/South and East-/West. In the North/South flow pattern, traffic is received from the network through FPGA interface and sent back out to the network. In the East-/West flow pattern, traffic is processed by a VNF and sent to another VNF through OVS-DPDK for further processing.


F. Slyne, R. Giller, J. Singh and M. Ruffini, “Experimental Demonstration of DPDK Optimised VNF Implementation of Virtual DBA in a Multi-Tenant PON,” 2018 European Conference on Optical Communication (ECOC), Rome, Italy, 2018, pp. 1-3.
doi: 10.1109/ECOC.2018.8535109
keywords: {Acceleration;Bandwidth;Passive optical networks;Merging;Engines;Real-time systems;Cloud computing},

OFC 2017 Paper in Antenna Spectrum and Capacity trade­off for Next Generation PONs

We propose a cost-optimal antenna vs. spectrum resource allocation strategy for mobile 5G MD-MIMO over Next-Generation PONs. Comparing wavelength overlay and shared wavelength approaches, split-PHY leads to solutions with higher mobile capacity than fronthaul.
Screenshot 2018-11-20 at 13.06.04.png

Figure 1:Optimal antenna numbers (al) and bandwidth (a2) for the fronthaul overlay model; optimal antenna numbers (bl) and bandwidth (b2) for the fronthaul shared wavelength model; optimal antenna numbers (c1) and bandwidth (c2) for the split-phy shared wavelength model. Overall system rate for the fronthaul (b3) and split-phy (c3) for the wavelength sharing model.

The results discussed in this section refer to a scenario where a number of users are randomly distributed in an area of 1km2. The scenario uses an average urban population density of 1,350 habitants and 570 dwellings per km2 (we take the city of Dublin, Ireland, as a reference); we analyse an MD-MIMO system with 20 active users and 64 RRHs. We have also simulated a city centre scenario with a density 4 times higher, scaling proportionally the number of active users and antennas. Since however the results were similar to those of the first scenario (Fig. 1), they are not reported in this paper. We assume the maximum spectrum bandwidth available is 50 MHz and the number of PONs (we assume a 64-way split) is enough to cover all dwellings and RRHs. We used exhaustive search in Matlab to solve the optimization problems previously described; the number of antennas used in each PON (mi) is determined by distributing uniformly the overall optimal number of antennas among the PONs.

The results presented in Fig. 1 show the optimal number of antennas and spectrum resources used for cost ratios of wireless spectrum to PON capacity (Rwb) and PON capacity to antenna site (Rbm) varying over several orders of magnitude. We have also attempted to estimate a potential reference value for RWb and Rbm, taking into consideration estimated costs for spectrum, antennas and PON channels. The cost of the spectrum (at 1.8GHz) was approximated to 0.1138 GBP per MHz per habitant for a 20-year lease, following data in [9]. The cost of antenna site leasing was chosen to be $1900 per month, according to [10]. The cost for leasing one of the eight NG-PON2 wavelengths was calculated at $1,510 per year, by carrying out a discounted cash flow model over costs reported in [11] (we considered 1% OPEX on passive and 4% on active infrastructure, a return on investment of 5% and a Weighted Average Cost Of Capital of 10%). All costs were brought back to a similar currency and normalized to one-year period; since we only consider cost ratios, we assume that similar ratios might still be valid when the lease time operates over much shorter time scales for highly dynamic resource allocation. The approximate reference value for Rbm is thus calculated at 0.066 (although, due to high variability of antenna site costs, we highlight in the plots a two order of magnitude shaded area from 0.006 to 0.6), while the approximate value for RWb is of 0.0065.

The plots are organized as follows: the first three in the upper line, (al), (b1) and (c1) report the optimal number of antennas from, respectively, the fronthaul overlay model, the fronthaul shared wavelength model, and the split-PHY shared wavelength model. In the lower line, (a2), (b2) and (c2) report the associated optimal spectrum bandwidth. Plots (b3) and (c3) in the last column report instead the wireless data rate, across all users, for the wavelength sharing model with, respectively, fronthaul (b3) and split-PHY (c3). The plots show that the higher Rwb, the higher is the sensitivity of the optimal number of antennas to Rbm: indeed low cost in optical transport facilitates the use of more antennas as cbdecreases over CW. From plot (al) we can see that for the fronthaul overlay model the reference value (between red and green curves) has low sensitivity to changes in the Rbmratio, meaning that the optimal strategy is to use the lowest numbers of antennas necessary for MD-MIMO. This is due to the high cost of optical transport with respect to spectrum, and only when this ratio changes considerably (i.e., by 100 times-blue curve) the system becomes sensitive to Rbm and the optimal strategy quite variable with it. When fronthaul is considered (b1), the situation does not change considerably within the reference shaded zone although the strategy becomes more sensitive to changes in Rbm and Rwb. The sensitivity becomes instead more pronounced for the wavelength sharing over split-PRY case (cl), as the red and green curves (i.e., around the reference value) become steeper for values near the shaded area. In this scenario in fact the split-PRY drastically reduces the C-RAN bit rate, which, combined to the ability to share PON wavelengths between multiple RRHs signals, lowers considerably the cost of optical transport. Thus for split-PRY the optimal MD-MIMO strategy is visibly dependent on the cost ratios, making resource allocation optimisation a necessity in dynamic markets where costs change with demand, or across different countries and geotypes.

Looking at the bandwidth plots in the lower line (a2), (b2) and (c2), we can see that while the spectrum used tends to decrease as more antennas are used, the relation is not strictly inversely proportional, because the model objective is the minimisation of the cost per bit. Moreover, the optimal bandwidth is less sensitive to Rbmfor the fronthaul overlay model, as the high optical transport cost makes it difficult to use more antennas. For split-PRY, the lower cost of optical transport allows instead the use of more antennas even when more spectrum is utilized, leading to an increase in the overall capacity (as visible in plot (c3)). Finally, the last column plots (b3) and (c3) show the overall MD-MIMO system rate (according to Shannon capacity) for fronthaul and split-PRY over shared wavelength models: the lower cost of split-PRY transport enables higher wireless data rates compared to fronthaul.


I. Macaluso, B. Cornaglia and M. Ruffini, “Antenna, spectrum and capacity trade-off for cloud-RAN Massive distributed MIMO over next generation PONs,” 2017 Optical Fiber Communications Conference and Exhibition (OFC), Los Angeles, CA, 2017, pp. 1-3.
keywords: {5G mobile communication;antennas;MIMO communication;next generation networks;passive optical networks;radio access networks;resource allocation;cost-optimal antenna;spectrum resource allocation;capacity trade-off;cloud-RAN massive distributed MIMO;next generation PON;mobile 5G MD-MIMO;passive optical networks;wavelength overlay;shared wavelength approaches;split-PHY;mobile capacity;fronthaul;radio access network;massive distributed multiple input multiple output system;Antennas;Passive optical networks;Bandwidth;Mobile communication;Wireless communication;Mobile computing;Dynamic scheduling},




Marco Ruffini: Human of CONNECT


Marco Ruffini, Assistant Professor at the School of Computer Science and Statistics and CONNECT Principal Investigator at Trinity College Dublin, gives some career advice…

How did you get to this point in your life?

Not by planning! I could never make or stick to a long term plan. I always made choices as they presented themselves. Often I ended up going for the more challenging option…I always tried to put myself out of my comfort zone, to the point where that became my only comfortable place. I probably found comfort in knowing that I wasn’t going to miss out on something by not being afraid to try.

Is there anything about life that you didn’t expect?

I never expected anything really and I never took for granted that I would get to the end of what I started. So every time I got there was a pleasant surprise. During my PhD I was always afraid I wouldn’t be able to get to the end of it, which pushed me to put a lot of extra work on it.

Tell us about an experience that taught you a life lesson.

A few times that I felt overly confident about something and I ended up making a fool of myself, once I did that in my final year in college in front of the entire class of my telecommunications lab.

I’ve now learned to be in the state of mind where if I notice that I’m feeling too confident about an event, such as a presentation or talk, I know that it’s not a good sign, and it means I need to explore it more because I’m surely missing something. Doubt is a powerful ally…it’s only through doubting myself that I get pushed to find out more, and that really helps in being able to creatively respond to unexpected questions or comments.

What do you think could be the next defining trend in technology?

I really don’t know, but here’s one thought. If we look at the past, successful inventions have been those that have either prolonged our life, increased our productivity or else made us feel more connected to others and to the world in general. If we look back, trains, automobiles, planes, radio, TV, Internet, Skype, social media, and the greatest technology of all times, pubs, are those that really are the centre of our lives and economy. I think there is more to come in enhancing our experience within our cities and local environment. Although there is a lot of buzz around this, I don’t think we have found the next big innovation in this area.

What’s the best piece of life advice you have ever received?

I don’t think I ever received any meaningful or useful verbal advice. But I learned a lot observing a few great people that I had the opportunity and pleasure to meet. One thing I learned is to be the first to make a generous move towards someone you meet through your professional or social environment. It goes a long way in creating a good and positive environment around you and it’s often paid back by even more generosity. Sometimes it isn’t, but it’s not an exact science and the challenge is to try and make it second nature.

Tell us about your research. What do you enjoy most about it?

I enjoy the independence: no one can tell you what you should do next and it’s only up to you to find out. I found this to be at the same time the most thrilling and scary part of academic work. Sometimes I think it would be much more comfortable to follow the directions of someone more senior without the apprehension of making an uncertain choice. But then who can say that would be the right choice? Plus as I mentioned I’ve learned to be more comfortable outside my comfort zone.

Two new principal investigators for CONNECT



Marco Ruffini and Anding Zhu have been appointed Principal Investigators at the CONNECT Centre in Trinity College Dublin, and University College Dublin respectively.

Anding is a Professor in UCD’s School of Electrical and Electronic Engineering, while Marco is Assistant Professor in Optical Network architectures in Trinity’s School of Computer Science and Statistics.

Congratulating both researchers, Professor Luiz DaSilva, Director of CONNECT, said:

“Marco and Anding have already established impressive research reputations. Marco Ruffini is an expert in the field of network consolidation and the convergence of mobile and optical networks. He is contributing to industry standards in this area, and leading a €1 million SFI Investigator project, O’SHARE, which involves collaboration with Vodafone and Intel.

“Anding Zhu’s research in physical layer, network-aware intelligent radio access nodes has resulted in several collaborative projects with industry partners including Xilinx, Analog Devices, MA-COM and Synopsys. Impactful engagement with industry is a hallmark of successful investigators in CONNECT.”

CONNECT is the world leading Science Foundation Ireland Research Centre for Future Networks and Communications. CONNECT is funded under the Science Foundation Ireland Research Centres Programme and is co-funded under the European Regional Development Fund. We engage with over 35 companies including large multinationals, SMEs and start-ups. CONNECT brings together world-class expertise from ten Irish academic institutes to create a one-stop-shop for telecommunications research, development and innovation.

Marco Ruffini elected Fellow of Trinity College


Dr Marco Ruffini, Trinity College Dublin

Congratulations to Marco Ruffini, CONNECT Funded Investigator and Assistant Professor in Trinity College Dublin, who has been elected a Fellow of Trinity College Dublin.

Research achievement of a high order is the primary qualification for Fellowship, coupled with evidence of a candidate’s contribution to the academic life of Trinity College Dublin and an effective record in teaching.

Congratulating Marco, Prof. Luiz DaSilva, CONNECT Director, said “This is a well-deserved recognition of Marco’s contribution to Trinity’s reputation for world class teaching and research. Telecommunications is one of Trinity’s research themes, and Marco is rapidly establishing a reputation as a world leader in the area of fiber broadband research.”

Marco’s research focuses on flexible high-capacity fiber broadband architectures, next generation PON testbeds, Software Defined Access Networks, and access network virtualization. He is currently leading a €1 million telecoms research project, OSHARE, which explores ways of improving the capacity of optical networks to cope with the surges in demand.

Dr Ruffini is a native of Ancona in Italy. He completed his undergraduate degree in Marche Polytechnic University in 2002. He then worked with Philips R&D, before completing his doctorate degree in Trinity College.

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