Author Archives: Nima Afraz

CONNECT papers accepted for OFC 2018

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CONNECT researchers have had several papers accepted for the Optical Fiber Communication (OFC) Conference in San Diego in March 2018. This is the most prestigious conference in the world of optical networks.

Marco Ruffini‘s team at Trinity College Dublin have had four papers and a demonstration accepted. The papers address SDN, network virtualisation, multi-tenancy and the convergence of fixed and mobile networks for 5G. Liam Barry and his team at Dublin City University have had two papers accepted.

The paper titles are:

DBA Capacity Auctions to Enhance Resource Sharing across Virtual Network Operators in Multi-Tenant PONs
Nima Afraz and Marco Ruffini, CONNECT, Trinity College Dublin.

Experimental Demonstration of SDN-controlled Variable-rate Fronthaul for Converged LTE-over-PON
Pedro Alvarez, Frank Slyne, Christian Blümm, Johann M. Marquez-Barja1Luiz A. DaSilvaMarco Ruffini, CONNECT, Trinity College Dublin. (1 – IMEC, University of Antwerp)

Joint Optimization of BBU Pool Allocation and Selection for C-RAN Networks
Yao Li1, Mariya Bhopalwala1Sandip Das2, Jiakai Yu1, Weiyang Mo1Marco Ruffini2, Daniel C. Kilper1.
1 – University of Arizona, 2 – CONNECT, Trinity College Dublin.

Demonstration of Real Time VNF Implementation of OLT with Virtual DBA for Sliceable Multi-Tenant PONs
Frank Slyne, Amr Elrasad and Marco Ruffini, CONNECT, Trinity College Dublin. This will also be accompanied by a demonstration.

Narrow linewidth hybrid InP-TriPleX photonic integrated tunable laser based on silicon nitride micro-ring resonators 
Yi Lin (1); Colm Browning (1); Roelof Bernardus Times (2); Douwe H. Geuzebroek (2); Chris G. H. Roeloffzen (2); Dimitri Geskus (2); Ruud M. Oldenbeuving (2); René G. Heideman (2); Youwen Fan (3, 2); Klaus J. Boller (3); Jialin Zhao(4); Liam Barry(1).
1. CONNECT, Dublin City University, Dublin, Ireland. 2. LioniX International, Enschede, Netherlands. 3. University of Twente, Enschede, Netherlands. 4. Huawei Technologies Co., Shenzhen, China.

Doubly Differential Two-level 8PSK for Enabling Optical Packet Switching in Coherent Systems 
Fan Liu (1, 2); Yi Lin (2); Anthony Walsh (2); Yonglin Yu (1); Liam Barry (2);
1. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China. 2. CONNECT, Dublin City University, Dublin, Ireland.

ONDM 2017 Paper on Virtual DBA

Frame Level Sharing for DBA virtualization in multi-tenant PONs in Multi-Tenant PON

 

Figure 2

Fig. 2:Frame level sharing architecture, sharing frames among VNOs.

Performance Evaluation

We developed a C++ XGS-PON simulator (e.g., using symmetric 10G upstream/downstream rates) and used it to simulate one OLT and 60 ONUs with maximum physical distance of 40 Km. The upstream capacity was set to 9.95328 bps, according to the standard. The Ethernet frame size for the packet load generator ranges from 64 to 1518 bytes with trimodal size distribution, as reported in [14]. We employed self-similar traffic with long range dependence (LRD) and Hurst parameter 0.8. The ONUs are divided equally among VNOs and all VNOs employ the GIANT [9] DBA algorithm with three T-CONTs, namely: assured, non-assured and best effort. We considered service intervals of 4, 8, and 8 frames respectively. The ONU buffer size is set to 3 MB. The offered load is uniformly distributed among ONUs and T-CONTs. We assume the total PON assured traffic capacity is divided homogeneously among VNOs, but they are allowed to exceed this figure with non-assured and best effort traffic.

Regarding the number of VNOs and offered load distribution, we consider three simulation scenarios as follows:

  • Scenario 1: we consider two VNOs with offered load divided equally among them.
  • Scenario 2: we consider five VNOs with offered load divided equally among them.
  • Scenario 3: we consider two VNOs with offered divided on a 1:2 ratio among them.

The performance of our FLS algorithm is tested against the SS framework [7], discussed in section II. In order to achieve a fair comparison between FLS and SS capacity sharing policies we use same number of VNOs and same service intervals (both mechanism are based on the GIANT DBA) and set the maximum service rate to the full XGS-PON capacity. The forgetting factor was set to 0.125 [7], while the minimum committed service rate was set equal to the share of the total PON capacity for each VNO. Our main performance metrics is the average packet delay, while we also investigate the frame loss rate.

A. Scenario 1

The average packet delay for our proposed FLS and the benchmark SS is shown in Fig. 5. It can be noted that both static and dynamic SS switching mechanisms have the same performance for assured, non-assured, and best effort traffic. Regarding FLS, both capacity sharing and no capacity sharing have very close performance. Capacity sharing has lower delay than no capacity sharing at high load for best effort traffic. This situation is reversed for non assured traffic. Comparing both SS and FLS, we find that FLS delay is significantly lower than SS delay by 50% for assured and non assured traffic. This statement is still true for best effort traffic for offered load below 9 Gbps. The minimum achieved delay by SS matches our note in Section II. Since the service interval is equal to 4 for assured traffic and there are two VNOs, then the assured T-CONTs are polled every 8 frames. Hence the minimum average delay is 12 frames (1500μs). The reported results in the original SS paper [7] show the same behavior. On the other hand, FLS allows assured T-CONTs to be polled every 4 frames. Hence FLS achieves 50% lower delay. The frame loss rate is similar in both SS and FLS, thus we do not report its plot.

Figure 5

Fig. 5:Average delay (scenario 1): (a) Assured bandwidth (b) non-assured bandwidth (c) best effort.

B. Scenario 2

In scenario 2, the number of VNOs is set to 5. There are three interesting points to note. First, in FLS for best effort traffic the capacity sharing policy shows significant lower delay at high load compared to no capacity sharing. Second, the minimum achieved latency for FLS is still the same as in scenario 1. This shows that FLS is more resilient to the number of VNOs compared to SS. Third, the minimum achieved latency of SS is increased by a factor of 2.5, since, as explained in the subsection above, it is proportional to the number of VNOs in the system. This shows that SS framework performance is highly dependent on the number of VNOs.

C. Scenario 3

In Scenario 3, the number of VNOs is set to 2, but the offered load of one VNO is twice that of the other VNO. The delay performance of the low loaded operator is shown in Fig. 7, while the high loaded one is shown in Fig. 8. Comparing assured bandwidth performance for both operators, we see that FLS achieve higher isolation than SS, as we notice that the assured bandwidth delay of the lower loaded operator is almost constant over the load range for both the capacity and no-capacity sharing policies. On the other hand for SS, the dynamic switching mechanism achieves increasing delay for the lower loaded operator and decreasing delay for the higher loaded operator at high offered load. This is because as the offered load increases, the SS layer (dynamic) assigns more upstream frames to the higher loaded operator. This leads to reduced delay for the higher loaded operator and increased delay for the lower loaded one. Regarding Best effort traffic, FLS capacity sharing policy achieves significant lower delay compared to no-capacity sharing policy and both SS switching mechanisms.

The frame loss rate is reported in Fig. 9. For the lower loaded operator, the SS dynamic approach increases the frame loss rate by a small amount. For higher loaded operator, the SS dynamic approach and FLS capacity sharing policy are more stable than the SS static approach and FLS no-capacity sharing policy. However, the FLS capacity sharing approach has the advantage of not raising either the lower loaded operator frame loss rate nor the average delay, thus providing again good isolation between the two VNOs.

Figure 7

Fig. 7:Average delay (scenario 3, low loaded VNO): (a) Assured bandwidth (b) non-assured bandwidth (c) best effort.

Figure 8

Fig. 8:Average delay (scenario 3, high loaded VNO): (a) Assured bandwidth (b) non-assured bandwidth (c) best effort.

Figure 9

Fig. 9:Scenario 3: frame loss rate (a) low loaded VNO (b) high loaded VNO.

Conclusion

In this work, we proposed a novel virtualized PON sharing architecture called Frame Level Sharing (FLS). FLS introduces the concept of virtualization by migrating and virtualizing the DBA function from the physical OLT (owned by the infrastructure provider) to a virtual PON slice controlled by the virtual network operator. FLS is designed to achieve upstream frame level sharing among VNOs while maintaining service isolation among them, by introducing a new sharing engine layer on top of the TC layer. The sharing engine is responsible for merging the received virtual bandwidth maps into the physical bandwidth map to be transmitted along with the downstream frame. Simulation results in balanced load scenarios shows that FLS achieves less delay compared to a benchmark scheme (the Slice Scheduler) found in literature, and a low dependency on the number of VNOs sharing the PON. In addition, even for non-balanced load scenario, FLS achieves excellent service isolation among VNOs.

A. Elrasad and M. Ruffini, “Frame Level Sharing for DBA virtualization in multi-tenant PONs,” 2017 International Conference on Optical Network Design and Modeling (ONDM), Budapest, 2017, pp. 1-6.
doi: 10.23919/ONDM.2017.7958528
keywords: {channel capacity;passive optical networks;frame level sharing;DBA virtualization;PON;fiber-to-the-premises access network;ubiquitous fiber infrastructure;passive optical networks;point-to-point solutions;virtual network operators;capacity scheduling;virtual dynamic bandwidth assignment;Passive optical networks;Bandwidth;Delays;Engines;Switches;Business;Scheduling algorithms},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7958528&isnumber=7958513

O’SHARE research popular online

A 5G research paper by Dr Marco Ruffini, Assistant Professor in Trinity College Dublin, has been among the Top 10 most viewed papers on the website of the Journal of Lightwave Technology (IEEE/OSA) for the past four months.

The paper is entitled ‘Multidimensional Convergence in Future 5G Networks‘ (Vol. 35, No. 3, March 2017).

In addition, two more papers involving Marco and other CONNECT researchers were among the most downloaded papers during the month of June from the website of the IEEE/OSA Journal of Optical Communications and Networking.

These papers are:

Access and metro network convergence for flexible end to end network design
IEEE/OSA Journal of Communications and Networking, Vol. 9, No. 6, June 2017.

End-to-end Service Orchestration From Access to Backbone
IEEE/OSA Journal of Communications and Networking, Vol. 9, No. 6, June 2017

TY Immersion Week at CONNECT

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CONNECT volunteers Nima and Jernej with Titi and Kome.

The CONNECT Centre at Trinity College Dublin welcomed Titilola and Kome, Transition Year Students at Larkin Community College in Dublin, for a week of coding and learning about communications.

The students were at CONNECT for TY Immersion Weekwhich aimed to explore the role coding plays in effective communications. Kome and Titilola spent the week working on a project entitled “Own your own Skype – WebRTC setup and test”.

Well done to project coordinator Jacek Kibiłda, and CONNECT volunteers Nima Afraz, Jernej Hribar and Stephen O’Farrell.

Marco Ruffini’s Software Defined Networks paper in Top 5

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Dr Marco Ruffini, CONNECT Funded Investigator at Trinity College Dublin, has led a team to victory at the OFCity Challenge at OFC in San Diego, California.

Marco led a team called “ALIVE” which included Thomas Pfeiffer (Nokia Bell Labs, Germany); Dave Hood (Huawei, USA); Junwen Zhang (ZTE, USA); Daniel King (Lancaster University, UK).

A list of the teams and their abstracts can be found here. Here is the ALIVE abstract:
The Augmented Living Experience (ALIVE) team is tackling the challenging problem of delivering highly predictive deterministic packet transport for high fidelity acoustic and video applications. Our use cases are based on the requirement to schedule connectivity and maintain specific low latency network guarantees for remote multi-site locations for real-time concert rehearsals, and sport streaming.
We achieve our objectives by employing a software-defined controller over a hierarchical multi-layer network architecture that utilises transparent metro optical switching, and strict-QoE traffic engineered packet paths across packet core infrastructure. Guarantees are provided via in-situ OAM monitoring and feedback to the controller.
Network connectivity is complemented with sound and augmented reality (AR) video processing and synchronisation methods, tightly interacting with the network controller which modifies QoE paths accordingly and ensures seamless protection switching.
Additional key contributions include integrated fiber-wireless access network and Geo-distributed Fog Computing technologies, which allow predictive network connectivity for time-sensitive and ultra-reliable communications such as: autonomous vehicles, localised content delivery and live augmented reality concert and sport streaming.
Team ALIVE will present a cost-effective solution for smart ultra-low-latency communication providing a long-lasting legacy to OFCity, demonstrating its technology leadership in the area of 5G smart cities for many years to come.

CONNECT researchers had several papers at this year’s OFC:

Avishek Nag, Yi Zhang, Luiz A. DaSilva, Linda Doyle, and Marco Ruffini, “Integrating Wireless BBUs with Optical OFDM Flexible-Grid Transponders in a C-RAN Architecture“. Accepted at OFC 2017.

Amr Elrasad, Nima Afraz, and Marco Ruffini, “Virtual Dynamic Bandwidth Allocation Enabling True PON Multi-Tenancy“. Accepted at OFC 2017.

A. Saljoghei, Arman Farhang, Colm Browning, Nicola Marchetti, Linda Doyle, and Liam Barry, “Investigation of the Performance of GFDMA and OFDMA for Spectrally Efficient Broadband PONs”. Accepted at OFC 2017.

Irene Macaluso, Bruno Cornaglia, and Marco Ruffini, “Antenna, Spectrum and Capacity trade-off for Cloud-RAN Massive Distributed MIMO over Next Generation PONs“. Accepted at OFC 2017.

Colm Browning, Alexander Gazman, Vidak Vujicic, Aravind Anthur, Ziyi Zhu, Keren Bergman, and Liam Barry, “Optical circuit switching/multicasting of burst mode PAM-4 using a programmable silicon photonic chip“. Accepted at OFC 2017.

OFC 2017 Paper: Introduction to DBA Virtualisation

We propose a virtual-DBA architecture enabling true PON multi-tenancy, giving Virtual Network Operators full control over capacity assignment algorithms. We achieve virtualization enabling efficient capacity sharing without increasing scheduling delay compared to traditional (non-virtualized) PONs.
Figure 2

Fig. 2:(a) Average delay, load divided by 1:1 (b) average delay, load divided by 1:2 (c) frame loss rate

The aim of our analysis is to show that the vDBA mechanism we propose does not cause additional delay to the capacity scheduling, while it gives full control to VNOs over their vDBA algorithm and is able to re-assign unused capacity among the VNOs.
Fig. 2 shows the average delay versus the offered load for the sharing capacity, nonsharing capacity and traditional (non-virtualized) PON as well as the frame loss rate. From Fig. 2.a and Fig 2.b, we can see that employing virtualization in the DBA does not affect the delay of the assured and non-assured bandwidth performance. For these two cases the plots show the same constant delay performance both for the traditional PON and the two virtualized PONs. Assured bandwidth is the most important to consider as it is the most likely to carry traffic with higher QoS requirements.
Regarding best effort traffic, in Fig 2.a we see that when the load increases towards saturation this experiences delay, which however is similar across traditional and virtual PONs (red, black and blue curves increase together). From Fig 2.b however we can see that when the traffic is unbalanced between VNOs, MT-PON with capacity sharing (blue) outperforms MT-PON with non-sharing capacity (red) and it performs similarly to a traditional PON (black). This advantage is also clear from Fig 2.c, showing that MT-PON with capacity sharing does not experience any noticeable frame loss when the load is unbalanced. On the other hand the non-sharing capacity MT-PON experiences noticeable loss rate (here we also show the case where the traffic is unbalanced by a factor of 3).
In conclusion, our approach to DBA virtualization has shown that it is possible to achieve true multi-tenancy in PONs, giving operators full control over capacity scheduling, without increasing delay performance and without wasting PON capacity when the load is unbalanced among the VNOs.
A. Elrasad, N. Afraz and M. Ruffini, “Virtual dynamic bandwidth allocation enabling true PON multi-tenancy,” 2017 Optical Fiber Communications Conference and Exhibition (OFC), Los Angeles, CA, 2017, pp. 1-3.
keywords: {optical communication equipment;optical fibre networks;passive optical networks;virtual dynamic bandwidth allocation;true PON multitenancy;virtual-DBA architecture;virtual network operators;capacity assignment algorithms},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7936877&isnumber=7936771