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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 2 — Jan. 28, 2013
  • pp: 2083–2096
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Delivery of video-on-demand services using local storages within passive optical networks

Sandu Abeywickrama and Elaine Wong  »View Author Affiliations


Optics Express, Vol. 21, Issue 2, pp. 2083-2096 (2013)
http://dx.doi.org/10.1364/OE.21.002083


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Abstract

At present, distributed storage systems have been widely studied to alleviate Internet traffic build-up caused by high-bandwidth, on-demand applications. Distributed storage arrays located locally within the passive optical network were previously proposed to deliver Video-on-Demand services. As an added feature, a popularity-aware caching algorithm was also proposed to dynamically maintain the most popular videos in the storage arrays of such local storages. In this paper, we present a new dynamic bandwidth allocation algorithm to improve Video-on-Demand services over passive optical networks using local storages. The algorithm exploits the use of standard control packets to reduce the time taken for the initial request communication between the customer and the central office, and to maintain the set of popular movies in the local storage. We conduct packet level simulations to perform a comparative analysis of the Quality-of-Service attributes between two passive optical networks, namely the conventional passive optical network and one that is equipped with a local storage. Results from our analysis highlight that strategic placement of a local storage inside the network enables the services to be delivered with improved Quality-of-Service to the customer. We further formulate power consumption models of both architectures to examine the trade-off between enhanced Quality-of-Service performance versus the increased power requirement from implementing a local storage within the network.

© 2013 OSA

1. Introduction

In recent years, the deployment of passive optical networks (PONs) has resulted in significant growth of on-demand services over the access segment. Studies have forecasted that global Video-on-Demand (VoD) traffic will triple by 2016 [1

1. “Cisco visual networking index: Forecast and methodology, 2011 – 2016,” http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360.pdf.

]. The amount of VoD traffic in 2016 will be equivalent to 4 billion DVDs per month [1

1. “Cisco visual networking index: Forecast and methodology, 2011 – 2016,” http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360.pdf.

]. Providing VoD streams whilst maintaining the required Quality-of-Service (QoS) levels will be especially challenging during peak hours with potentially hundreds of customers of a single PON, watching the same video. Furthermore, research is currently being carried out to enhance the Quality-of-Experience (QoE) for the customer with the aid of higher resolution screens, 3D effects, and higher definition audio features. Forecasts in [1

1. “Cisco visual networking index: Forecast and methodology, 2011 – 2016,” http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360.pdf.

] further predict that by 2016 High Definition (HD) internet video will comprise 79% of the global VoD. Therefore, it is to be expected that video file sizes will become larger, thus raising the bandwidth requirements to suit their functions. QoS requirements concerning network parameters for different classes of services provided over IP networks including VoD have been specified in the ITU-T and ETSI recommendations [2

2. “End user multimedia QoS categories,” ITU-T Recommendation G.1010 (2001).

4

4. “Quality of Service ranking and measurement methods for digital video services delivered over broadband IP networks,” ITU-T recommendation J.241 (2005).

]. Further, it has been shown that customers are demanding even higher QoS attributes in studies conducted using techniques such as user opinion scoring systems [5

5. P. Dymarski, S. Kula, and T. N. Huy, “QoS conditions for VoIP and VoD,” J. Telecommun. Inf. Technol. 3, 29–37 (2011).

]. Therefore, the minimum QoS requirement levels can be expected to be more stringent in years to come.

In this paper, we study and compare the QoS attributes and power requirement of PON with, and without the use of the above-mentioned LS. We carry out packet level simulations for two architectures whereby VoD services are delivered with and without the presence of LS within the PON. Further, we introduce a dynamic bandwidth allocation (DBA) algorithm to optimize packet delivery in the LS PON with the aim of enhancing QoS levels. We formulate power consumption models for the two architectures to analyze the additional power requirement that may have been introduced to the PON by the LS equipment. The power consumption of a LS is attributed to the processing and video storage of LS server and storage arrays respectively. The QoS and power consumption values are then critically analyzed to study the trade-off between the QoS performance and the network power consumption.

2. Video-on-demand over passive optical networks

3. Dynamic bandwidth allocation algorithm for local storage PONs

According to the IEEE 802.3ah standard, the DBA is left open for vendor implementation. The formats of control frames are adjustable to suit the services delivered by the PON. Here, we modify the frame format of both GATE and REPORT Multi-Point Control Protocol (MPCP) control frames. In the REPORT control frame message, one octet is allocated to carry the requested video identity number from the ONU to the OLT. The format of the REPORT control frame is adjusted to accommodate the VoD service parameter, the Requested Video ID, as highlighted in Fig. 2
Fig. 2 Format of the REPORT control frame with a modified field for the Request Video ID.
. The opcode specific fields, which can carry a multiple number of report bitmaps and queue sizes, depending on the number of queue sets, are generally allocated 40 octets by the standard. One octet is taken out from this set to be used as the Requested Video ID carrier to the OLT. This adjustment will eliminate one cycle from the initial request communication between the ONU and OLT, which in return will directly help improve the delay performance of the network.

4. QoS analysis and discussion

The impact on QoS attributes from placing a LS within a PON is presented in this section. In this work, we consider delay, jitter, and downstream bandwidth availability to be the main QoS attributes for VoD services. The QoS attributes of a VoD service over a PON mainly relies on the speed of video contents reaching the client ONU as quickly as possible and with minimal jitter. From a vendor’s point of view, decreasing the response time is considered very important for such video delivery schemes. The response time is highly dependent on packet delays. As explained in Section 3, our proposed architecture reduces the initial packet delay by one cycle time. Further, packet delays are attributed to transmission delays, queuing delays and processing delays. However, we consider in this work that the processing delay is negligible when compared to transmission and queuing delays. Hence, we ignore processing delays in our delay measurements. In accordance to the proposed architecture, when a LS that is located within the PON is transmitting video packets on λS to a ONU, the transmission delay will be significantly reduced as compared to the case when video is transmitted from the OLT. This is mainly due to the difference in transmission distances in the two cases. In our simulations, the OLT is considered to be 20 kilometers away from the remote node yielding a round trip transmission delay of 200 μs. The LS which is located at the optical splitter is placed 1 kilometer away from the ONU, giving rise to a round trip time of only 10 μs. This reduction is more significant in long-reach deployments whereby the round trip transmission delay between the OLT and ONU could be up to 1 ms (i.e. 80-100km between OLT and ONU). The queuing delay is the duration a packet spends at buffer queues of transmitting equipment. This value depends heavily on the buffer queue sizes of the OLT and LS. We consider exact buffer sizes in both OLT and LS server. Due to this usage of exact buffer sizes, and the fact that a majority of traffic is downstream from either the OLT on λD, or from LS on λS, queuing delay should converge to similar final average values in both cases.

As it is important to comprehend the practical aspects of how individual packets behave during the transmission, we carry out packet level simulations of the PON with and without LS to study its impact on the QoS attributes of VoD services. The network and protocol parameters used in our simulations are listed in Table 1

Table 1. Network and Protocol Parameters

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. The simulation programs were coded in C# and executed in a computer with .NET framework 3.5 as the run-time environment. The simulations were carried out to measure the resulting delay, jitter, and available downstream bandwidth values for the two above mentioned architectures. In order to improve the comparability of results, two extremes were simulated where in the first case all requests are serviced by the OLT at the CO and in the second case all requests were serviced by the LS.

Further, to improve the granularity of the results, the number of active ONUs using the VoD service at a given time is varied from 1 to 32. The results thus represent how the QoS attributes behave under different conditions of network load. The simulation time for each run was chosen to be 5 seconds which is equivalent to 2500 cycles (cycle time = 2ms).

The packet delay values obtained by simulating the two cases are plotted in Fig. 6
Fig. 6 Delay as a function of number of active ONUs.
. The flat line represents the average delay values measured at the ONU when the VoD requests are solely handled by the OLT. These values do not vary with the number of active ONUs due to the time division multiplexed nature of the downstream transmission. That is, the time window for an ONU transmission is constant irrespective of the number of active ONUs. The dotted line represents the average delay values from the packets received by the ONUs on λS from the LS. Since the LS assigns timeslots in which duration is dependent on the number of active ONUs, the delay varies with different numbers of active ONUs along the horizontal axis. Nonetheless, results show that irrespective of how many ONUs are active at any given time during the day, the delay attribute observed by the ONUs is lower when the LS is present in the PON. As discussed, this improvement in the delay performance when LS is present, will be increasingly significant as the distance between the OLT and ONUs increases.

Figure 7
Fig. 7 Jitter as a function of number of active ONUs.
plots the jitter values whereby the continuous flat line and the dotted line represent values measured from the VoD packets delivered from the OLT on λD and from LS on λS, respectively. We define jitter as the variation of packet delays from their mean valueover a period of time. This variation is minimal when VoD is serviced from the OLT since the downstream transmission is a continuous process. The queuing delay of video packets will converge from the initial packet, thus making the total delay a steady value. However, in the case of LS, the LS will have to adjust its downstream timeslots on λS every time the number of serviced ONUs changes. This dynamic adjustment will interrupt the smooth packet flow on λS creating a variance from the mean delay. This will cause the LS transmissions to have higher jitter levels compared to the downstream delivery of VoD from the OLT, as indicated in Fig. 7. Jitter levels from using the LS (dotted line) is always greater than that from using the CO (flat line), irrespective of number of active ONUs. However the use of a jitter buffer at each ONU will enable such delay variations to be offset even in the worst case in which the recorded jitter is approximately 0.25 ms. In literature, jitter levels of less than 50 ms is considered to be acceptable for high-definition VoD delivery over the Internet [12

12. Y. Chen, T. Farley, and N. Ye, “QoS requirements of network applications on the Internet,” IOS Press, Systems Management 4, 55–76 (2004).

]. Therefore, the observed jitter from our simulation results satisfies this requirement.

The available bandwidth on λD for both architectures were also measured and plotted in the graph shown in Fig. 8
Fig. 8 Percentage of available downstream bandwidth for services other than VoD.
. The continuous line represents the percentage of available bandwidth in the case where the OLT services the video requests. This trend is due to the time division multiplexed scheme used for downstream transmission. When the number of active ONUs is increased, more timeslots will be utilized for distribution of VoD contents, thus decreasing the bandwidth available for other services. However, in a case where only one ONU is downloading a video and the rest is idle, the OLT will still use the dedicated timeslot to transmit video content, thus wasting a significant portion of downstream bandwidth. With the use of LS, bandwidth utilization of λD and λS is more efficient. The top and bottom dotted lines represent the bandwidth availability on λD and λS respectively in the case where the LS is servicing the video requests. Bandwidth on λD is almost fully available (~99.9%) for other downstream services since the LS is servicing all VoD requests on λS. Note that a very small level of bandwidth on λD is used for the transmission of GATE control frames even when no downstream video is transmitted to the ONUs. This continuous transmission of GATE control frames is required to maintain communication between the OLT and ONUs. The dotted blue line in Fig. 8 represents the available bandwidth on λS when the LS is transmitting the video content. The availability is small (~0.8% to 1.5%) due to the optimal utilization of bandwidth on λS for VoD delivery. The observed fluctuation of available bandwidth on λS as a function of increasing number of active ONUs is due to an unused remainder of bandwidth per timeslot. The effect of such unused bandwidth on delay and jitter is explained in detail using the results in Fig. 9(a)
Fig. 9 (a) Delay and utilization(b) Jitter and utilization for the case when downstream VoD is from local storage (LS).
and 9(b).

5. Power consumption for VoD delivery over PONs using local storage

In this section, we study the impact on the power requirement of the PON due to the introduction of a LS in the architecture. We present models of power consumption per customer for VoD delivery over the two architectures, with and without LS. As discussed, the LS equipment connected to the splitter comprises a video server and the storage arrays that contain video data. Power consumption of the LS is attributed to the power requirements of the equipment used. We have previously published power consumption models for VoD delivery over 10 GE-PONs [13

13. E. Wong, M. Mueller, M. P. Dias, C. A. Chan, and M. C. Amann, “Energy-efficiency of optical network units with vertical-cavity surface-emitting lasers,” Opt. Express 20(14), 14960–14970 (2012). [CrossRef] [PubMed]

] to study the impact of energy-efficient ONUs. To maintain consistency and comparability, we consider the same network equipment used previously in the models presented in this work. Table 2

Table 2. Equipment Specifications

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lists the equipment specifications used for formulating our power consumption models of this work. The power consumption per customer values for the proposed PON architecture is given by:

Pcustomer(W)= PCstorageN+PCserverN+K PLstorageN+K PLserverN+ K POLTN+ PONU= 1N (V ×M central× 17pW/bit)+ 1N(B×Ncentral× 70W/Gbps) + KN(V × M local× 17pW/bit) + KN(B × Nlocal× 70 W/Gbps) + KPOLTN+ PONU
(1)

In the architecture without LS, the video server and storage arrays are located at the CO and all video requests are served by the CO video server. The power consumption per customer values for the PON architecture is given by:

 Pcustomer(W)=PCstorageN+PCserverN+ K POLTN+ PONU= (V ×M × 17 pW/bitN)+ (B×Nactive× 70 WGbpsN)+ K POLTN+PONU
(2)

Equation (2) describes the power consumption per customer for downstream VoD delivery from the CO. In (2), parameters V, N, K, and B are the same as in (1). Parameter M is the number of videos in CO storage and the parameter Nactive is the number of active ONUs connected to the server.

6. Power consumption analysis & discussion

The impact of implementing the LS within the PON on the power consumed per customer can be observed from the results shown in Fig. 10
Fig. 10 Power consumption per customer against different percentage of number of videos stored at LS and without LS.
. Curves of power consumed per customer from architectures with LS and without LS both show similar trends. Results from a range of percentage of videos stored in the LS, were considered. As the network scales, the power contribution from the video server, storage arrays and OLT chassis is shared amongst many ONUs thus reducing the power requirement per ONU towards saturation. Further, results indicate that the power consumption in the case of the LS architecture is always higher without LS, irrespective of the percentage of the number of videos stored in the LS. This is expected because the equipment in the LS will consume additional power. Figure 10 shows that as the percentage is increased, the curves are shifted upwards attributing to the additional power consumed by the LS storage arrays to store additional videos. Additionally, curves in Fig. 10 indicate periodic sharp edges along the horizontal axis as the number of ONUs increases. These edges are due to the additional power requirement of periodically added OLT line cards to support the increase of ONUs. As a single OLT line card can support only 32 ONUs, every 33rd ONU added to the network necessitates a new OLT line card. This periodic addition causes the above mentioned sharp increments in the power values, thus resulting in periodic edges along the curves. As highlighted in Fig. 10, we consider 5% (average of 1% - 10%) as the percentage of videos stored in the LS. We compare the power requirement of the two architectures further in Fig. 11
Fig. 11 Power consumption per customer and percentage of power increment as a function of number of active ONUs.
to study the power tradeoff in detail.

Figure 11 compares the power consumption per customer of both considered architectures along with the percentage of power consumption increment. At N = 1024, results indicate a power consumption level of about 11.6 W for the architecture with LS and 7.5 W for the architecture without LS. The percentage of increment of power consumption between the two scenarios, i.e. percentage difference between the two power values, is also plotted. At N = 1024, the plot indicates a percentage of power consumption increment of 53.8% due to the addition of the LS equipment in the network. The percentage of increment of power consumption can be viewed as a trade-off for achieving higher bandwidth availability in the LS architecture. The percentage of power increment of 53.8% can be reduced to 32.6% with a compromise of QoS through increasing the number of OLT line cards to 64, each supporting 64 ONUs, and thus increasing the maximum network size to 4096 ONUs. With this adjustment, the average delay and jitter values will rise to 2.208 ms and 0.33 ms respectively. These values are increased mainly due to downstream bandwidth being shared over moreONUs. Even though the increment of jitter seems significant, the final average jitter value of 0.33ms is still marginal enough to be alleviated by use of a jitter buffer at each ONU.

Alternatively, the power consumption in the LS architecture can be reduced by replacing the two receiver modules in each ONU by one tunable receiver module. This tunable receiver is capable of tuning between λD and λS. Such a receiver configuration potentially provides energy-savings through the elimination of one receiver module per ONU but at the expense of increased delay and jitter, and the capability to simultaneously receive packets on λD and λS. We are currently performing a comparative study of the LS architecture with two receiver modules per ONU and that with one tunable receiver per ONU, and we endeavor to report the results from this study in a future journal publication.

7. Summary

In this paper, we addressed the efforts in enhancing the QoS attributes of VoD customers by using local storage within the access network. We conducted packet level simulations to study the delay, jitter behavior, and the available downstream bandwidth for the customer ONUs. Results were subjected to thorough analysis in an attempt to study the implications of local storage on the QoS of VoD delivery. Results indicate that delay is reduced by the reduction in transmission delay, since the local storage is placed within the PON. The improvement in delay performance is expected to increase with increasing distance between the OLT and ONUs, especially in long-reach PON implementations However, with local storage, jitter values are increased. Nonetheless, this increment could be considered marginal enough for the jitter buffers to alleviate and to provide a smooth playback. Most importantly, each customer ONU receives content on an additional bandwidth channel on λS, doubling its available downstream bandwidth.

Further, we analyzed the power requirement introduced to the network through the use of the local storage equipment. The increment of power consumption per customer of 10.4% can be considered as the trade-off for significantly improved bandwidth availability and lower delays. The percentage increment of power can be reduced by scaling up the size of the network, resulting in minor compromises in the QoS values that are still within the QoS levels specified in the ITU-T and ETSI recommendations.

Acknowledgments

The authors would like to acknowledge Chien Aun Chan and Chamil Jayasundara for their helpful discussions on the formulation of the power consumption models and the video popularity tracking algorithm, respectively.

References and links

1.

“Cisco visual networking index: Forecast and methodology, 2011 – 2016,” http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360.pdf.

2.

“End user multimedia QoS categories,” ITU-T Recommendation G.1010 (2001).

3.

“Speech and Multimedia Transmission Quality (STQ); Audiovisual QoS for communication over IP networks,” ETSI ES 202, 667 (2009).

4.

“Quality of Service ranking and measurement methods for digital video services delivered over broadband IP networks,” ITU-T recommendation J.241 (2005).

5.

P. Dymarski, S. Kula, and T. N. Huy, “QoS conditions for VoIP and VoD,” J. Telecommun. Inf. Technol. 3, 29–37 (2011).

6.

D. De Vleeschauwer and K. Laevens, “Performance of caching algorithms for IPTV On-Demand services,” IEEE Trans. Broadcast. 55(2), 491–501 (2009). [CrossRef]

7.

J. Baliga, R. Ayres, K. Hinton, and R. Tucker, “Architectures for energy-efficient IPTV networks,” in Proceedings of OFC 2008, paper ThQ5 (2008).

8.

C. Jayasundara, A. Nirmalathas, E. Wong, and N. Nadarajah, “Energy-efficient content distribution for VoD services,” in Proceedings of OFC/NFOEC 2011, paper OWR3 (2011).

9.

C. Jayasundara, A. Nirmalathas, E. Wong, and C. A. Chan, “Improving energy efficiency of Video on Demand Services,” J. Opt. Commun. Netw. 3(11), 870–880 (2011). [CrossRef]

10.

C. Jayasundara, A. Nirmalathas, E. Wong, and N. Nadarajah, “Popularity-aware caching algorithm for Video-on-Demand delivery over broadband access networks,” in Proceedings of IEEE GLOBECOM, 1 – 5 (2010).

11.

“IEEE Standard for Information Technology – Telecommunications and information exchange between systems – Local and metropolitan area networks – Specific Requirements – Part 3: Carrier sense Multiple Access with Collision Detection (CSMA/CD) access method and physical layer specification,” ANSI/IEEE Standard 802.3 – 2002, http://standards.ieee.org/getieee802/download/802.3-2002.pdf.

12.

Y. Chen, T. Farley, and N. Ye, “QoS requirements of network applications on the Internet,” IOS Press, Systems Management 4, 55–76 (2004).

13.

E. Wong, M. Mueller, M. P. Dias, C. A. Chan, and M. C. Amann, “Energy-efficiency of optical network units with vertical-cavity surface-emitting lasers,” Opt. Express 20(14), 14960–14970 (2012). [CrossRef] [PubMed]

OCIS Codes
(060.0060) Fiber optics and optical communications : Fiber optics and optical communications
(060.4258) Fiber optics and optical communications : Networks, network topology

ToC Category:
Fiber Optics and Optical Communications

History
Original Manuscript: October 24, 2012
Revised Manuscript: January 6, 2013
Manuscript Accepted: January 6, 2013
Published: January 18, 2013

Citation
Sandu Abeywickrama and Elaine Wong, "Delivery of video-on-demand services using local storages within passive optical networks," Opt. Express 21, 2083-2096 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-2-2083


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References

  1. “Cisco visual networking index: Forecast and methodology, 2011 – 2016,” http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-481360.pdf .
  2. “End user multimedia QoS categories,” ITU-T Recommendation G.1010 (2001).
  3. “Speech and Multimedia Transmission Quality (STQ); Audiovisual QoS for communication over IP networks,” ETSI ES202, 667 (2009).
  4. “Quality of Service ranking and measurement methods for digital video services delivered over broadband IP networks,” ITU-T recommendation J.241 (2005).
  5. P. Dymarski, S. Kula, and T. N. Huy, “QoS conditions for VoIP and VoD,” J. Telecommun. Inf. Technol.3, 29–37 (2011).
  6. D. De Vleeschauwer and K. Laevens, “Performance of caching algorithms for IPTV On-Demand services,” IEEE Trans. Broadcast.55(2), 491–501 (2009). [CrossRef]
  7. J. Baliga, R. Ayres, K. Hinton, and R. Tucker, “Architectures for energy-efficient IPTV networks,” in Proceedings of OFC 2008, paper ThQ5 (2008).
  8. C. Jayasundara, A. Nirmalathas, E. Wong, and N. Nadarajah, “Energy-efficient content distribution for VoD services,” in Proceedings of OFC/NFOEC 2011, paper OWR3 (2011).
  9. C. Jayasundara, A. Nirmalathas, E. Wong, and C. A. Chan, “Improving energy efficiency of Video on Demand Services,” J. Opt. Commun. Netw.3(11), 870–880 (2011). [CrossRef]
  10. C. Jayasundara, A. Nirmalathas, E. Wong, and N. Nadarajah, “Popularity-aware caching algorithm for Video-on-Demand delivery over broadband access networks,” in Proceedings of IEEE GLOBECOM, 1 – 5 (2010).
  11. “IEEE Standard for Information Technology – Telecommunications and information exchange between systems – Local and metropolitan area networks – Specific Requirements – Part 3: Carrier sense Multiple Access with Collision Detection (CSMA/CD) access method and physical layer specification,” ANSI/IEEE Standard 802.3 – 2002, http://standards.ieee.org/getieee802/download/802.3-2002.pdf .
  12. Y. Chen, T. Farley, and N. Ye, “QoS requirements of network applications on the Internet,” IOS Press, Systems Management4, 55–76 (2004).
  13. E. Wong, M. Mueller, M. P. Dias, C. A. Chan, and M. C. Amann, “Energy-efficiency of optical network units with vertical-cavity surface-emitting lasers,” Opt. Express20(14), 14960–14970 (2012). [CrossRef] [PubMed]

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