Enhancing Bandwidth on Demand Service Based on Virtual Network Topology Control Takashi Miyamura† Eiji Oki Ichiro Inoue Kohei Shiomoto NTT Network Service Systems Laboratories, NTT Corporation 3-9-11 Midori-cho, Musashino-shi, Tokyo 180-8585, Japan Tel: +81-422-59-3527 Fax: +81-422-59-3494 E-mail: †
[email protected] Abstract— This paper presents multilayer IP optical traffic engineering technologies based on VNT (virtual network topology) control and studies their application to BoD (bandwidth on demand) services to improve flexibility and efficiency. Multilayer TE (traffic engineering) optimizes network-resource utilization considering all layers, rather than performing optimization independently for each layer, by performing traffic control in cooperation with IP routes and optical cross-connects (OXCs) in multilayer IP optical networks. Hence, we can accommodate unexpected trafficdemand fluctuations and network failures by dynamically reconfiguring VNT so that it consists of several optical paths among IP routers. The key to achieving multilayer TE lies in the optimal design of VNT based on measured traffic demand. Thus, by introducing multilayer TE into a BoD service, we can enhance flexibility of the service regarding unexpected trafficdemand changes without adding operational overhead that affects customers of the service.
•Point-to-Point •Point-to-Point connection connection establishment establishment is is triggered triggered by by customer customer request request •Bandwidth •Bandwidth is is manually manually specified specified by by customer customer
a) Conventional BoD Service •Multi-Point •Multi-Point connection connection is is established established permanently permanently •Bandwidth •Bandwidth is is automatically automatically adjusted adjusted by by service service provider provider
b) VNT Service
Fig. 1.
I. Introduction Recently, BoD (bandwidth on demand) services have received much attention as a means of providing highcapacity bandwidth of optical networks to end users [1]. In conventional BoD services, each customer is required to specify service parameters such as holding time and bandwidth, for example, upon service initiation. The price is dependent on these parameters, so each customer wishes to minimize utilized network resources while maintaining adequate service quality. However, estimating parameters at the time of service initiation is very difficult. Moreover, the connectivity of the service is basically limited to a point-to-point connection. Considering recent business circumstances, wholesale customers often construct their own network by combining multiple pointto-point connections to transport packet-based IP and Ethernet traffic. Thus, one of the important challenges is designing a minimum-cost network configuration in combination with multiple point-to-point connections provided by the BoD service while satisfying traffic demand. Furthermore, a network connecting customer edges (CE) is required to be both flexible and reliable. Flexibility regarding unexpected traffic fluctuations should be improved and highly reliable services should be provided while network resources are efficiently utilized. Therefore, the network
BoD Service and its enhancement using VNT
topology is expected to be automatically controlled in response to changes in customer demand without any additional operational overhead. In this paper, we thus present a VNT (virtual network topology) service (Fig. 1), which provides multi-point connectivity and improves flexibility regarding bandwidth while reducing operation overhead that affects customers and maintaining advantages of existing BoD services. From the service provider perspective, efficiently accommodating multiple VNTs allocated to each customer in the provider’s network to achieve a VNT service economically is important. For customers, the amount of used network resources should be minimized as long as adequate service quality is maintained. The following are two technologies for achieving the VNT service: i) a multilayer Traffic Engineering (TE) technology that optimizes network resource usage by adequately reconfiguring VNT in response to variations in demand, and ii) a resource-management mechanism for maximally utilizing network resources for better service while avoiding resource contention among multiple VNTs on the provider’s network. Multilayer TE optimizes network-resource utilization considering all lay-
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ers, rather than performing optimization independently for each layer. We have undertaken the research and development of an IP optical backbone network that could achieve the integrated management of both IP and OXCs. We developed an IP optical TE server for linking the IP and OXCs in an IP optical backbone network, controlling traffic, and we conducted successful test-bed trials of dynamic network control in an IP optical backbone network configured with IP routers and optical crossconnects (OXCs). The remainder of this paper is organized as follows. We briefly describe the concept of VNT and introduce a new BoD service based on VNT control in Section 2. We then describe a mechanism of multilayer TE and present some results of our experimental demonstrations in Section 3. Section 4 addresses VNT design and resource management and presents a framework of a pricing model for the BoD service based on VNT control. A brief conclusion is provided in Section 5. II. VNT-based BoD service A. Concept of VNT We assume that a network consists of electronic IP routers and optical cross-connects (OXC), as illustrated in Fig. 2. Note that we mainly consider IP as an upper layer in this paper, but the same discussion can be applied to Ethernet or any packet/frame-based interface. Each port of edge IP routers is connected to an OXC port. Traffic that is carried on the network between IP routers is carried over WDM links. An optical path is established between two IP routers by configuring OXCs along the route between the routers. An optical path is terminated at the transceiver of the last IP router, and the optical path is handled as an IP link in the IP layer network. A set of optical paths forms a VNT, and traffic between two routers is routed on top of the VNT using MPLS explicit-routing technology. The VNT provides connectivity among IP routers for efficiently handling IP-layer traffic demand. By adequately configuring VNT, we can accommodate unpredictably fluctuating traffic or improve reliability upon network failure. Thus, VNT is a useful tool for achieving sophisticated traffic engineering within a service provider’s network. B. VNT-based BoD service Now we present a VNT-based BoD service, known as a VNT service, as illustrated in Fig. 1, which provides multi-point connectivity and improves flexibility regarding traffic-demand changes without adding operation overhead that affects customers compared to the existing BoD service. Characteristics of the VNT service are summarized in Table I. In the VNT service, a service provider network provides continuous connections among multiple customer sites. Bandwidth of each pipe connecting a pair of customer edges is automatically adjusted in accordance with traffic-demand changes, which is predicted by measuring
Vir tual Network Topology
IP Layer
VNT1 VNT2
Optical Layer
Physical Fiber Topology
Fig. 2. A multilayer network consists of IP and optical-layer networks. A set of optical paths, VNT, forms a logical topology of the IP network. VNT is reconfigurable in response to traffic-demand changes or network failures for achieving better network performance.
the traffic matrix. A VNT is constructed on the provider’s network. Each CE is attached to the VNT, and traffic of each customer is transported over the VNT. The topology of the VNT is not recognized by the customers. The price is basically determined by a holding time multiplied by bandwidth. Customers wish to minimize the total cost while maintaining adequate service quality. In some cases, the upper limit of a budget is limited in each month. In the VNT service, a VNT is designed in consideration of those customer policies by the service provider. For example, a VNT is computed to minimize utilized network resources on the provider’s network while avoiding network congestions on the basis of estimated traffic demand among CEs. In another case, a VNT is designed to maximize network performance such as endto-end delay or throughput considering the total budget of a customer. From the service provider perspective, efficiently accommodating multiple VNTs allocated to a customer on top of the provider’s network for a cost-effective VNT service is important. For a given traffic-demand matrix, we compute an optimal VNT that minimizes network-resource consumption while satisfying service requirements of the customer. Several studies investigated VNT design algorithms [2], [3], [4]. Basically, VNT design problems can be formulated as an optimization problem. For example, the objective of the conventional problem is to determine the route of each packet-layer path and to design the VNT itself. Here, please note that the number of wavelengths per link and the number of electronic ports, which can handle packet-based IP traffic, at each router node are limited resources. Thus, routes of packet-layer paths and the VNT should be designed so that a set of traffic demands is efficiently satisfied. The objective of the multilayer TE is to find an adequate VNT for a given traffic demand matrix under the constraints of the number of wavelengths and the number of transceiver ports. The VNT service reduces operational overhead of customers and provides efficient usage of network resources. Compared to the conventional point-to-point BoD service,
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Conventional BoD Service
VNT Service
Connectivity
Point-to-Point
Muti-point
UNI (Data plane)
Any (L1/L2/L3)
Packet or Frame-based (L2/L3)
Connection initiation /release
Triggered by customer request
Continuously established
Allocated bandwidth
Manually specified upon connection request
Automatically adjusted based on actual demand by service provider
Operation cost for customer network
Estimating traffic demand and/or holding time is very complicated
No additional task is required for traffic demand variation
TABLE I Comparison of BoD service and VNT service
Carriers’ policy implemented (Multi-layer, quality of service, reliability, etc.) Policies can be customized.
IP optical TE server
IP layer VNT 1
B. VNT reconfiguration Multi-layer routes
IP/MPLS network Optical layer
VNT reconfiguration
VNT 2
Optical network VNT: Virtual Network Topology
Fig. 3.
are considered as well as IP links that already established. For example, the source router is not connected to the destination router using a current IP topology. The IP optical TE server decides to establish a new optical path for the requested end-to-end packet path based on both IP and optical-network resources. In the calculation, constraints such as bandwidth, delay, link attributes, inclusive/exclusive routes, and protection class, for example, are taken into account. The multi-layer route that is selected also depends on a carrier’s policy. For example, one policy tries to find available optical paths as much as possible. The other policy tries to establish a new optical path so that the number of hops between source and destination routers is minimized [7].
Multi-layer traffic engineering
customers are not required to support sophisticated connection control, and network-resource occupancy is always optimized by TE within a service provider’s network. III. Multilayer TE Multilayer TE is able to optimize network resource utilization considering all layers, rather than optimization at each layer independently. IP routes and OXCs are managed and operated under the optical TE server considering network resources of both layers [5], [6], as shown in Fig. 3. The IP optical server computes path routes across different layers and control paths upon requests from users and operators. The optical TE server dynamically reconfigures an IP network topology that consists of several optical paths, VNT, according to traffic-demand fluctuation and network failure. A. Multi-layer path computation and several constraints Let us consider computation of a route between source router and destination router in IP optical networks. There are two possible methods. One is single-layer path computation and the other is multi-layer path computation. In the single-layer path computation, only IP links, which are already established, may be optical paths. On the other hand, in multi-layer path computation, new IP links, which are optical paths that may be established,
The optical TE server is able to reconfigure VNT in response to a change in traffic demand, a network failure, or a change in topology under the control of an operator [8]. These trigger cases are described in the following. • Traffic demand change A traffic matrix is defined as traffic volume between border routers, which are located at the edge of optical networks. The traffic information is periodically collected from networks, the optimal VNT is computed, and the VNT is changed by setting up and releasing optical paths. • Network failure When a network failure occurs, the optical TE server detects that by means of a trap or advertisements of a routing protocol such as OSPF (Open Shortest Path First). Based on new network resources and the traffic matrix, VNT reconfiguration is performed. • Topology changes When a new link is added or some existing links are deleted, the optical TE server detects the topology change using the advertisement of routing protocol. If a network failure occurs based on new network resources and the traffic matrix, VNT reconfiguration is performed. • Operator initiation An operator can also initiate the VNT reconfiguration using the IP optical TE server. C. IP Optical TE server and its experimental demonstration We successfully performed experiments using a multilayer TE. The functions of multi-layer TE, including path computation across multiple layers and dynamic VNT reconfiguration in response to traffic fluctuation, were tested in MPLS and GMPLS networks. The experimental network configuration is shown in Fig. 4. The network consisted of four GMPLS routers, two MPLS routers, distributed traffic generation tools, and a multilayer network-monitoring viewer. The distributed traffic-
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Optical IP TE Server IP/MPLS network
Input
VNT Resource manager (VRM)
Customer’s traffic demand
VNT VNT optimization optimization
VNT viewer
Customer’s budget
Pricing Pricing mechanism mechanism
Network resource usage
Output Resource allocation Price VNT design
Customer
VNT1
VNT n •Feedback
GMPLS network
IP/MPLS network
IP/MPLS network
•Measured from network •Requested by customer
: GMPLS Router
Distributed traffic generation tool
: MPLS Router : OXC IP/MPLS network
Fig. 4.
Configuration of experimental network
VNT #2
Provider’s network
: Traffic Generator
Fig. 6.
Schematic view of VNT resource management
Customer network
IP layer
Optical layer Each Each VNT VNT is is designed designed by by considering considering i)i) traffic traffic demand demand of of each each customer, customer, ii) ii) network network resource resource utilization. utilization.
VNT #1 IP layer
Optical layer
Service Provider’s optical network
Fig. 5. Accommodating multiple VNT on top of service provider’s network
generation tool generates traffic from multiple terminals and consists of a traffic controller and traffic generators. The tool is able to emulate traffic fluctuation under an actual network environment. IV. VNT Resource management with Dynamic Pricing A. Architecture of VNT resource management In this section, we discuss how to allocate network resources to each VNT. The key is to introduce a dynamic pricing mechanism in conjunction with resource management. Here, we present a framework for resource management with dynamic pricing. As illustrated in Fig. 5, network resources in a provider’s network for the VNT service are shared by multiple VNTs, each of which is exclusively allocated to a customer. Thus, some inter-VNT resource-management mechanism is required for maximally utilizing network resources for better performance of each customer’s VNT while avoiding the exhaustion or contention of network resources. Basically, we can achieve efficient resource usage by taking into account the traffic-demand matrix of customers in designing each VNT. This problem can be formulated as an optimization problem by extending existing work [2], [3], [4]. However, the provider needs to maximize revenue by efficiently utilizing limited network resources, while cus-
tomers wish to maximize the benefit derived from the VNT service. The problem is that the objective of each player competes with each other. In addition, considering a commercial service, the amount of resources required by each customer is traded-off with their cost. Thus, the problem cannot be solved by simply optimizing networkresource usage. One possible solution is to introduce a sophisticated pricing mechanism that dynamically changes the price of a resource by considering the amount of residual network resources. A schematic view of the mechanism is depicted in Fig. 6. The VNT resource-management function consists of i) a VNT optimization function that computes the optimal VNT for a given traffic demand and ii) a dynamic-pricing function that determines an adequate price and resource allocation considering residual network resources and customer’s benefit. B. Pricing Mechanism Now we consider the framework for a dynamic-pricing mechanism. First, we define the problem that we are trying to solve. The objective is to find the optimal price and resource allocation that maximizes the benefit for the customers and the profit for the provider under the constraints of the network resources and the minimum bandwidth of each user. Here, the price is defined as the money that customers spend in exchange for a unit of network resources. Next, we present the mathematical formulation of the pricing model. We assume that a network consists of an one-link network model, as illustrated in Fig. 7, and the total capacity C is shared by customers R. We also use the following notations: • C: total capacity of the network • R: set of customers • br : budget of customer r ∈ R • xr : amount of resources allocated to customer r min • xr : minimum guaranteed bandwidth for customer r • Ur (xr ): utility function of customers r 204
Cr (xr ): cost function of customers r • Cp (xr ): network cost of the provider for providing resource xr The mechanism can be formulated as follows: Maximize { (Ur (xr ) − Cr (xr ))} + α · { (Cr (xr ) − Cp (xr ))} •
r∈R
Total capacity (C) Resource for customer r (x r)
Customer r Total allocated resources ( x ) Customer 2 Customer 1
Fig. 7.
Simple one-link network used in pricing model
r∈R
budget constraint Cr (x) ≤ br ∀ r xr ≤ C capacity constraint r∈R
Price
subject to
∀ r, service quality constraint xr ≥ xmin r where α is defined as the weight of the provider’s revenue normalized by the customer’s benefit. Notice that we describe the case of a simple one-link network model for easy understanding, in which the price can be considered to be determined by the amount of residual resources at the heavily congested link. To solve the above formulation, we need to determine utility function Ur (xr ), cost function of the customers Cr (xr ), and cost function of the provider Cp (xr ). Several studies [11], [12] have investigated those functions. As shown in [11], the utility function can be represented as a logarithmic function given by x Ur (x) = U0 + wlog min , x where U0 and w represent the base value of the utility and the sensitivity of the utility to allocated resources, respectively. Furthermore, the price should be dependent on the amount of the residual resources to avoid resource exhaustion in the network: th n if u ≥ fr Prf ix × ( 1−U 1−u ) Pr (u) = f ix Pr if u < fi , where U th denotes a threshold of the price curve, u corresponds to the network utilization and is given by (1/C) × r∈R xr , and n is the positive integer that characterizes the slope of the price curve. Thus, the cost function is expressed by the above price function and the amount of allocated resources: Cr (xr ) = xr · Pr (u). An example of the price function is illustrated in Fig. 8. Finally, we observe the qualitative characteristic of the above price mechanism. By introducing the above price mechanism, we can avoid resource contention among customers. If the network utilization is relative low (u ≤ Uth ), customers are encouraged to consume more network resources under the constraint of the utility function. On the other hand, when the network is highly congested (u = 1), the price sharply increases and customers are discouraged from using more resources. By solving the above formulation, we can obtain the optimal pricing mechanism
Prfix
U
th
Network utilization
Fig. 8.
Example of price function
and network resources allocated to each customer, which could maximize the benefit of customers and the provider while effectively avoiding network congestions. C. Related Work Here, we briefly address related work regarding pricing mechanisms in network services. Wang and Schulzrinne [11]. proposed a pricing mechanism in differentiated services for adaptive applications, which dynamically adapt their service requirements according to network congestion Their motivation of introducing pricing is to provide different level of service quality to users efficiently by adequately regulating the volume of traffic through pricing schemes. Recently, Courcoubetis et al. [13] investigated pricing strategies in BoD services. They considered three types of pricing strategies, static contracts, dynamic contracts, and mixed contracts. In the static contracts, each customer is required to buy a fixed amount of network resources based on predetermined tariff ahead of time. On the other hand, in the dynamic contracts, a customer is allowed to buy some amount of resources at any time period. The mixed contracts are the mixture of static and dynamic contracts. They revealed that the mixed contracts enable providers to increase their revenue compared to the other strategies. Their concern is focused on the comparison of three different pricing strategies, thus their study is based on a conventional single-layer point-to-point BoD service. Our research goal is to establish multilayer network resource control mechanisms in conjunction of a dynamic pricing mechanism. By measuring packet-layer traffic demand and optical-layer residual network resources, we determine adequate price which enable us to maximize the benefit for the customers and the profit for the provider.
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V. Conclusions This paper presented multilayer IP optical TE technologies based on VNT control and proposed its application to BoD services. We believe the VNT service can improve flexibility regarding bandwidth while reducing operation overhead that affects customers and maintaining advantages of existing BoD services. The following are two technologies for achieving the VNT service: i) a multilayer TE technology and ii) inter-VNT resource management with a dynamic pricing mechanism. For providing costeffective services, optimizing network resource utilization globally is essential, i.e., taking into account all layers, rather than optimizing resource utilization at each layer independently. This allows better network efficiency to be achieved. We successfully performed multi-layer TE experiments. The IP optical TE server dynamically reconfigures the VNT that consists of several optical paths in response to traffic demand fluctuations and network failures. We also demonstrated that we can enhance the flexibility of a BoD service regarding unexpected traffic-demand changes by introducing multilayer TE into a BoD service. To investigate the feasibility of the service, we discussed the framework of inter-VNT resource management and a dynamic pricing mechanism in consideration of networkresource utilization. References [1] S. Liu and L. Chen, “Deployment of Carrier-Grade Bandwidthon-Demand Services over Optical Transport Networks: A Verizon Experience”, OFC/NFOEC 2007, pp. 1-8, March 2007. [2] B. Mukherjee, et al., ”Some principles for designing a wide-area WDM optical network,” Networking, IEEE/ACM Transactions on vol. 4, no. 5, pp.684-696, Oct. 1996.
[3] D. Banerjee and B. Mukherjee, ”Wavelength-routed optical networks: Linear formulation, resource budgeting tradeoffs, and a reconfiguration study,” IEEE/ACM Trans. Networking, vol. 8, pp. 598-607, Oct. 2000. [4] J. Wey et al, ”Network control and management for the next generation Internet,” IEICE Trans. Commun., vol. E83-B, no. 10, October 2000, pp. 2191-2209 [5] E. Oki, J.L. Le Roux, A. Farrel, “Framework for PCE-Based Inter-Layer MPLS and GMPLS Traffic Engineering,” IETF draft, draft-ietf-pce-inter-layer-frwk-04.txt, July 2007. (work in progress) [6] E. Oki et al., “PCC-PCE Communication Requirements for Inter-Layer Traffic Engineering,” draft-ietf-pce-inter-layer-req05.txt, July 2007. (work in progress) [7] E. Oki, K. Shiomoto, M. Katayama, W. Imajuku, and N. Yamanaka, “Performance of Dynamic Multi-Layer Routing Schemes in IP+Optical Networks,” 2003 Workshop on High Performance Switching and Routing, pp. 233-238, June 2003. [8] K. Shiomoto, E. Oki, W. Imajuku, S. Okamoto, and N. Yamanaka, “Distributed Virtual Network Topology Control Mechanism in GMPLS-Based Multi-Region Networks,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 8, pp. 12541262, Oct. 2003. [9] JP. Vasseur et., “Path Computation Element (PCE) communication Protocol (PCEP) - Version 1,” IETF draft, draft-ietfpce-pcep-08.txt, July 2007 (working in progress). [10] K. Shiomoto, E. Oki, I. Inoue, and S. Urushidani, “A serverbased traffic engineering method in IP+Optical multi-layer networks,” Int. Conf. on IP+Optical Network (iPOP) 2006 Session T3-4, June 2006. [11] X. Wang, H. Schulzrinne, Pricing network resources for adaptive applications in a differentiated services network, in: Proceedings of IEEE INFOCOM 2001, Anchorage, AK, April 2001. [12] T. Li, Y. Iraqi, and R. Boutaba, “Pricing and admission control for QoS-enabled internet,” Comput. Networks, vol. 46, no. 1, pp. 87-110, 2004. [13] C. Courcoubetis, S. Soursos, and R. Weber, “Pricing Resources on Demand”, in Proceedings of IEEE International Workshop on Bandwidth on Demand 2006, San Francisco, CA, December 2006.
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