Green Provisioning for Optical WDM Networks - Semantic Scholar

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, VOL. 17, NO. 2, MARCH/APRIL 2011

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Green Provisioning for Optical WDM Networks Ming Xia, Student Member, IEEE, Massimo Tornatore, Member, IEEE, Yi Zhang, Pulak Chowdhury, Student Member, IEEE, Charles U. Martel, and Biswanath Mukherjee, Fellow, IEEE

Abstract—Since the Internet consumes a large (and increasing) amount of energy, “green” strategies are desirable to help service providers (SP) operate their networks and provision services more energy efficiently. We focus on green provisioning strategies for optical wavelength-division multiplexing networks. A number of approaches from component layer to network layer are discussed, which should help improve the energy efficiency of the networks. Then, we consider a typical optical backbone network architecture, and minimize the operational power for provisioning. Typically, operational power depends on strategy (e.g., optical bypass versus traffic grooming), operations (e.g., electronic domain versus optical domain), and route. We analyze the constituents of operational power in various scenarios, and discuss the opportunities for energy savings. We propose a novel auxiliary graph, which can capture the power consumption of each provisioning operation. Based on the auxiliary graph, we develop a power-aware provisioning scheme to minimize the total operational power. Performance evaluation shows that our scheme always needs the least operational power, with comparison to a direct-lightpath approach and a traffic-grooming approach. The result also suggests proportional power consumption by operations (network equipment) and endnode traffic grooming to fully exploit the power-saving potential of optical networks. Index Terms—Energy efficiency, green provisioning, optical network, power consumption, wavelength-division multiplexing (WDM).

I. INTRODUCTION S THE Internet continues to grow, it requires network elements with more capacity, higher transmission rates, and faster processing speeds. Typically, Internet traffic (in the electronic layer) is carried over optical backbone infrastructure (optical layer). Such a layered network architecture can exploit both the flexibility and logical processing capability offered by legacy electronics as well as the enormous bandwidth promised

A

Manuscript received March 1, 2010; revised April 15, 2010; accepted May 11, 2010. Date of publication July 11, 2010; date of current version April 6, 2011. M. Xia was with the Department of Computer Science, University of California, Davis, CA 95616 USA. He is now with the National Institute of Information and Communications Technology, Tokyo 184-8795, Japan (e-mail: [email protected]). M. Tornatore was with the University of California, Davis, CA 95616 USA. He is now with the Department of Electronics and Information, Politecnico di Milano, Milano 20133, Italy (e-mail: [email protected]). Y. Zhang is with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China and also with the University of California, Davis, CA 95616 USA (e-mail: [email protected]). P. Chowdhury, C. U. Martel, and B. Mukherjee are with the Department of Computer Science, University of California, Davis, CA 95616 USA (e-mail: [email protected]; [email protected]; mukherje@ cs.ucdavis.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTQE.2010.2050867

by fiber optic technologies such as wavelength-division multiplexing (WDM) [1]. However, energy use also increases with higher transmission rates and larger capacity, and this increasing appetite for energy can hamper the expansion of the future Internet. Baliga et al. [2] estimate that the Internet consumes 4% of electricity in broadband-enabled countries. Pickavet et al. [3] report that network equipment (e.g., switches and routers) accounts for approximately 14.8% of the total information and communication technology (ICT) energy consumption, and this proportion is estimated to increase to 21.8% by 2020. From a broader view, the Internet’s energy consumption will ultimately cause a series of problems, both environmentally and socially. Gartner, a networking research and advisory company, estimates that the manufacture of ICT equipment, its use, and its disposal accounts for 2% of global CO2 emissions, which is a direct reason for the greenhouse effect [4]. Therefore, an environmentally friendly (Green) ICT is desirable, and should be considered for design and operation of the future Internet. We use operational power consumption as a metric to evaluate energy efficiency. When a service (carried by a connection)1 is provisioned, incremental operational power is needed. Taking a 16 × 16 switching fabric of a router as an example, the power consumption of the switching fabric can vary from 20 to 90 mW when traffic changes from 10% to 50% [5]. Hence, operational power (apart from the power needed for running the network infrastructure) plays a significant role in affecting energy efficiency. From the network perspective, operational power typically depends on traffic volume, the provisioning strategy (e.g., optical bypass versus traffic grooming), operations (e.g., electronic domain versus optical domain), route, etc., which essentially deals with traffic engineering (i.e., to put the traffic where the bandwidth is) [1]. Complying with other constraints (e.g., bandwidth), a service provider (SP) needs to provision while achieving high energy efficiency. Note that the energy needed for running a network infrastructure is excluded from our study, since it can be reduced during Network Planning [1]. We target a green provisioning strategy for optical backbone networks to reduce the operational power. Our technical contributions are mainly fourfold: 1) we analyze the constituents of power consumption for service provisioning at the operation level; 2) we discuss the pros and cons of optical bypass and traffic grooming (i.e., electronically packing multiple subwavelength traffic on one lightpath channel [6]) in terms of power efficiency; 3) we propose a novel auxiliary graph that can characterize the operations for provisioning, and capture the power consumption of each operation; and 4) we develop an efficient power-aware provisioning scheme based on the auxiliary graph. 1 In

the rest of this paper, service and connection are used interchangeably.

1077-260X/$26.00 © 2010 IEEE

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Performance evaluation shows reduced power of our scheme under various network settings, compared with the direct-lightpath and the traffic-grooming schemes. This paper is organized as follows. Section II examines a number of approaches that seem promising for green optical backbone networks. Section III analyzes the operational power for service provisioning, and formulates the problem of power minimization. Section IV describes the auxiliary graph, and presents a power-aware provisioning scheme. Section V reports the performance study. Section VI concludes the paper.

tary profiles of traffic on a single network element (e.g., a router or a fiber) [10], the excess capacity and protection resources as well as energy consumption can be reduced. 6) Network Planning: Energy use can be considered at the network-planning stage such that all traffic demands are accommodated using minimum total energy. Also, in optical networks, equipment can be located virtually anywhere, thanks to its huge transmission capability. Therefore, relocating network services and data centers to remote renewable energy sites not only helps the environment by consuming less energy, but can also save an institution significant electricity cost.

II. GREENING THE OPTICAL BACKBONE A. Broader View

B. Related Work

Examining the features of optical WDM networks, we outline several promising approaches for making them “green” in the following. 1) Component: Advanced IC technologies, such as clock gating and process-specific supply voltages can facilitate design of core router processors and electronic modules with reduced power. Technical advances are expected for all-optical components (e.g., switch and wavelength converter), which can help avoid energy-intensive electronic-domain processing, and hence, improve energy efficiency and reduce heat dissipation [7]. 2) Transmission: Long-reach WDM transceivers and lowattenuation low-dispersion fibers increase transmission efficiency such that the metric J/(b·km) can be reduced. Mixed-linerate technology [8] enables multiple transmission rates (e.g., 10/40/100 Gb/s) on a single fiber, which is promising to realize green optical networks by: 1) adaptively accommodating subchannel connections at lower rates with less energy; and 2) traffic packing to enjoy energy volume discount by high-rate transmission. 3) System: Intelligent power management strategies are needed for multichassis multilinecard equipment, which consolidate traffic from underloaded ports, and enable synergic sleeping/wake up mechanisms among individual modules. For a certain amount of traffic, the configuration of multiple linecards can be optimized for reduced power. Efficient liquid cooling schemes are also desirable for compact and highly integrated equipment [9]. 4) Traffic Engineering: Network traffic can be directed to more energy-efficient routes, e.g., a shorter path that requires fewer in-line amplifiers. Traffic grooming can reduce operational overhead such that power can be kept proportional to traffic as much as possible. Also, direct lightpaths optically bypass intermediate nodes without energy-intensive optical-toelectronic-to-optical (O/E/O) conversion and electronic processing. This can reduce a major portion of the energy consumed in the electronic domain. 5) Network Engineering: As network traffic is usually not balanced, the network topology can be globally reconfigured and optimized by selectively shutting down underloaded switching nodes while still maintaining network connectivity. In addition, the network traffic pattern may be dependent on user behavior, time zones, and other factors. By considering the complemen-

We examine a number of recent studies that take into account energy consumption when designing and operating a network. Tucker [7] compares power dissipation of large-capacity optical and electronic crossconnects for a number of crossconnect architectures, and generalizes the pros and cons of optical and electronic technologies in terms of energy efficiency at “component” and “transmission” levels. Many energy-saving opportunities for “greening the Internet” are discussed in [11]. In particular, the study shows that current IPs can support a more aggressive strategy to put network components to sleep. Chabarek et al. [9] create a generic model for router energy consumption by optimizing its configuration, which falls under the “system” category. Mahadevan et al. [12] study energy optimization for a single administrative domain network as a tradeoff between energy saving and network performance. Chiaraviglio et al. [13] use a “network engineering” approach to reduce energy consumption of backbone networks, by dynamically switching off network nodes. Qureshi et al. [14] identify the variation of electricity prices, and aim at exploiting this variation for economic gain. Time-zone-dependent traffic for network-level optimization, which offers an extra dimension for energy saving (and electricity fee reduction) is studied in [15] and [16]. References [14]–[16] fall in the category of “network planning”. Recently, some works are emerging on energy efficiency of optical backbone networks. Shen and Tucker [17] studied the design of an IP over WDM network to accommodate a certain traffic demand, and minimize the energy needed. In [18], the energy consumption of an optical WDM network is modeled using the energy consumed by an individual lightpath. Huang et al. [19] minimize power consumption of optical WDM networks in a “network planning” scenario by minimizing the number of interface pairs. In [20], energy analysis is conducted in a WDM ring network to evaluate the advantages of single-hop, hop-byhop, and multihop provisioning. As these works are more related to our study, a comparison is provided in Table I. Though they have a similar analysis of traffic grooming as we do, our approach dictates provisioning at the operation level directly based on the power of operations, instead of using indirect optimization objectives (e.g., minimizing the number of lightpaths). Here, our effort on greening optical backbone networks follows a traffic engineering approach. We analyze power consumption for service provisioning (i.e., operational power), which is route- and strategy-dependent. Then, using optimal

XIA et al.: GREEN PROVISIONING FOR OPTICAL WDM NETWORKS

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TABLE I COMPARISON AMONG THE WORKS THAT FOCUS ON ENERGY CONSUMPTION OF OPTICAL WDM NETWORKS

provisioning, operational power can be minimized. For simplicity, we use “power” instead of “operational power,” to refer to the power needed for service provisioning. III. ANALYSIS OF POWER CONSUMPTION In a typical IP/multiprotocol label switching over reconfigurable optical backbone architecture, IP router [or digital crossconnect (DXC)] interfaces are connected to the ports of WDM optical crossconnects (OXCs), and OXCs are interconnected in a mesh configuration with multiwavelength fiber links. Typically, traffic in the electronic domain enters an IP router attached to an OXC is converted to an optical signal, and then is transmitted via fiber links. Within the reach of a lightpath, traffic optically bypasses the intermediate OXCs through “express channel,” without the need of being processed. An optical signal needs to be converted to an electronic signal at the end of each lightpath. To achieve high power efficiency, it would be ideal for the power consumed by network components to be proportional to the processed traffic volume [21]. However, current network equipment is far from being power proportional [22]. For example, due to discrepancy between the requested bandwidth and the granularity of a wavelength, subwavelength traffic needs to be operated on a full wavelength, even if only partial bandwidth is used. Without losing generality, we assume that power consumed by operations (e.g., O/E conversion, optical switching, etc.) consists of two parts: a fixed overhead PO , and a trafficdependent power PT [18], i.e., P = PO + P T × t

(1)

where PO and PT are two operation-dependent parameters on one-wavelength basis, t is the actually carried traffic amount and t ∈ [0, 1]. According to (1), the smaller is the portion of a wavelength used, the larger is the portion of overhead. Considering the overhead, power efficiency can be improved by traffic grooming, i.e., multiple subwavelength traffic flows can be multiplexed on a single wavelength. This strategy addresses the problem of bandwidth fragmentation so that fewer lightpaths are needed. In addition, when new traffic is groomed

to an existing lightpath, the overhead can be avoided, since it has already been paid by an existing traffic flow. Though traffic grooming can reduce overhead, it requires cumbersome O/E/O conversions and electronic processing, which consume significantly more power than optical switching (bypass). Tucker [7] gave some typical values of power consumption for several operations. Also, traffic grooming typically takes longer routes to exploit existing lightpaths, which requires more transmission power. As the power saved by reducing overhead and number of lightpaths may be exceeded by extra operations and transmission, traffic grooming is not always optimum. It is likely that a direct lightpath between a source and a destination node consumes less power, or a connection may partly use a newly set up lightpath and partly be groomed to an existing lightpath, so that overall power can be minimized. We analyze the constituents of power in different cases to evaluate the power efficiency of both optical bypass and traffic grooming. We can then analyze the tradeoffs between routing using grooming versus adding new lightpaths, and thus, find routes with minimized power.

A. Modeling Power Consumption Signal transmission can be decomposed into multiple serial operations, such as O/E conversion, electronic switching. In reality, several operations may be integrated in one component, e.g., a linecard typically performs O/E conversion and electronic switching together. In our study, the decomposition of signal transmission is schematic, which follows the typical architecture of an IP over WDM network. Our first analysis is a connection using a direct lightpath, as shown in Fig. 1(a). Traffic arriving at the DXC of node A is electronically switched (ES) to the port connecting to an OXC, and then converted to an optical signal (EO). After E/O conversion, the OXC switches it to an outgoing port (OS), and transmits it via a WDM-compatible transponder (TX). Along the fiber link, the signal is amplified by in-line amplifiers (AM), and optically switched (OS) by the intermediate OXC (at node B). After the traffic is received by a WDM terminal at node C (RX), it is optically switched to a drop port (OS), converted to an

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stated as + PEO + POS + PTX + PAM + PRX + POS PTotal2 = PES + POE + PES + PEO + POS + PTX + PAM + PRX + POS + POE + PES .

(3)

Note that, since all operations in (3) are used for traffic grooming, they do not include the overhead term in (1), while the ones in (2) do. Also note that some other operations may exist, e.g., transmission from a DXC to an OXC. Due to the short distance, we assume this power to be trivial. However, our model for power consumption can be modified by changing the power constituent according to the configuration of the adopted network equipment. B. Problem Definition Fig. 1. Process for traffic flow. (a) Direct lightpath. (b) Two lightpaths with traffic grooming.

electronic signal (OE), and then electronically switched by the DXC (ES). We associate a power to each of these operations;2 therefore, the total power consumption of the provisioning, is evaluated as PTotal1 = PES + PEO + POS + PTX + PAM + POS + PAM + PRX + POS + POE + PES .

(2)

Next, we examine the power consumption of a connection carried by two existing lightpaths using traffic grooming [see Fig. 1(b)]. The two lightpaths are from node A to node B and from node B to node C, respectively. The traffic is first groomed at node A, and then groomed at node B. Entering the grooming port of the DXC, the traffic is electronically switched (ES) to be multiplexed on an existing connection. The multiplexed signal is converted to an optical signal (EO), optically switched (OS) to an outgoing port, and transmitted via a WDMcompatible transponder (TX). Then, the optical signal is amplified (AM), and received by a WDM terminal at node B (RX). It is then optically switched (OS) to a grooming port, converted to an electronic signal (OE), and multiplexed on the next connection by electronic switching (ES). After ES, the traffic is converted to an optical signal (EO), optically switched to an outgoing port (OS), and transmitted via a WDM-compatible transponder (TX). Note that wavelength conversion is automatically granted when O/E/O is performed at node B. Finally, the traffic reaches node C, and follows the same receiving procedure as described for the direct-lightpath case (demultiplexing can be done by electronic switching to different drop ports via the DXC). Thus, total power consumption of the connection using two existing lightpaths and traffic grooming can be

2 We model the power consumed by an amplifier on individual wavelength basis, which is derived from [7]. Shen and Tucker [17] assumed this power to be independent from amplified traffic.

The operational power minimization problem is defined as follows. Given: 1) a mesh network’s topology Graph G(V ,E), where V is the set of nodes, E is the set of fiber links connecting the nodes, and each fiber link has K wavelengths; 2) a set of known connection requests (static scenario) or online-arriving connection requests (dynamic scenario); 3) power consumption of each operation. Need to: Provision the connections. Objective: Minimize the total operational power. IV. POWER-AWARE PROVISIONING We address the problem of operational power minimization using an auxiliary graph. By routing on the auxiliary graph, we consider optical bypass, traffic grooming, and hybrid strategies using both optical bypass and traffic grooming to minimize total power. We assign arc weights corresponding to power usage, such that the “shortest” path approximates the most powerefficient route and provisioning strategy. In line with these targets, the auxiliary graph needs to: 1) capture the operation flow of provisioning (e.g., recognize O/E/O conversion and related operations at the intermediate node of two lightpaths); 2) capture the power consumption of each operation; and 3) evaluate the total required power for service provisioning by the “length” of path. The rest of this section first describes the construction of the auxiliary graph, and then presents a power-aware Provisioning scheme. A. Auxiliary Graph We use a six-node topology for illustration [see Fig. 2(a)]. Nodes labeled from A to E consist of both DXCs and OXCs in a layered structure. Fig. 2(b) shows the virtual topology, where two lightpaths exist, A–B–C and C–D. Fig. 2(c) shows the auxiliary graph, which has a physical layer and a virtual topology layer corresponding to the physical and the virtual topology, respectively. Each node is represented by two nodes in the physical


Fig. 2.

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Six-node topology. (a) Physical topology. (b) Virtual topology. (c) Auxiliary graph.

layer: a physical optical node (PON) corresponding to an OXC, and a physical electronic node (PEN) for a DXC. Each node in the virtual topology is also represented by two nodes: a virtual topology lightpath ingress node (VIN) to terminate a lightpath, and a virtual topology lightpath egress node (VEN) to initiate a lightpath. The four types of nodes are connected by directional arcs, and each arc represents one or multiple operations as described before. When a connection is routed on the auxiliary graph, the selected path captures a series of operations for a traffic flow. As an example, when a connection is to be provisioned from node A to node D bypassing node B and node C, it originates at PEN a, takes route A → B → C → D, and terminates at PEN d. If the connection exploits the two existing lightpaths using traffic grooming, the route for this connection will be a → AE → CI → c → CE → DI → d.

(4)

Traffic grooming is performed at both node A and node C. For example, at node C, the O/E/O conversion and electronic switching for multiplexing are characterized by the segment CI → c → CE . Also, the two lightpaths are represented by two segments AE → CI and CE → DI . By assigning a weight to each arc as the amount of the power consumed by the associated operations, the weight of a route, e.g., from PEN a to PEN d, is the power consumed by the connection from node A to node D. To find a path that minimizes total power consumption, we run a shortest path algorithm. Based on the operations we have examined, we list the weight assignment for each type of arc in Table II. As noted before, we have not examined all possible operations; some types of network equipment may require additional operations. Also, for amplifiers that consume a fixed amount of power independent of traffic amount, the value of the term PAM (or PAM ) needs to be moved from arc weight, and this fixed amount of power is added to the total power. Now, the SP may define its own power assignment table to match the actual network equipment and architecture. For the power assignment table, note that power associated with the directional arc from a VEN to a VIN is based on the actual power consumed by the corresponding physical links. Take the arc from VEN AE to VIN CI as an example: the

TABLE II POWER CONSUMPTION FOR EACH TYPE OF LINK

power consumed by amplifiers (PAM ) are the amplifiers along the physical link from PON A to PON C via PON B, which is the term PA M (between PON A and PON C via PON B) for link VEN AE → VIN CI in Table II. Also, overhead should be considered, if a new lightpath is set up, while it can be avoided when traffic is groomed to existing lightpaths. This requires arcs to be assigned weights differentially based on their use (either lightpath or traffic grooming). The auxiliary graph, due to its layered structure, facilitates an easy way for arc-weight assignment, which can effectively capture this distinction. For example, the arcs in the physical layer are used for setting up a new lightpath, while the arcs in the virtual topology layer, or across the physical layer and the virtual topology layer, are used for traffic grooming. Overhead [see (3)] should be excluded from the weight of an arc in the second category. B. Power-Aware Provisioning We present a provisioning scheme (described in Algorithm 1) for power minimization, as shown in Fig. 3 which takes a known set of connections (static scenario). Step 2 orders connections, which may affect provisioning. For a better result, multiple sequences can be tried, and the optimal one is chosen from them. When dealing with online traffic (dynamic scenario), provisioning can follow the connection arrival sequence. Our scheme does not depend on the value of the power consumed by an individual operation; therefore, it is applicable in many scenarios as long as weights can be matched to each operation’s power consumption.

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TABLE III POWER CONSUMPTION FOR OPERATIONS

Fig. 3.

Provision scheme for power minimization.

Fig. 5.

Total power versus Network load.

Based on [7] and [17], we derive the relative powers for each operation, which are listed in Table III.3 In addition, we define the ratio of overhead as Fig. 4.

Sample network with fiber lengths (in km) marked on each link.

V. ILLUSTRATIVE NUMERICAL EXAMPLES We simulate a WDM-enabled optical backbone network to evaluate the performance of our scheme. The topology is a 24-node U.S. mesh network with 43 fiber links (see Fig. 4). The capacity of each wavelength is OC-192 (10 Gb/s). The bandwidth granularity is OC-1 (51.84 Mb/s), and the number of the connection requests follows the distribution OC-1:OC3:OC-12:OC-48:OC-192 = 20:10:10:4:1, which is close to the bandwidth distribution in a practical backbone network [23]. Traffic grooming is assumed at each node; therefore, multiple subchannel traffic can be multiplexed on one wavelength as long as their total bandwidth does not exceed the wavelength capacity. There are six “Large” nodes (red circled) with larger nodal degrees, and the rest are “Small” nodes. Traffic is generated using the following pattern: large–large: 40%, large–small: 40%, small–small: 20% [24]. To avoid bias of our results due to connection rejection, we set the network capacity as very large (overprovisioning of capacity), i.e., each link can accommodate any number of wavelengths (however, wavelength capacity is OC-192 to avoid unlimited traffic grooming on a single wavelength). In our last experiment, a limited capacity scenario with 100 wavelengths per link is investigated for comprehensiveness.

Ratio of overhead =

PO PT

(5)

where PO and PT are shown in (1). PT is power consumed by full-wavelength traffic, not by actually carried traffic. Our experiments are run thousands of times with different seeds, and the results are averaged to improve statistical confidence. For comparison, two other strategies are also considered as follows. 1) Traffic grooming: Prefer to route traffic through multiple existing lightpaths than to set up a direct lightpath (corresponding to [6, operation 2]); 2) Direct lightpath: Set up a direct lightpath between the source and the destination as long as link capacity allows (corresponding to [6, operation 3]). A. Power Versus Network Load We first compare the total power consumption under different network loads. The aggregate network load varies from 0 to 13 Tb/s. In addition, the overhead ratio is set as 0.2. In Fig. 5, we see that when network capacity is large enough, as network 3 Due to the variety of technologies and implementations (e.g., AWG-based and SOA–gate-based switch fabric), the value of power consumption by each operation may vary. However, changing the values in Table III does not affect the optimality of our power-aware scheme.


Fig. 6.

Total power versus ratio of overhead.

load increases, all the three schemes consume power proportional to traffic. Our power-aware scheme outperforms the other two, and the performance gap grows when more traffic is accommodated. Also, we note that the power consumed by the directlightpath scheme is higher than traffic grooming scheme, despite the power efficiency of optical transmission. This is due to the existence of a large amount of subwavelength traffic that causes significant power wastage on overhead when setting up new lightpaths. For example, when using a full wavelength to provision OC-1 traffic, the needed power is 38.4 times (i.e., 192 × 0.2 = 38.4) more than when the traffic can be groomed to an existing lightpath. Therefore, even though optical bypass can avoid electronic domain operations, this advantage cannot always compensate for the overhead due to bandwidth fragmentation, which may drastically degrade the performance of the direct-lightpath strategy. B. Power Versus Ratio of Overhead We now investigate the performance as the overhead ratio changes. Network load is set as 10 Tb/s, and the overhead ratio is varied from 0 to 0.4. The result is shown in Fig. 6. In addition, the portion of overhead is indicated by crosshatch pattern. We see that when no overhead exists, the direct-lightpath and power-aware approaches perform almost the same, and consume less power than the traffic-grooming approach. But, as overhead ratio increases, traffic grooming turns out to be better than the direct-lightpath strategy due to its “packing” of multiple subwavelength traffic on one wavelength. As the power-aware approach seeks an optimal combination of the two strategies, it always consumes the least power among the three approaches. We find that optical bypass is more advantageous than traffic grooming in terms of traffic-dependent power, which verifies the “absolute” energy efficiency of optical processing. Since future components and systems will become more power-proportional to actual processed traffic, optical bypass offers promising opportunities for energy savings. However, as we show in the experiments with bounded capacity in the following, using full wavelengths wastes bandwidth and can lead to many rejected connections.

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Fig. 7.

Total power versus ratio of OC-192 connections.

C. Full-Wavelength Versus Subwavelength Traffic Now, we investigate how traffic pattern (i.e., full-wavelength versus subwavelength traffic) can affect power consumption. We define the ratio of OC-192 traffic (full-wavelength traffic) as Number of OC-192 connections Total number of connections

(6)

where the numbers of OC-x connections are the same. We set this ratio as 1:5, 1:3, 1:2, and 1:1 (only OC-192 connections), and the overhead ratio as 0.2. Since total network load may vary under different settings, we use power consumption per Gb/s as our evaluation metric. We plot both the traffic-dependent power and overhead in Fig. 7. The result shows that our power-aware scheme achieves the best performance under various traffic patterns. Moreover, as the ratio of OC-192 traffic becomes larger, all the three schemes need less power, and there is less difference between them. This power saving comes from the decrease of overhead: provisioning full-wavelength traffic is effective at reducing power consumption. Therefore, other than directly provisioning subwavelength traffic and performing traffic grooming at intermediate nodes, subwavelength traffic can be groomed as full-wavelength at the source node for more energy-efficient transmission. D. Power Versus Network Load With Limited Network Capacity In the earlier experiments, the network had no capacity limit; therefore, no connection requests are rejected. Now, we consider a scenario, where each link has 100 wavelengths; therefore, connections will be rejected when capacity is insufficient. The overhead ratio is set as 0.2. We vary network load from 0 to 11.8 Tb/s, and plot power consumption for each scheme in Fig. 8. Also, we plot the bandwidth-blocking ratio (BBR) of the three schemes in Fig. 8. BBR is defined as the rejected bandwidth over the total requested bandwidth. We find that, as network load increases, the direct-lightpath strategy has a much higher BBR than the other two (about 52% when network load is 11.8 Tb/s), so a significant amount of traffic is rejected. Therefore, even though the direct-lightpath scheme consumes the least power, this “advantage” cannot be justified due to its

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Fig. 8.


Power versus network load (limited network capacity).

high BBR. In contrast, both traffic-grooming and power-aware schemes have much lower BBR (less than 20% when network load is 11.8 Tb/s). Particularly, we see that the power-aware scheme has a smaller BBR than the traffic-grooming scheme, which indicates that the power-aware scheme achieves both bandwidth and power efficiency. Since traffic grooming uses generally longer routes for existing lightpath, it may consume more bandwidth in terms of the bandwidth × hop product. The smaller BBR of the power-aware approach indicates that it sets up more lightpaths, which can effectively reduce the length of routes and increase resource efficiency. VI. CONCLUSION As the Internet is consuming an increasing amount of energy, it is imperative for SP to provision services more energyefficiently. We focused on reducing the energy consumption of optical WDM backbone networks, and discussed a number of approaches that are promising to improve energy efficiency. In particular, we studied the operational power for optical-network provisioning. Our analysis shows that, traffic grooming is more advantageous to keep power proportional to traffic volume, and optical bypass reduces operational power by avoiding energyintensive electronic-domain processing. To find the optimum strategy (or a combination of the two), we devised a poweraware provisioning scheme. Provisioning is performed on an auxiliary graph that captures the operations for provisioning as well as their associated power consumption. Numerical results showed that our scheme needs the least power under various scenarios, compared to a direct-lightpath approach and a trafficgrooming approach. Our result extends to the conclusion that power-proportional network equipment is desirable to exploit the energy efficiency of optical-domain processing. We also suggest traffic grooming be performed at end nodes instead of intermediate nodes for higher energy efficiency. REFERENCES [1] B. Mukherjee, Optical WDM Networks. New York: Springer-Verlag, Feb. 2006. [2] J. Baliga, K. Hinton, and R. Tucker, “Energy consumption of the internet,” presented at the COIN-ACOFT, Melbourne, Australia, Jun. 2007.

[3] M. Pickavet, W. Vereecken, S. Demeyer, P. Audenaert, B. Vermeulen, C. Develder, D. Colle, B. Dhoedt, and P. Demeester, “Worldwide energy needs for ICT: The rise of power-aware networking,” in Proc. IEEE ANTS, Dec. 2008, pp. 1–3. [4] Gartner, 2007 Press Release, 2007. [5] T. Ye, L. Benini, and G. D. Micheli, “Analysis of power consumption on switch fabrics in network routers,” in Proc. ACM/IEEE Des. Autom. Conf., Aug. 2002, pp. 524–529. [6] K. Zhu and B. Mukherjee, “Traffic grooming in optical WDM mesh networks,” IEEE J. Sel. Areas Commun., vol. 20, no. 1, pp. 122–133, Jan. 2002. [7] R. Tucker, “The role of optics and electronics in high-capacity routers,” IEEE/OSA J. Lightw. Technol., vol. 24, no. 12, pp. 4655–4673, Dec. 2006. [8] A. Nag and M. Tornatore, “Transparent optical network design with mixed line rates,” in Proc. IEEE Adv. Netw. Telecommu. Syst., Dec. 2008, pp. 1–3. [9] J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang, and S. Wright, “Power awareness in network design and routing,” in Proc. IEEE INFOCOM, Apr. 2008, pp. 457–465. [10] M. Gagnaire, M. Koubaa, and N. Puech, “Network dimensioning under scheduled and random lightpath demands in all-optical WDM networks,” IEEE J. Sel. Areas Commun., vol. 25, no. 9, pp. 58–67, Dec. 2007. [11] M. Gupta and S. Singh, “Greening of the internet,” in Proc. ACM SIGCOMM, Aug. 2003, pp. 19–26. [12] P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan, “Energy aware network operations,” in Proc. IEEE Global Internet Sympo., Apr. 2009, pp. 25–30. [13] L. Chiaraviglio, M. Mellia, and F. Neri, “Reducing power consumption in backbone networks,” in Proc. IEEE ICC, Jun. 2009, pp. 1–6. [14] A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs, “Cutting the electric bill for internet-scale systems,” presented at the ACM SIGCOMM, Barcelona, Spain, Aug. 2009. [15] C. Cavdar, A. Gencata, and B. Mukherjee, “CATZ: Time-zone-aware bandwidth allocation in layer 1 VPNs,” IEEE Commun. Mag., vol. 45, no. 4, pp. 60–66, Apr. 2007. [16] K. Le, R. Bianchini, M. Martonosi, and T. Nguyen, “Cost- and energyaware load distribution across data centers,” presented at the HotPower, Big Sky, MT, Oct. 2009. [17] G. Shen and R. Tucker, “Energy-minimized design for IP over WDM networks,” IEEE/OSA J. Opt. Commun. Netw., vol. 1, no. 1, pp. 176–186, Jun. 2009. [18] E. Yetginer and G. Rouskas, “Power efficient traffic grooming in optical WDM networks,” presented at the IEEE Globecom, Honolulu, HI, 2009. [19] S. Huang, D. Seshadri, and R. Dutta, “Traffic grooming: A changing role in green optical networks,” presented at the IEEE GLOBECOM, Honolulu, HI, Dec. 2009. [20] I. Cerutti, L. Valcarenghi, and P. Castoldi, “Power saving architectures for unidirectional WDM rings,” presented at the IEEE/OSA OFC, San Diego, CA, Mar. 2009. [21] L. Barroso and U. Holzle, “The case for energy-proportional computing,” IEEE Comput., vol. 40, no. 12, pp. 33–37, Dec. 2007. [22] P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan, “A power benchmarking framework for network devices,” in Proc. IFIP Netw., May 2009, pp. 795–808. [23] S. Huang, B. Mukherjee, and C. Martel, “Survivable multipath provisioning with differential delay constraint in telecom mesh networks,” in Proc. IEEE INFOCOM, Apr. 2008, pp. 718–725. [24] Dynamic multi-terabit core optical networks: Architecture, protocols, control and management (CORONET), DARPA, BAA06-29. (2006). [Online]. Available: www.darpa.mil/STO/Solicitations/CORONET/index.htm

Ming Xia (S’05) received the B.S. degree in electrical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2004, and the M.S. and Ph.D. degrees in computer science from the University of California, Davis, in 2008 and 2010, respectively. He is currently an Expert Researcher at the National Institute of Information and Communications Technology, Tokyo, Japan. His research interests include wired/wireless broadband access networks, optical network design optimization and survivability analysis, and green information and communication technologies.


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Massimo Tornatore (S’03–M’07) received the Laurea degree in telecommunications engineering in 2001 and the Ph.D. degree in information engineering from Politecnico di Milano, Milano, Italy, in 2001 and 2006, respectively. He was a Postdoctoral Researcher in the Department of Computer Science, University of California, Davis for two years. He is currently an Assistant Professor in the Department of Electronics and Informatics, Politecnico di Milano. He has authored or coauthored more than 70 papers published in various international journals and conference proceedings. His research interests include design, energy efficiency, traffic grooming in optical wavelength-division multiplexing networks, and group communication security.

Charles U. Martel received the B.S. degree in computer science from the Massachusetts Institute of Technology , Cambridge, in 1975, and the Ph.D. degree in computer science from the University of California, Berkeley, in 1980. Since 1980, he has been a Professor at the University of California, Davis. He was one of the founders of the Department of Computer Science, University of California, Davis, and was the Chairman of the department during 1994–1997. He is involved in research on a broad range of combinatorial algorithms including applications to networks, parallel and distributed systems, scheduling, and security. His current research interests include design and analysis of network algorithms, graph algorithms (particularly for modeling small worlds), and security algorithms. Dr. Martel won the World Bridge Championship five times.

Yi Zhang received the B.S. degree from Tsinghua University, Beijing, China, in 2007, where he is currently working toward the Ph.D. degree. He is a Visiting Ph.D. Student in the Department of Computer Science, University of California, Davis. His research interests inlcude Telecom backbone networks and energy-efficient Internet.

Biswanath Mukherjee (S’82–M’87–F’07) received the B.Tech. (Hons.) degree from the Indian Institute of Technology, Kharagpur, West Bengal, India, in 1980, and the Ph.D. degree from the University of Washington, Seattle, in 1987. Since 1987, he has been at the University of California, Davis, where he was the Chairman of the Department of Computer Science during 1997– 2000, and is currently the Child Family Endowed Chair Professor. He has authored the textbook Optical WDM Networks (Springer, 2006) and is the Editor of Springer’s Optical Networks Book Series. Dr. Mukherjee was the Technical Program Co-Chair of the Optical Fiber Communications Conference 2009 and was the Technical Program Chair of the IEEE INFOCOM’96 conference. He has been involved with the editorial boards of eight journals, most notably the IEEE/ACM TRANSACTIONS ON NETWORKING and IEEE NETWORK. He is Steering Committee Chair of the IEEE Advanced Networks and Telecom Systems (ANTS) Conference (the leading networking conference in India promoting industry–university interactions), and was the General Co-Chair of ANTS during 2007–2008. He is the co-winner of the Optical Networking Symposium Best Paper Awards at the IEEE Globecom 2007 and IEEE Globecom 2008 conferences. He has supervised to completion the Ph.D. dissertations of 40 students, and he is currently supervising approximately 20 Ph.D. students and research scholars. He was a Founding Member of the Board of Directors of IPLocks, Inc., a Silicon Valley startup company, for five years. He was involved with the Technical Advisory Board of a number of startup companies in networking, most recently Teknovus, Intelligent Fiber Optic Systems, and LookAhead Decisions Inc. (LDI).

Pulak Chowdhury (S’06) received the B.Sc.Eng. degree in computer science and engineering from the Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in 2002, and the M.A.Sc. degree from McMaster University, Hamilton, ON, Canada, in 2005. He is currently working toward the Ph.D. degree in the Department of Computer Science, University of California, Davis. His research interests include in wireless, optical, and hybrid wireless–optical broadband-access networks.