A Case Study in Network Architecture Tradeoffs∗

Nikolai MatniCalifornia Institute of

Technology1200 E California BlvdPasadena, California

nmatni@caltech.edu

Ao TangCornell University

337 Frank H. T. Rhodes HallIthaca, NY

atang@ece.cornell.edu

John C. DoyleCalifornia Institute of

Technology1200 E California BlvdPasadena, California

doyle@caltech.edu

ABSTRACTSoftware defined networking (SDN) establishes a separationbetween the control plane and the data plane, allowing net-work intelligence and state to be centralized – in this way theunderlying network infrastructure is hidden from the appli-cations. This is in stark contrast to existing distributed net-working architectures, in which the control and data planesare vertically combined, and network intelligence and state,as well as applications, are distributed throughout the net-work. It is also conceivable that some elements of networkfunctionality be implemented in a centralized manner viaSDN, and that other components be implemented in a dis-tributed manner. Further, distributed implementations canhave varying levels of decentralization, ranging from myopic(in which local algorithms use only local information) tocoordinated (in which local algorithms use both local andshared information). In this way, myopic distributed archi-tectures and fully centralized architectures lie at the twoextremes of a broader hybrid software defined networking(HySDN) design space.

Using admission control as a case study, we leverage re-cent developments in distributed optimal control to providenetwork designers with tools to quantitatively compare dif-ferent architectures, allowing them to explore the relevantHySDN design space in a principled manner. In particu-lar, we assume that routing is done at a slower timescale,and seek to stabilize the network around a desirable operat-ing point despite physical communication delays imposed bythe network and rapidly varying traffic demand. We showthat there exist scenarios for which one architecture allowsfor fundamentally better performance than another, thushighlighting the usefulness of the approach proposed in thispaper.

∗N. Matni & J. C. Doyle were in part supported by theAFOSR and the Institute for Collaborative Biotechnologiesthrough grant W911NF-09-0001 from the U.S. Army Re-search Office. A. Tang was in part supported by the ONRunder grant N00014-12-1- 1055.

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DOI: http://dx.doi.org/10.1145/2774993.2775011.

Categories and Subject DescriptorsC.2.1 [Network Architecture and Design]: Miscella-neous; C.4 [Performance of Systems]: Miscellaneous.

General TermsDesign, Theory, Performance

1. INTRODUCTIONA common challenge that arises in the design of networks

is that of achieving globally optimal behavior subject to thelatency, scalability and implementation requirements of thesystem. Many system properties and protocols – such as net-work throughput, resource allocation and congestion avoid-ance – are inherently global in scope, and hence benefit fromcentralized solutions implemented through Software DefinedNetworking (SDN) (e.g., [3, 4, 9]). However, such central-ized solutions are not always desirable due to latency andscalability constraints – in particular the delay inherent incommunicating the global state of the network, solving aglobal optimization problem and redistributing the solutionto the network, can often negate the benefits achieved fromthis holistic strategy. In these cases, distributed networkingsolutions can be preferable (e.g., [1]).

Traditionally, there has been very little in the way of the-oretical tools at the disposal of network designers to quan-titatively compare different architectures. Because of this,the appropriateness of an architectural decision could onlybe confirmed once a suitable algorithm had been prototypedand validated via experiment. This process is time consum-ing and expensive, and even worse, can be inconclusive. Inparticular, if an algorithm performs poorly, it is not clearif this poor performance is due to an inherent limitation ofthe chosen architecture, or simply due to a poorly designedalgorithm, making it very difficult to quantitatively comparenetwork architectures.

In this paper, we argue that due to both the increasedflexibility afforded to network designers by SDN and to re-cent advances in distributed optimal control, the gap be-tween theory and practice has closed significantly. Althoughthe gap has not yet completely closed, we show that in thecontext of certain network applications, existing theory canindeed be used to quantitatively compare the fundamen-tal performance limits of different network architectures. Inparticular, we leverage the fact that new theory can nowexplicitly take into account the effect of communication de-lays inherent to coordinating control actions in a networkedsetting.

Towards this end, we define the broader Hybrid SoftwareDefined Networking (HySDN) design space (§2) of networkarchitectures, and argue that an important metric in deter-mining the appropriateness of an architecture is the optimalperformance achievable by any algorithm implemented onthat architecture. This additional metric can then be usedby a network designer to quantify the performance tradeoffsassociated with using a simpler or more complex architec-ture, allowing for more informed decisions about architec-ture early in the design process.

We further explain how recent advances in distributedoptimal control theory allow us to apply this approach toa class of network control problems in which the objec-tive is to regulate network state around a pre-specified de-sired set-point. In §3, we illustrate the usefulness of thisapproach with an admission control case study. Perhapssurprisingly, we show that for two nearly identical routingtopologies, there can be significant differences in the per-formance achievable by centralized and distributed networkarchitectures.

We emphasize that we are not arguing that a specific im-plementation, algorithm or architecture is best suited for agiven application – rather, we are illustrating the usefulnessof a methodology that allows network designers to makequantitative decisions about architectural choices early inthe design process. We are also not proposing that thismethod replace the important steps of simulation, proto-typing and experiments, but rather as a complement to it.Indeed, in order to achieve theoretical tractability, simpli-fying assumptions such as linearized flow models, constantdelays, reliable control packet communication and negligiblecomputation time are made – the validity of these assump-tions can only be confirmed through experiments. We dohowever believe that the proposed tools can help network de-signers streamline the prototyping process by allowing themto narrow down the range of potential architectures thatneed to be explored.

2. ARCHITECTURAL TRADEOFFSCompletely distributed network architectures, in

which local algorithms take local actions using only locallyavailable information, and centralized network architectures,in which a centralized algorithm takes global action usingglobal information, can be viewed as lying at the extremesof a much richer design space. It is possible to build a net-work architecture in which certain network logic elementsare implemented in a centralized fashion via SDN, and inwhich other network logic elements are implemented in adistributed fashion. Further, distributed architectures canhave varying levels of decentralization, ranging from com-pletely distributed (as described above), or myopic, archi-tectures to coordinated distributed architectures, in whichlocal algorithms take local actions using both locally avail-able information and shared subsets of global state informa-tion. We call this broad space of architectures the HybridSoftware Defined Networking (HySDN) design space (illus-trated in Figure 1), as its constituent architectures are nat-urally viewed as hybrids of distributed and software definednetworks.

The question then becomes how to explore this even largerdesign space in a systematic way. As we have already al-luded to, there are inherent tradeoffs associated with any ar-chitecture: algorithms running on centralized architectures

typically achieve better steady state performance, but oftenreact with higher latency than those implemented on a dis-tributed architecture – conversely distributed algorithms areoften simpler to implement but can lead to less predictablesteady state performance.

We pause here to recognize that network architectures andalgorithms are judged by many different metrics, includingbut not limited to performance, scalability, ease of deploy-ment and troubleshooting, and flexibility. We argue thatas much as possible, each of these different metrics shouldbe traded off against each other in a quantitative way – forexample, it is important for a network designer to be ableto quantify the performance degradation suffered by using anetwork architecture or algorithm that is simpler to deployand troubleshoot. Our approach to exploring these tradeoffsis simple: we compare network architectures by comparingthe optimal performance achievable by any algorithm im-plemented using them. This approach allows for a networkdesigner to compare fundamental limits of achievable per-formance across different architectures. The approach alsoallows for the comparison of the performance of more scal-able, flexible, or easier to deploy algorithms implementedusing a given network architecture to that network architec-ture’s best possible performance, allowing for a quantifiabletradeoff between these metrics of interest.

In order to make the discussion concrete, we focus on algo-rithms that can be viewed as controllers that aim to keep thestate of the network as close to a nominal operating point aspossible, while exchanging and collecting information sub-ject to the communication delays imposed by the network.For example, in §3, we consider admission control algorithmsthat aim to keep the link flow rates at a user-specified set-point while minimizing the admission buffer size, despitephysically imposed communication delays and rapidly vary-ing source rates. In particular, we are not addressing theproblem of determining what nominal operating point thecontrollers should attempt to bring the network state to –we aim to extend our analysis to such problems in futurework. We recognize that this measure of performance is notstandard in the networking community, but note that it is anatural one to consider when explicitly taking into accountrapidly varying and unpredictable source rates.

By restricting ourselves to problems of this nature, wecan leverage recent results in distributed optimal controltheory to classify those architectures for which the opti-mal algorithm and achievable performance can be computedefficiently.1 It is well known that the optimal centralizedcontroller, delayed or not, can be computed efficiently viaconvex optimization [13]. Further, it is known that myopicdistributed optimal controllers are in general NP-hard tocompute [12, 8]. Up until recently however, it was unclear ifand when coordinated distributed optimal controllers couldbe specified as the solution to a convex optimization prob-lem.

The challenge inherent in optimizing distributed controlalgorithms is that control actions (e.g., local admission con-trol decisions) can potentially serve two purposes: actionstaken by local controllers can be used to both control thestate of the system in a manner consistent with performance

1Throughout this discussion, we assume that the dynamicsof the network are linear around a neighborhood of the nom-inal operating point. This assumption holds true for manycommonly used network flow models [5, 2].

(a) Distributed Networking(b) Hybrid Software Defined Network-ing

(c) Centralized Software Defined Net-working

Figure 1: The Hybrid Software Defined Networking Design space, ranging from distributed (Fig 1a) to centralized (Fig 1c)network protocols.

objectives, and to signal to other local controllers, allowingfor implicit communication and coordination. Intuitively, itis this attempt to both control the system and to implic-itly communicate that makes the problem difficult to solvecomputationally. However, if local controllers are able tocoordinate their actions via explicit communication, ratherthan by implicit signaling through the system, then the op-timal controller synthesis problem becomes computationallytractable [11, 10]. Further it is not difficult to argue thatdistributed controllers using explicit communication to co-ordinate will outperform those relying on implicit signal-ing through the system. Removing the incentive to signalthrough the system can be done in a network control settingby giving control dedicated packets, i.e., packets containingthe information exchanged between local algorithms, prior-ity in the network.2

These theoretical developments thus provide the neces-sary tools to explore a much larger section of the HySDNdesign space in a principled manner. We propose leverag-ing these results to compare the performance achievable byalgorithms implemented on four different classes of architec-tures, described below:

1. The GOD architecture: in order to quantify thefundamental limits on achievable performance, we pro-pose computing the optimal controller implementedusing the Globally Optimal Delay-free (GOD) archi-tecture. This architecture assumes instantaneous com-munication to and from a central decision maker –although not possible to implement, the performanceachieved by this architecture cannot be beaten, andas such represents the standard against which otherarchitectures should be compared.

2. The centralized architecture: this architecture cor-responds to the SDN approach, in which a centralizeddecision maker collects global information, computes

2Specifically, if local controllers can communicate with eachother as quickly as the effect of their actions propagatethrough the network, then the resulting optimal controlproblem is convex. By giving such communication packetspriority in the network and ensuring that they are routedalong suitably defined shortest paths, this property is guar-anteed to be satisfied [10].

a global control action to be taken, and broadcasts itto the network. Although global in scope, the latencyof algorithms implemented using this architecture isdetermined by the communication delays inherent incollecting the global network state and broadcastingglobal actions.

3. The coordinated architecture: this architecture isdistributed, but allows for sufficient coordination be-tween local controllers so that the optimal control lawcan be computed efficiently [11, 10]. This architecturetakes both rapid action based on timely local infor-mation, and slower scale action based on global butdelayed shared information, and can thus be viewed asan intermediate between centralized and myopic archi-tectures.

4. The myopic architecture: this architecture is onein which local controllers take action based on localinformation. Although the optimal controller cannotbe computed, the performance achieved by any my-opic controller can be compared with the performanceachieved by an optimal coordinated controller, thusproviding a bound on the performance difference be-tween the two architectures.

It should be noted that the coordinated architecture willalways perform at least as well as the myopic and centralizedarchitectures, as any algorithm implemented on the latterarchitectures can also be implemented on the former. It isclear why this holds for myopic algorithms, but it is worthemphasizing why this holds for centralized algorithms. Thisis true because the delays faced by a coordinated distributedalgorithm in collecting and sharing state information are alsofaced by a centralized algorithm. Whereas a centralized al-gorithm waits until the global state has been collected andprocessed to react to make changes to the system, a coordi-nated distributed algorithm takes both local timely actionsbased on local information and delayed actions based onshared information.

What we seek to understand is how large of a gap in per-formance exists between these different architectures. Bycomputing the performance of each of these architectures,the network designer can then quantify tradeoffs in imple-mentation complexity and performance in a computationally

efficient and inexpensive manner. We demonstrate the use-fulness of this approach on an admission control case studyin the next section.

3. ADMISSION CONTROL DESIGNIn this section we pose an admission control problem, de-

fine the relevant HySDN design space and show that it canbe explored in a principled and quantitative manner usingtools from distributed optimal control theory. We discussthe problem at a conceptual level in this section, and referthe interested reader to [7] for the technical details.

3.1 ProblemWe consider the following admission control task: given

a set of source-destination pairs (s, d), a set of desired flowrates f�

�,(s,d) on each link � for said source-destination pairs,and a fixed routing strategy that achieves these flow rates,design an admission control policy that maintains the linkflow rates f�,(s,d)(t) as close as possible to f�

�,(s,d) while min-imizing the amount of data stored in each of the admissioncontrol buffers, despite fluctuations in the source rates xs(t).

The architectural decision that the network designer isfaced with is whether to implement the admission controlpolicy in a myopic, coordinated, or centralized manner –representative examples of these possible architectures areillustrated in Figure 2 for the case of three sources. In themyopic distributed architecture, local admission controllersAC1, AC2 and AC3 have policies that depend solely on theirlocal information – in what follows, we define the local in-formation available to a local algorithm for the specific casestudies that we consider. In the coordinated distributed ar-chitecture, the local admission controllers take action basedon both locally available information and on informationshared amongst themselves – this shared information is de-layed, as it must be communicated across the network and istherefore subject to propagation delays. Finally, in the cen-tralized architecture, a central decision maker collects theadmission control buffer and link flow rate states subject toappropriate delays, determines a global admission controlstrategy to be implemented and broadcasts it to the localAC controllers – this strategy also suffers from delay due tothe need to collect global state information and to broadcastthe global policy to each AC controller.

As mentioned in the previous section, we know a priorithat the coordinated distributed optimal control architec-ture will perform no worse than either the myopic or cen-tralized architecture. Conversely, the myopic and central-ized schemes are significantly easier to deploy, and the cen-tralized scheme is significantly easier to troubleshoot. Thus,in quantifying the gaps in performance between the cen-tralized, myopic and distributed architectures, the networkdesigner is able to make an informed decision as to whetherthe added complexity of the coordinated distributed schemeis warranted.

As described in §2, our approach to exploring the HySDNdesign space is to compute optimal admission controllers im-plemented on each of these different architectures. In par-ticular we compute admission controllers that minimize aperformance metric of the form

N∑

t=1

∑

�,(s,d)

(f�,(s,d)(t)− f�

�,(s,d)

)2+ λ‖A(t)‖22, (1)

Noisy sourcexs(t) = x�s(t) + δs(t)

eδ (t)

AdmissionControlBuffer

As(t)

Admitted flowas(t)

Figure 3: Diagram of an edge admission controller.

AdmissionControlBuffer

As(t)AAA

Ingoing flowsf1(t)

f2(t)

f3(t))))

a3(t)

a2(t)a1(t)Admittedflows

Figure 4: Diagram of an internal admission controller.

where A(t) is a vector containing the size of the admissioncontrol buffers and N is the optimization horizon. Thus thecontrollers aim to minimize a weighted sum of flow rate de-viations and admission queue lengths over time, where λ > 0determines the relative weighting assigned to each of thesetwo terms in the final cost. By solving the correspondingoptimal control problems, we obtain two parameters: anoptimal cost and an admission control policy that achievesit. These optimal costs thus serve as a quantitative measureof the performance of a given architecture, as by definition,they correspond to the best performance achievable by anyadmission control policy implemented on that architecture.

3.2 Case StudyWe consider a simple routing topology overlaid onto the

abilene network, and two different admission control scenar-ios: one in which only edge admission control is allowed (cf.Figure 3) and one in which edge and internal admission con-trol is allowed (cf. Figure 4). We model the dynamics of thesystem using a flow based model and solve the optimal con-trol problem with the cost (1) taken to be the infinite horizonLQG cost using the methods described in [13] and [6] – werefer the reader to [7] for the technical details. Intuitively,this cost measures the amount of “energy” transferred fromthe source rate deviations to the flow rate deviations andbuffer sizes. We assume that the nominal source rates x�

s(t)and the nominal flow rates f�

�,(s,d) are all equal to 1 (thisis without loss of generality through appropriate normaliza-tion of units), and empirically choose λ = 50 based on theobserved responses of the synthesized controllers.

We compute three optimal controllers for each of the sce-narios considered: a coordinated distributed optimal con-troller in which local admission controllers are able to ex-change information via the network in order to coordinatetheir actions, as illustrated in the middle pane of Figure2, a centralized optimal controller subject to the delays in-duced by collecting global state information and broadcast-ing a global control action, and the GOD controller. We alsocompare the performance of these controllers with the per-formance achieved by the best myopic distributed controllerwe are able to compute via non-linear optimization (recallthat optimal myopic controllers are in general computation-ally intractable to compute).

Figure 2: The HySDN design space for an admission control problem with three sources. Blue arrows denote the flow of traffic,whereas dashed black lines denote the flow of admission control related information. Note that the dotted lines correspond tovirtual connections, and can be implemented using either control dedicated communication links, or using control dedicatedtunnel overlays on the network.

Figure 5: Routing topology used for case study: each source-destination path is denoted by a dashed line. Sources 1 and2 have edge admission controllers as depicted in Figure 3,whereas Source 3 either has an edge admission controlleror an internal admission controller, as depicted in Figure 4,depending on the scenario considered.

We present two different settings to illustrate the benefitof our approach to exploring the HySDN design space: onein which using a coordinated distributed algorithm leads toa significant improvement over a centralized algorithm anda slight improvement over a myopic distributed algorithm,and one for which the optimal GOD algorithm is inherentlymyopic in nature. In the former case, the significant im-provement over a centralized implementation justifies theuse of a coordinated or myopic architecture, whereas in thelatter case, the myopic distributed architecture is the clearchoice as it achieves the same performance as a controllerimplemented using the GOD architecture.

The topology that we consider is illustrated in Figure 5– source-destination pairs are illustrated with dashed lines,and each source has an admission controller. We first con-sider the edge only admission control scenario, where eachadmission controller is as depicted in Figure 3, and computethe optimal controller implemented using the GOD archi-tecture. Perhaps surprisingly, the optimal control policy isnaturally myopic, i.e., the admitted flow as(t) at admissioncontrol buffer s is strictly a function of As(t). In other words,there is no loss in performance in using a myopic distributed

architecture so long as the local control actions are appro-priately specified.

We next consider a scenario where Sources 1 and 2 haveedge admission controllers, but now Source 3 has an internaladmission controller as depicted in Figure 4. In particular,the internal admission controller takes as inputs both theincoming flows due to Sources 1 and 2 arriving at the Den-ver switch, as well as the contribution from Source 3. Weonce again begin by computing the optimal controller im-plemented using the GOD architecture. In this case, theGOD policy requires instantaneous sharing of informationbetween different admission controllers and hence cannot beimplemented. We then compute a centralized optimal con-troller, in which we assume that the central decision makeris located at the Denver switch. At this location, the largestround trip time between Denver and an admission controlleris 18ms: we assume that computation time is negligible, andthus, it takes 18ms for the centralized decision maker to re-act to local changes.

We also compute the optimal coordinated distributed con-troller, in which we assume that admission controllers haveaccess to flow rate and admission control buffer informationwith delay specified by the routing topology. For example,the admission control buffer at Source 1 has access to theadmission control buffer state at Source 2 with a delay of3ms and to the admission control buffer state at the Den-ver switch with a delay of 6ms. This information sharingprotocol is sufficient for the optimal coordinated distributedcontroller to be specified by the solution to a convex op-timization problem, and can be computed efficiently usingthe methods in [6]. Finally, we bound the performance ofthe myopic architecture using the best myopic distributedalgorithm that we are able to generate via non-linear opti-mization.

We normalize the costs achieved by each of the architec-tures by the performance achieved by the GOD architecture:in this way, a ratio of 1 corresponds to the best possible per-formance achievable. The optimal algorithm implementedusing the centralized architecture achieved a cost ratio of1.13 – thus the delay needed to take centralized decisionsleads to a 13% performance degradation over the GOD ar-chitecture performance. We then computed the optimal co-

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Figure 6: Sample flow deviations and admission bufferlength evolution under the GOD and coordinated dis-tributed controllers when the system is subject to the ran-dom source rate fluctuations, illustrated in Figure 7.

0 0.5 1 1.5−1

−0.5

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Δ x

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Figure 7: Sample source rate fluctuations – these are lowerbounded by -1 as the nominal source rates are all assumedto be 1.

ordinated distributed controller and obtained a cost ratio of1.01 – thus, with a mild amount of coordination, a realisticcontroller implementation can achieve performance nearlyidentical to that of a controller using the GOD architecture.Finally, the best myopic distributed controller we were ableto synthesize achieved a cost ratio of 1.04 – these results aresummarized in Table 1.

A representative example of flow deviation and admissionbuffer length evolution under the GOD and coordinated dis-tributed controllers can be found in Figure 6 – the drivingsource rate deviations are illustrated in Figure 7. As thereis very little quantitative difference in the performance ofthese two controllers, one does not expect to see a qualita-tive difference in the state trajectories.

Thus, in this scenario there is a significant quantifiableadvantage in performance when adopting either a myopicor coordinated distributed architecture over a centralized

GOD Myopic Coordinated CentralizedRatio 1 1.04 1.01 1.13

Table 1: Summary of admission control case study resultsfor edge and internal admission control scenario.

architecture, indicating that the increased complexity in de-ployment and troubleshooting may be worthwhile. The dif-ference in performance between the two distributed archi-tectures considered is much less significant – in this case, itis up to the network designer to choose whether the addi-tional complexity of implementing a coordinated algorithmis worth the 3% performance gain over the proposed myopicalgorithm.

Although the differences in performance in this case studyranged from 1% to 13%, in general, the gap between cen-tralized and distributed architectures can become arbitrar-ily large as the network size (as measured in terms of end toend communication delays) increases. In particular, as thenetwork size increases, the centralized approach requires anincreasingly larger delay to collect and take global actions,leading to a corresponding degradation in performance. How-ever, this is not true for the coordinated distributed archi-tecture, as local actions are taken without delay, and thedelay needed for two controllers to exchange information tocoordinate their control actions is independent of the rest ofthe network.

4. SUMMARYIn this paper, we propose a methodology for quantifying

the achievable performance of network architectures. We fo-cused on problems in which the task is to keep the networkstate near a nominal operating point despite physically im-posed delays and varying operating conditions. We showedhow recent results in distributed optimal control theory al-low for the efficient computation of optimal algorithms im-plemented using the GOD, centralized and coordinated dis-tributed architectures, and proposed using the performanceachieved by these controllers, as well as the performanceachieved by a candidate myopic distributed algorithm, asa means of comparing the respective network architectures.We applied this approach to an admission control case study,and in particular, discovered that for the topology consid-ered, the GOD policy can be implemented using a myopicdistributed architecture when only edge admission controlis allowed. However, if internal admission control is used, acoordinated distributed architecture leads to the best per-formance.

We believe that our proposed approach could play an im-portant and complementary role to the traditional exper-imental validation of network architectures and algorithmsby allowing network designers to narrow the range of promis-ing architectures earlier in the design process, and by let-ting them quantifiably measure the effects on performanceof other metrics such as scalability, ease of deployment andtroubleshooting, and flexibility.

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