680 IEEE COMMUNICATIONS LETTERS, VOL. 18, NO. 4, APRIL 2014

Effects of Cooperation Policy and Network Topology onPerformance of In-Network Caching

Liang Wang, Suzan Bayhan, and Jussi Kangasharju

Abstract—In-network caching is a key component ininformation-centric networking. In this paper we show that thereis a tradeoff between two common caching metrics, byte hit rateand footprint reduction, and show that a cooperation policy canadjust this tradeoff. We model the cooperation policy with onlytwo parameters – search radius r and number of copies in thenetwork K. These two parameters represent the range of coop-eration and tolerance of duplicates. We show how cooperationpolicy impacts content distribution, and further illustrate therelation between content popularity and topological properties.Our work leads many implications on how to take advantage oftopological properties in in-network caching strategy design.

Index Terms—In-network caching, cache network,information-centric network, cache cooperation, topology,performance analysis.

I. INTRODUCTION

CACHING is a key component in information-centricnetworks (ICN) [1]–[4]. In-network caching not only

reduces an ISP’s outgoing traffic, but also reduces trafficwithin an ISP network. Byte hit rate (BHR) is a commonmetric for evaluating savings in inter-ISP traffic and footprintreduction (FPR) [5] measures the amount of traffic eliminatedby caching (as product of traffic volume and distance ittravels in the network) and is the metric we use for intra-ISP traffic savings. We show that BHR by itself is insufficientin capturing the performance of a network of caches; this isoften overlooked by existing work. This paper shows that thereis a subtle interplay between BHR and FPR and that in somecases these two metrics oppose each other. We argue that acooperation policy among routers can mediate this tradeoffbetween BHR and FPR and show that two parameters can tunethe desired operating region: maximum number of duplicatesfor each content item (K) and the radius for cooperation (r).

To improve BHR, a cooperation policy covering a largeradius enhances network storage utilization by reducing thenumber of duplicates (cache diversity in [6]). However, large-scale cooperation causes communication overheads and in-creases intra-ISP traffic because requests may be redirectedmany times. Despite efforts in designing a cooperation pol-icy [7], [8], [10], a proper model for its impact on BHR andFPR is still missing. We characterize cooperation policy by itssearch strength (r) and capability of reducing duplicates (K).We show how different r and K values lead to different trade-offs between BHR and FPR, and discuss their implication.

There is considerable interest in exploiting topologicalproperties in cache networks. Initial efforts [11], [12] indicatecentrality as a promising metric, but questions like how to

Manuscript received November 29, 2013. The associate editor coordinatingthe review of this letter and approving it for publication was B. Rong.

The authors are with the Department of Computer Science, University ofHelsinki, Finland (e-mail: liang.wang@cs.helsinki.fi).

Digital Object Identifier 10.1109/LCOMM.2014.013114.132647

measure the topological impact on performance and mecha-nism of the interplay between topology, and caching strategystill remain open. We use a cache cooperation policy to couplecontent with topology and show that this coupling explainshow topological properties impact caching performance; thetightness of the coupling indicates degree of topology’s im-pact.

Our contributions in this paper are as follows:1) We highlight the importance of FPR as a performance

metric for in-network caching, and show how BHR andFPR conflict each other at the Pareto frontier.

2) We propose a cooperation policy model to show therelationship between cooperation policy, content, andtopology. We also categorize different cooperation types.

3) We propose a novel way to measure the impact oftopology, and perform a thorough numerical analysis toshow how it influences system performance.

II. SYSTEM MODEL

Consider a network of M routers, L of which directlyreceive user requests and are edge routers. A router denotedby Ri is equipped with a storage capacity of Ci bytes. Weassume N distinct files, denoted by fi and being si bytes insize. All files are stored permanently at the Content Provider(CP) represented as the (M+1)th router (RM+1). Denote therequest probability of fi by this file’s popularity pi, and denotethe popularity vector by p = [pi]. When a request for fi arrivesto an edge router Rj (j = {1, ..., L}), Rj first searches fi inits cache. If Rj possesses it, Rj transmits fi to the user; thisa hit. Otherwise, in case of a miss, Rj contacts routers inits r-hop neighborhood to see if any of them has fi; this isthe cooperation policy. We call the set of all routers locatedat most r-hops away from Rj as the searchable set of Rj ,denoted by Sc

j . If fi is stored in Scj , it is retrieved to Rj

from the closest router (if multiple routers holding fi) andforwarded to the user. Let Rj,CP be the set of all routers onthe path between a edge router Rj and the CP (excluding theCP). If no router in Sc

j stores the item, the request is routed tothe next router in Rj,CP and searched there as well as in thenew searchable set; there may be overlap between searchablesets of two neighboring routers, depending on r. We definethe reachable set of a router denoted by Sr

j as the set of allrouters in the searchable sets of routers in Rj,CP . If no routerin Sr

j has fi, it is downloaded from the CP and routed to theuser following the backward path.

III. OPTIMAL IN-NETWORK CACHING

A. Cooperation Policy Design

Performance of a cooperation policy is determined bycontents in the searchable set which is a function of r. The

1089-7798/14$31.00 c© 2014 IEEE

WANG et al.: EFFECTS OF COOPERATION POLICY AND NETWORK TOPOLOGY ON PERFORMANCE OF IN-NETWORK CACHING 681

diversity of contents cached in this set increases cachingefficiency which then calls for a caching scheme avoidingduplicate copies in the set [8], [9]. However, popular contentmay better be cached in multiple routers to be more accessiblefrom all network edge routers. We model this tradeoff withparameter K which is the maximum number of contentreplicas in the network. In reality every file would have its ownmaximum number of copies which emerges automatically if ris fixed; we use a fixed K to illustrate system behavior acrossthe whole parameter range. Using these two parameters, wename a cooperation policy with parameters K and r as (K,r)-Cooperation Policy, classified into four as follows:

1) Type I, small r, small K: Weak cooperation due tolimited access to other caches and limited availability ofpopular content; the system is not using all its resources.

2) Type II, small r, large K: This is en-route caching. Themost popular content is pushed to the network edge.

3) Type III, large r, small K: Network storage is effec-tively a single cache. Popular content is in network core.

4) Type IV, large r, large K: Strong cooperation. BHRand FPR cannot be improved at the same time sincecaching system is fully-utilized and reaches its Paretofrontier.

The complexity of cooperation can be calculated via com-munication and computation overhead [8]. Initially, all routersexchange their set of stored contents with routers in theirsearchable set. Assuming that each content is unit size anddropping the router index, this initialization step requiresO(MC|Sc|) messages and results in O(M2C) message ex-changes in the worst case. Upon a change in the cache of arouter, this router informs all its r-hop neighbours about theevicted and admitted items. This per change announcementrequires O(|Sc|) message in the worst case. In terms ofcomputation, the cooperation does not involve any processingrather than discovering which of the replicas is closest to aspecific router. Therefore, computation overhead is O(|Sc|).

B. Optimal Caching under (K, r)-Cooperation Policy

Assume a centralized entity knowing the content distribu-tion at time t, Xt = [xi,j ] where xi,j is 1 if fi is stored at Rj ,and zero otherwise. At time t a user issues a request to Rl forfu which is stored at Rhit. Denote the set of intermediaterouters on the path between Rl and Rhit by S, and theextended set S ∪RM+1 by S+. An optimal caching strategy(COPT) determines whether to cache fu in the routers in S,and if to be cached which items to evict in case of full cacheoccupancy.

A requested item can be served from edge router Rj orretrieved from another router Rk including the CP. Let cj,kdenote the cost of downloading one byte at Rj from Rk. Thecost function reflects the distance between the two entities andcan be calculated using shortest path algorithms. For a (K, r)-Cooperation Policy, as the routers not in Sr

j are not reachablefrom Rj , we set cj,k = ∞ if Rk �∈ Sr

j . Let our decisionvariables Yt = [yi,j,k] ∈ {0, 1} be 1 only if Rj downloads fifrom Rk. Note that if yi,j,j = 1, then fi is stored in Rj . COPT

aims to minimize the total cost of serving all user requestsarriving to all edge routers in the long run (first term in (1)) and

cost of serving fu for which user request is just received andcan be cached in one of the routers in S (second term). COPT

exploits its knowledge of current content distribution Xt, filepopularities (p), and file size (si) information as follows:

minN∑i=1

L∑j=1

M+1∑k=1

sipicj,kyi,j,k+supu

L∑j=1

∑Rk∈S+

cj,kyu,j,k

(1)

s.t. Cache capacity constraints:N∑i=1

sixi,jyi,j,j+suyu,j,j(1−xu,j)≤Cj ∀Rj ∈ S (2)

N∑i=1

siyi,j,j≤Cj ∀Rj �∈ S (3)

Maximum replica constraint:M∑j=1

yi,j,j ≤ K ∀i (4)

Feasibility constraints: yi,j,k ≤ yi,k,k ∀i, ∀j, ∀k (5)

yi,j,j = xi,j ∀i, ∀Rj �∈ S (6)

Service constraint: 1 ≤M+1∑k=1

yi,j,k ∀i, ∀j (7)

Availability constraint: yi,M+1,M+1 = 1 ∀i. (8)

Our objective (1) aims to minimize the serving cost byfavoring the most popular files. Cache capacity constraintsin (2) and (3) ensure the total size of items to be stored ina router’s cache cannot exceed cache capacity. Only routersin S can consider putting the requested item fu into theircaches. Maximum replica constraint in (4) ensures that anitem can have maximum K replicas in the network. Note thatby removing this constraint, system can figure out optimalK for each neighborhood automatically. Feasibility constraintin (5) reflects fi being retrievable from Rk only if Rk storesfi whereas (6) states that contents cached by routers not inS do not change. Service constraint (7) forces the requestsreceived at edge routers to be served from some location(i.e., local cache, another router’s cache, or the CP), whileavailability constraint in (8) ensures all items are availablefrom the CP. After COPT determines Yt, we update the newcontent distribution as Xt+1 = Yt for the next decision instant.COPT is an integer linear programming problem which can besolved with optimization software for small instances of theproblem but requires low-complexity distributed schemes forlarge scale networks. We leave distributed solutions for futurework.

Let Fj = {uj,1, uj,2, uj,3...} be the list of user requestsarriving at edge router Rj where uj,i is the ith request for afile with size suj,i . Rj can retrieve it only from its reachableset Sr

j which is defined as follow:

Srj =

⋃Rk∈Rj,CP

Sck. (9)

A request will be counted as hit if at least one of the routersin Sr

j stores it. More formally, we define hit function δj,i for

682 IEEE COMMUNICATIONS LETTERS, VOL. 18, NO. 4, APRIL 2014

Footprint Reduction

Byte H

it Rate

A

B

C

1 increase K

2 increase r

1

22

1

Cooperation Policy

Pareto FrontierD

C

B

D

Pareto Frontier

Type III

Type IV

Type II

Fig. 1: Conflicting BHR and FPR at the Pareto frontier. Typeof cooperation at different points shown on right.

request uj,i (assuming uj,i is a request for fi) as follows:

δj,i =

{1 if

∑Rk∈Sr

jyi,k,k ≥ 1

0 o/w.

Next, we calculate BHR as follows:

BHR =

∑Lj=1

∑uj,i∈Fj

suj,iδj,i∑Lj=1

∑uj,i∈Fj

suj,i

. (10)

If request uj,i is served from a router that is hj,i hops awayfrom the user, and the path from Rj to the CP is Hj hopslong, we can compute the FPR as follows:

FPR = 1−∑L

j=1

∑uj,i∈Fj

suj,ihj,i∑Lj=1 Hj

∑uj,i∈Fj

suj,i

. (11)

IV. NUMERICAL ANALYSIS

A. Setup and Metrics

We performed numerical evaluation on realistic and syn-thetic topologies. Realistic topologies are from [13], andsynthetic topologies are scale-free networks of 50 nodes. Eachnode can store 25 objects. We present results on syntheticnetworks; realistic topologies produce similar results. Contentpopularity is modeled according to [14], and content setcontains 5000 objects. We calculate the betweenness centrality(CB) of each router in order to analyze its impact on cachedcontent in a specific router under various (K, r) pairs. Wedefine coupling factor (CPF) as the Pearson correlation be-tween CB and average popularity per bit in a node’s cache;it measures topological impact on system performance. Therationale is that optimal system performance is achieved byplacing content at specific locations in a network accordingto its popularity and that CB is a good metric to characterizea node’s position in a graph. Strong correlation between thetwo indicates that content is tightly “coupled” with topologyand topological properties influence system performance.

In the simulations, 30% of the edge routers are randomlyselected and connected with client, and the server randomlyconnects to one of the 5 core nodes with highest CB . Exper-iments were repeated at least 50 times.

B. Pareto Frontier

Fig. 1 shows how K and r impact caching performance.The solution to COPT provides the optimal cache profiles forgiven K and r (e.g., point A in Fig. 1), but it does not indicatethe best values for these two parameters, i.e., we can improveperformance by tuning K and r, because the system may beunderutilized. However, our optimization model can be usedto find Pareto frontier of the performance (green arc BC inFig. 1). When we reach the Pareto frontier, we cannot improveBHR or FPR without hurting the other. The fan-shaped areadefined by ABC is the area which a cooperation policy canexplore to find the best tradeoff between K and r. Point Dwhere we eventually reach the Pareto frontier depends on howcooperation policy balances BHR and FPR. Lines AB and ACare not parallel to the x- and y-axis, since changing either ofr or K affects both BHR and FPR, as we show below.

The upper graph in Fig. 2a shows how BHR and FPR varyas we move along the segments AB, BC, and CA, by varyingr and K . Starting from A and moving clockwise (left to rightin the figure), we increase the search radius which improvesBHR, but decreases FPR due to additional search traffic orletting content be cached at routers with higher hj,i in (11).From B to C, along the Pareto frontier, we observe the tradeoffbetween BHR and FPR, with FPR reaching its maximum atC. From C to A, r is 0 so the system reduces to en-routecaching where larger number of copies (near C) is beneficial,hence as we move towards A, both BHR and FPR decrease.Fig. 2b shows heatmaps of CPF, BHR, and FPR as functionof K and r. Lighter values indicate higher values. It showshow BHR and FPR conflict each other, i.e., one achieving thehighest performance while the other has the worst, in regionscorresponding to the Pareto frontier.

C. Coupling Content and Topology

The lower plot in Fig. 2a shows how CPF evolves along thesame path. Values close to -1 or 1 indicate strong dependencebetween popularity and betweenness. A router with a high CB

is in or close to the core of the network whereas a router withlow CB is close to the network edge. At B, where CPF isclose to 1, popular content is in nodes with high CB , i.e., thecore, whereas at C, where CPF is close to -1, it is at the edgewhere CB is low. Along the Pareto frontier BC, we observe a“migration” of content from core to edge. At D where CPF is0, both BHR and FPR are close to halfway point between theirrespective minima and maxima at B and C. We have observedthis phenomenon across a wide range of experimental settings,but its full investigation is left for further study.

Fig. 3 shows cooperation policy’s impact on content place-ment along the Pareto frontier BC. Point B (Fig. 3a), repre-senting Type III cooperation favors BHR and places contentin the core. Point D along BC (Fig. 3b) strikes a tradeoffbetween BHR and FPR and the content is neither in the corenor on the edge; this is Type IV cooperation. Finally, point C(Fig. 3c) favors FPR and pushes popular content to the edge.

D. Implications

Our results have profound implications on relationshipbetween cooperation policies, content popularity, and network

WANG et al.: EFFECTS OF COOPERATION POLICY AND NETWORK TOPOLOGY ON PERFORMANCE OF IN-NETWORK CACHING 683

0.3

0.5

0.7

0

0.1

0.2B

HR

FP

R

BHRFPR

-1-0.5

0 0.5

1

A B D C A

CP

F

CPF

(a) Change in BHR (Top plot, left y-axis), FPR (top, right y-axis), and CPF (bottom) along AB, BC, and CA.

r

K

FPR

FP

R

r BHR

BH

R

r CPF

CP

F

(b) CPF, BHR, and FPR as afunction of K and r.

Fig. 2: Performance of (K, r)-Cooperation along the boundary defined by ABC (a), and for all (K, r) pairs (b).

0.2<CB ≤1.0

0.1<CB ≤0.2

0.0<CB ≤0.1

(a) Point B in Fig. 1.

0.2<CB ≤1.0

0.1<CB ≤0.2

0.0<CB ≤0.1

0.2<CB ≤1.0

0.1<CB ≤0.2

0.0<CB ≤0.1

(b) Point D in Fig. 1.

0.2<CB ≤1.0

0.1<CB ≤0.2

0.0<CB ≤0.1

0.2<CB ≤1.0

0.1<CB ≤0.2

0.0<CB ≤0.1

0.2<CB ≤1.0

0.1<CB ≤0.2

0.0<CB ≤0.1

(c) Point C in Fig. 1.

Fig. 3: Content placement at points B, D, and C. Red color (dark dots) marks the most popular content. Nodes are groupedin circles according to CB . Content migrates from core to edge as we move from B to C.

topology. They also give us hints on when and how topologicalproperties should be taken into account in caching strategydesign. We summarize the main implications as follows:

1) Cooperation policy pushes performance to Pareto fron-tier and couples content popularity and topological prop-erties together. How and where it falls on the frontierdepends on how it balances BHR and FPR.

2) Content popularity and topology strongly correlate witheach other only close to the Pareto frontier. Whether thecorrelation is positive or negative depends on how thecooperation policy favors one of the two metrics.

3) The optimization model implies that CB has moreinfluence on performance when we get closer to points Aor B in Fig. 1; only Type II and III cooperation policiescan fully utilize CB to enhance performance.

4) We conjecture that tight coupling between contentpopularity and topology comes similar mathematicalstructures as both exhibit power-law properties. Werepopularity closer to uniform or topology closer to arandom network, this tight coupling might disappear.

V. CONCLUSION

We modeled cache cooperation by its search radius andtolerance of duplicates. We performed a thorough numericalanalysis and showed that cooperation policy pushes systemperformance to its Pareto frontier, and how it couples contentwith topology. We proposed a way to measure impact oftopology on system performance. We show when and howtopological information should be taken into account in in-network caching strategy design.

REFERENCES

[1] V. Jacobson, et al., “Networking named content,” in Proc. 2009 ACMCoNEXT.

[2] T. Koponen, et al., “A data-oriented (and beyond) network architecture,”in Proc. 2007 ACM SIGCOMM.

[3] Publish/Subscribe Internet Routing Paradigm, “Conceptual architectureof PSIRP including subcomponent descriptions. Deliverable d2.2, PSIRPproject,” Aug. 2008.

[4] C. Dannewitz, “Netinf: an information-centric design for the futureinternet,” in 2009 GI/ITG KuVS Workshop on The Future Internet.

[5] A. Anand, V. Sekar, and A. Akella, “SmartRE: an architecture forcoordinated network-wide redundancy elimination,” in Proc. 2009 ACMSIGCOMM.

[6] G. Rossini and D. Rossi, “Evaluating CCN multi-path interest forward-ing strategies,” Computer Commun., vol. 36, no. 7, pp. 771–778, 2013.

[7] Y. Li, H. Xie, Y. Wen, and Z.-L. Zhang, “Coordinating in-networkcaching in content-centric networks: model and analysis,” in 2013 IEEEICDCS.

[8] V. Sourlas, L. Gkatzikis, P. Flegkas, and L. Tassiulas, “Distributed cachemanagement in information-centric networks,” IEEE Trans. Network andService Management, vol. 10, no. 3, pp. 286–299, 2013.

[9] J. M. Wang, J. Zhang, B. Bensaou, “Intra-AS cooperative cachingfor content-centric networks,” in 2013 ACM SIGCOMM Workshop onInformation-Centric Networking.

[10] S. Saha, A. Lukyanenko, and A. Yla-Jaaski, “Cooperative cachingthrough routing control in information-centric networks,” in 2013 IEEEINFOCOM.

[11] D. Rossi and G. Rossini, “On sizing CCN content stores by exploitingtopological information,” in 2012 Infocom Computer CommunicationsWorkshops.

[12] W. K. Chai, D. He, I. Psaras, and G. Pavlou, “Cache “less for more” ininformation-centric networks,” in 2012 IFIP Networking Conference.

[13] N. Spring, R. Mahajan, and D. Wetherall, “Measuring ISP topologieswith Rocketfuel,” in Proc. 2002 ACM SIGCOMM.

[14] M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon, “I tube,you tube, everybody tubes: analyzing the world’s largest user generatedcontent video system,” in Proc. 2007 ACM IMC.