Improved Routing in the Data Centre Networks HCN and BCN


 Colin Carroll
 2 years ago
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1 Improved Routing in the Data Centre Networks HCN and BCN Iain A. Stewart School of Engineering and Computing Sciences, Durham University, South Road, Durham DH 3LE, U.K. Abstract We present improved onetoone routing algorithms in the data centre networks HCN and BCN, in that our routing algorithms result in much shorter paths when compared with existing algorithms. We also present a much tighter analysis of HCN and BCN by observing that there is a very close relationship between the data centre networks HCN and the interconnection networks known as WKrecursive networks. We use existing results for WKrecursive networks to prove the optimality of our new routing algorithm for HCN and also to significantly aid the implementation of our routing algorithms in both HCN and BCN. Keywordsdata centre networks; HCN; BCN; onetoone routing; WKrecursive networks. I. INTRODUCTION The traditional architecture of a data centre network (DCN) is switchcentric whereby the primary structure is a topology (almost always treebased) of switches with the switches possessing interconnection intelligence. The DCNs FatTree [], VL [5] and Portland [] are typical of such DCNs. A more recent and alternative architecture is servercentric whereby the interconnection intelligence resides within the servers and the switches are dumb crossbars (so, there are no switchswitch links). The DCNs DCell [7], FiConn [9], BCube [6], MDCube [] and HCN and BCN [8] are typical of servercentric DCNs. The servercentric architecture possesses a number of advantages when compared with the switchcentric architecture such as: the underlying topologies are better suited than the switchcentric treebased topologies to support traffic patterns prevalent in data centres (such as onetoall and alltoall); the switches can be chosen to be commodity switches as they require no intelligence; and multiple network interface controller (NIC) ports on servers can be utilized so that more varied topologies can be constructed (see, for example, [3], [8], [0]). Whilst multiple NIC ports can be used when building DCNs, commodity servers usually only have a small number of NIC ports, often only two. Motivated by the desire to use commodity servers only, Guo, Chen, Li, Li, Liu and Chen introduced and evaluated the DCNs HCN and BCN [8]. The general construction is that the DCNs HCN are recursivelydefined networks, with the DCNs BCN built using DCNs from HCN by including an additional layer of interconnecting links. A number of routing algorithms (including onetoone, multipath and faulttolerant algorithms) were developed and evaluated, primarily in comparison with FiConn and according to a number of basic metrics. We pursue the analysis of the DCNs HCN and BCN in this paper. In particular, we present significantly improved onetoone routing algorithms in both HCN and BCN, in that our routing algorithms result in much shorter paths than those in [8]. We also present a much tighter analysis of HCN and BCN by observing that there is close relationship between the DCNs HCN and the interconnection networks known as WKrecursive networks which originated in [4] and which have been well studied as general interconnection networks. We use existing results concerning WKrecursive networks to prove the optimality of our new routing algorithm for HCN and to significantly aid the implementation of our routing algorithms in both HCN and BCN. II. THE DCNS HCN In this section we define the DCN HCN(n,h) from [8], where n and h 0: the parameter n is the degree of the base nstar in the recursive construction (the base n star takes the form of a switchnode with n adjacent servernodes); and the parameter h is the depth of the recursion (we reiterate that in the servercentric DCN architecture, all DCNs consist of a mix of switchnodes and servernodes so that every switchnode is adjacent only to servernodes). For clarity, we give full definitions of the complex DCNs HCN and BCN. We then place these definitions within the context of WKrecursive networks, first defined in [4]. A. The recursive construction We begin with a base DCN G 0 consisting of an nstar with the hubnode 0 being the solitary switchnode and the other nodes,,...,n being servernodes. We fix α and β 0 so that α + β = n: the nodes,,...,α are called the masternodes; and the nodes α+,α+,...,n the slavenodes. We suppress reference to slavenodes below. We next take α disjoint copies of G 0, namely G 0,G0,...,G 0 α, and refer to (master) node j of G0 i as node (i,j), for i,j {,,...,α} (in what follows, all indices come from {,,...,α} and switchnodes and slavenodes play no role in the construction). For i,j {,,...,α}, where i j, we join nodes (i,j) G 0 i and (j,i) G 0 j via an additional link. Note that no additional link involves any node of {(i,i) : i =,,...,α}. Denote the resulting
2 network byg, with masternodes and switchnodes (as well as slavenodes) inherited from G 0,G0,...,G0 α but so that any node (i,j) G 0 i, where i j, becomes a usednode in G. We call the α(α ) usednodes the usednodes at level and the additional links we introduced the level links. New links introduced in the subsequent construction are not incident with usednodes. We can iterate the process above as follows. Take α disjoint copies of G, namely G,G,...,G α, and refer to node (j,k) of G i as node (i,j,k). Note that each copy G i has α masternodes. For i,j {,,...,α}, where i j, we join nodes (i,j,j) and (j,i,i) via an additional link. Denote the resulting network by G. Note that any node (i,i,i) has degree and any node (i,j,k) where it is not the case that i = j = k has degree. In G, the switchnodes and usednodes (at level ) are inherited from G,G,...,G α (as are the slavenodes) but any masternode (i,j,j) G i where i j becomes a usednode in G. We call the α(α ) newlydesignated usednodes the usednodes at level and the additional links we introduced the level links. As before, new links introduced in the subsequent construction are not incident with usednodes. We proceed similarly to construct G 3,G 4,...,G h and obtain usednodes and links at levels 3,4,...,h. We refer to the identification of a (used or master) node of some G i as a tuple of i + digits as the index of the node; indeed, henceforth we equate a node with its index. The DCN HCN(n,h) is defined to be G h. Note that given some index (i h,i h,...,i,i 0 ) of a masternode or a usednode, the index of the switchnode to which it is adjacent can be obtained as (i h,i h,...,i,0). The DCN HCN(7,) can be visualized as in Fig., where α = 4 and β = 3. The slavenodes are in white, the masterand usednodes are in black and the index of any masternode or usednode is obtained by replacing the rightmost component of the index of the adjacent switchnode by the node s number from {,,3,4}. In general, the index of a used or masternode in HCN(n, h) is obtained by replacing the rightmost component of the index of the adjacent switchnode by the node s number from {,,...,α}. B. Recursive structure and node enumerations Within HCN(n,h), there is a natural indexing of the slavenodes as {(i h,i h,...,i,y) : i h,i h,...,i {,,...,α},y {α +,α +,...,n}}. So, all nodes of HCN(n,h) of index (i h,i h,...,i,z), for some fixed i h,i h,...,i {,,...,α} (with z {0,,,...,n}) induce a copy of G 0. Similarly, if 0 γ < h and we fix i h,i h,...,i γ+ {,,...,α}, then all nodes of HCN(n,h) of index (i h,i h,...,i γ+,j γ,j γ,...,j,z), wherej γ,j γ,..., j {,,...,α} and where z {0,,...,n}, induce a copy of HCN(n,γ). Note that there are α h γ such copies of HCN(n, γ) within HCN(n, h), with the copy above Figure. The network HCN(7, ). identified by the tuple (i h,i h,...,i γ+ ). These are the canonical copies of HCN(n, γ) in HCN(n, h). So far, we have identified nodes with indices. However, we also refer to nodes by their names. Suppose that (i h,i h,...,i,i 0 ) {,,...,α} h+ is the index of some master or usednode of HCN(n,h). We say that this node has nameid((i h,i h,...,i,i 0 )) = Σ h l=0 (i l )α l + (we suppress the parameters α and h in the denotation of the function id). The function id is clearly a bijection from the set of master and usednodes of HCN(n,h) to the set {,,...,α h+ }. The function id can also be used to name the copies of HCN(n,γ) within HCN(n,h), where γ < h, as {,,...,α h γ }, and also the switchnodes of HCN(n,h) as {,,...,α h } by stripping away the rightmost component of the index of any switchnode. Consider a slavenode (i h,i h,...,i,y) in HCN(n,h), wherei h,i h,...,i {,,...,α} andy {α+,α+,...,n}. We define the function id as id ((i h,i h,..., i,y)) = (id((i h,i h,...,i )) )β + (y α) (again, α, β and h are suppressed). This function id is a bijection from the set of slavenodes of HCN(n,h) to the set {,,...,α h β}. C. WKrecursive networks As is stated in [8], if two (master or used) nodes of HCN(n, h) are adjacent to the same switchnode then it is assumed that the length of a shortest path joining these two nodes is (the same assumption is also adopted as regards the analysis of DCell [7], FiConn [9], BCube [6] and MDCube []). This is equivalent to removing every switchnode from HCN(n, h) and assuming that the masternodes and usednodes adjacent to some switchnode are joined as a clique (of α nodes). What remains is a WKrecursive network that was first defined in [4] (we are ignoring slavenodes recall). WKrecursive networks have been extensively
3 studied and, as we shall see later, we can use the analysis of these networks in order to better understand the topological properties of the DCNs HCN and BCN. More formally, the WKrecursive network WK(α, h) is defined so that: it has nodeset {,,...,α} h+ ; there are links ((i h,i h,...,i,i,x),(i h,i h,...,i,i,x )), for i,i,...,i h,x,x {,,...,α}, where x x ; and links ((i h, i h,..., i j+, i j, i j,... j times..., i j ), (i h,i h,..., i j+,i j,i j,... j times...,i j )), for j {,,...,h} and for i h,i h,...,i j+,i j,i j {,,...,α}, where i j i j. III. THE DCNS BCN We now explain how the DCNs BCN from Sections 3. and 3.3 of [8] can be constructed from the DCNs HCN of the previous section. Whereas it was the masternodes (of the nstars) that were used to build the DCNs HCN, now we construct the DCNs BCN using the slavenodes. Let h 0, γ 0, α and n = α+β be given. Case (a): BCN(α,β,h,γ), where h < γ. The network BCN(α,β,h,γ) is defined to be HCN(n,h); so, there are β slavenodes hanging off each switchnode (as in Fig. ; the parameter γ plays no role when h < γ). Case (b): BCN(α,β,h,h). Set s = α h β (that is, the total number of slavenodes in HCN(n,h)). In order to construct BCN(α,β,h,h), we take s+ disjoint copies of HCN(n,h), denoted,b,...,b s, and build the network K s+ (,B,...,B s ), where K s+ is the clique on s+ nodes, as follows: we add additional links to ensure that every slavenode of any B i is joined to exactly slavenode of some other B i so that if two slavenodes of B i are joined to slavenodes in B i and B i then i i. There are various ways to implement the above construction and two were highlighted in [8]. Consider some slavenode (i h,i h,...,i,y) in B u and identify it as (u,v), where u {0,,...,s} and v = id ((i h,i h,...,i,y)) {,,...,s} (recall that id was defined in Section IIB). The first method from [8] proceeds as follows: we join the slavenode (u,v) of B u to the slavenode (v,u) of B v, if u v, and to the slavenode (v,u + ) of B v if u < v. Call this construction slaveconstruction. The second method of adding additional links that was highlighted in [8] proceeds as follows. Define the maps f,g : {0,,...,s} {,,...,s} {0,,...,s} by:f(u,v) = u+v mod (s+) andg(u,v) = s+ v. It is not difficult to show that the map (u, v) (f(u, v), g(u, v)) yields a required set of additional links through joining the slavenode (u,v) of B u to the slavenode (f(u,v),g(u,v)) of B f(u,v). Call this construction slaveconstruction. Case (c): BCN(α,β,h,γ), where h > γ. We set s = α γ β (that is, the number of slavenodes in BCN(α,β,h,γ)) and again we take s + disjoint copies of HCN(n,h), denoted,b,...,b s. Note that each B u contains within it t = α h γ (disjoint) canonical copies of HCN(n,γ), each identified by some unique index (i h,i h,...,i γ+ ) {,,...,α} h γ (see Section IIB). Thus, any HCN(n,γ) can be identified by a pair (u,v), where u {0,,...,s} and where v = id((i h,i h,...,i γ+ )) {,,...,t} (recall that id was defined in Section IIB). Furthermore, any slavenode of this copy of HCN(n, γ), indexed as (i h,i h,...,i,y), can be identified by a triple (u, v, w) with u and v as above and where w = id ((i γ,i γ,...,i,y)) {,,...,s} HCN( α, β, γ) HCN( α, β, h) HCN( α, β, h) HCN( α, β, h) HCN( α, β, h) t B B B3 Bt B B K s+(, B,..., B s ) B s B K s+(, B,..., B s ) B s B3 K s+( 3, B 3,..., B s 3 ) B3 s B t B K s+( t, B t,..., B s t ) Figure. The network BCN(α,β,h,γ) when h > γ. Fix (i h,i h,...,i γ+ )) {,,...,α} h γ so as to define v = id((i h,i h,...,i γ+ )) {,,...,t}. In every B u, there is one copy of HCN(n,γ) identified by (u,v). Denote this copy of HCN(n,γ) by Bu v. Build the network K s+ (B0 v,bv,...,bv s ) as we did above in Case (b). Moreover, do this for every v {,,...,t}. What results is the DCN BCN(α,β,h,γ). The structure of BCN(α, β, h, γ) can be visualized in Fig., where: the lighter grey denotes the construction of some HCN(n,h); and the darker grey denotes a compound construction using a clique interconnection of some K s+ (B0,B v,...,b v s). v The superscriptv in Bu v denotes the row (from {,,...,t}) that the copy of HCN(n,γ) lies in and the subscript u the column (from {0,,...,s}). In order to traverse a column the links of some HCN(n,h) are used, and in order to traverse a row the links of some K s+ (B0,B v,...,b v s) v are used. IV. ONETOONE ROUTING IN THE DCNS HCN In this section we describe the onetoone routing algorithm for HCN(n, h) called FdimRouting that was derived in [8] before describing an improved onetoone, minimal routing algorithm called NewFdimRouting. In essence, the algorithm FdimRouting is that obtained in [, Section 3.] and the algorithm NewFdimRouting is actually that obtained in [, Section 3.]. Henceforth, we regard every servernode as a masternode or a slavenode according to its origin; so, we no longer have usednodes. B s t B s
4 A. Routing with FdimRouting The onetoone routing algorithm for HCN(n, h) from [8], named FdimRouting, proceeds as follows. Given a source node (i h,i h,...,i 0 ) {,,...,α} h+ and a destination node(i h,i h,...,i 0 ) {,,...,α}h+, letj be such that i h = i h,i h = i h,...,i j+ = i j+,i j i j : if such a j does not exist then the source and the destination coincide; and if j = 0 then the source and the destination are adjacent to the same switchnode. For the moment, we assume that source and destination nodes are masternodes. If j > 0 then a path is obtained from the source to the node (i h,i h,...,i j+,i j,i j,... j times...,i j ) recursively and by working entirely within the canonical copy of HCN(n, j ) within HCN(n, h) indexed by (i h,i h,...,i j+,i j ). This path is then extended by a link at level j to the node (i h,i h,...,i j+,i j,i j,... j times...,i j ). Thus, we need a path from (i j,... j times...,i j ) to (i j,i j,...,i 0 ) in the canonical copy of HCN(n,j ) indexed by (i h,i h,...,i j+,i j ) which is obtained by proceeding recursively. What we have just described is the routing algorithm for WK(n, h) from [, Section 3.]. It was shown in Theorem 4 in [8] that FdimRouting yields a path joining any two masternodes of HCN(n, h) of length at most h+. So, the length of a shortest path between any two masternodes of HCN(n,h) is at most h+. Lemma. of [] yields that there exist two nodes for which a shortest path between them has length exactly h+. It is trivial to implement the algorithm FdimRouting as a sourcerouting algorithm so that it has O(h h ) time complexity (and not O( h ) as was stated in [], [8]; for even writing the route takes O(h h ) time where we assume that n = O()). Also, it is not difficult to see that FdimRouting can be implemented as a distributedrouting algorithm so that the time taken for each iterim node to compute the next node on the route is O(h). B. Routing with NewFdimRouting As was noted in [, Section 3.], the routing algorithm FdimRouting is not a minimal routing algorithm and can be improved. Consider applying the routing algorithm FdimRouting to the source node (,,) and the destination node (3,,) of HCN(4,). The resulting path is: (,,) (,,3) (,3,) (,3,3) (3,,) (3,,) (3,,) (3,,). However, the path (,,) (,,) (,,3) (,3,) (,3,3) (3,,) is shorter. The following algorithm, which we call GetShortest, was proven in [, Section 3.] to result in a minimal routing algorithm for WK(n, h) (and so for HCN(n, h) with masternodes as the source and destination). We write u = (u h,u h,...,u,u 0 ) {,,...,α} h+ and v = (v h,v h,...,v,v 0 ) {,,...,α} h+. Algorithm: GetShortest input: source u, destination v with u v; compute the length l of the path obtained by executing FdimRouting with source u and destination v; let i be s.t. u i v i and u j = v j, for all j {h,h,...,i+}; for each z {,,...,α} s.t. u i z v i : compute the length lz of the path obtained by executing FdimRouting with source u and destination (u h,u h,...,u i+,u i,z,... i times...,z); compute the length lz of the path obtained by executing FdimRouting with source (v h,v h,...,v i+,v i,z,... i times...,z) and destination v; set l z = lz + lz + i + ; if l min{l z : u i z v i }: output 0; else: output z where l z = min{l z : u i z v i }; The value output by the algorithm GetShortest yields a shortestpath algorithm that we call NewFdimRouting. If the output is 0 then the shortest path from u to v is obtained by executing FdimRouting on u and v. If the output is z 0 then the shortest path from u to v is obtained by executing FdimRouting: with source u and destination (u h,u h,...,u i+,u i,z,... i times...,z); with source (u h,u h,...,u i+,z,u i,... i times...,u i ) and destination (u h,u h,...,u i+,z,v i,... i times...,v i ); and with source (u h,u h,...,u i+,v i,z,... i times...,z) and destinationv, before concatenating the resulting paths of nodes. One might think that one will have to actually (repeatedly) execute FdimRouting during an execution of GetShortest. However, by [, Lemma 3.3] the following is true. Theorem Let (z,z,... h times...,z) and (u h,u h,..., u,u 0 ) be two nodes of WK(n,h). The length of a shortest path joining these two nodes is i where i ranges over {i : i = 0,,...,h,u i z}. Consequently, we can calculate the length of a shortest path along with which route it takes without computing any actual path; a simple numeric calculation suffices. Once we have this information, we can build the actual path. Let us now return to when at least one of our source and destination nodes in HCN(n, h) is a slavenode (this was left blurred in [8]). W.l.o.g. suppose that our source is a slavenode. We calculate the length of a shortest path between every masternode adjacent to the same switchnode as the source and: the destination node, if the destination is a masternode; or to every masternode adjacent to the destination node, if the destination is a slavenode. We take the resulting path of minimal length as our shortest path.
5 Theorem assists significantly with this computation. As we noted above, FdimRouting can be implemented as both a sourcerouting and a distributedrouting algorithm. This is also true for NewFdimRouting. When implemented as a sourcerouting algorithm, the repeated numeric computations take O(h) time (recall, n is assumed to be O()); so, the complexity remains at O(h h ). When implemented as a distributedrouting algorithm, as well as carrying the source and the destination within the packet header, the value z, output from GetShortest, must also be carried. When it is, the time taken for each interim node to compute the next node on the route remains at O(h). V. ONETOONE ROUTING IN THE DCNS BCN In this section we describe the routing algorithms for the DCNs BCN derived in [8]. We show how these routing algorithms do not necessarily result in shortest paths before explaining how to improve the routing algorithms. A. Routing in BCN Consider BCN(α, β, h, γ) where h γ. With reference to Fig., where s = α γ β and t = α h γ, there are two cases to consider: when h = γ (and t = ); and when h > γ. As remarked earlier, the canonical copies of HCN(n, γ) in BCN(α,β,h,γ) (denoted Bu v in Fig. ) are identified by the pairs (u,v) {0,,...,s} {,,...,t}. Suppose that h = γ. The routing algorithm BdimRouting from [8] proceeds as follows. If the source and destination both reside in B u, for some u {0,,...,s}, then use FdimRouting within B u to find a route. If the source and destination reside in B u and B u, where u u, then: find the unique bridgelink (x,x ) joining a slavenode x in B u to a slavenodex inb u ; and build the route from the source to x using FdimRouting within B u, concatenated with the link (x,x ) and concatenated with the route from x to the destination built using FdimRouting in B u. Suppose that h > γ. Two routing algorithms were proposed in [8] where the second is simply a symmetric version and so we ignore it. The main routing algorithm from [8] is called BdimRouting and proceeds as follows. If the source and destination both reside in B u, for some u {0,,...,s}, then use FdimRouting within B u to find a route. If the source and destination reside in Bu v and Bu v, where u u, then: find the unique bridgelink (x,x ) joining a slavenode x in Bu v to a slavenode x in Bu v ; and build the route from the source to x using FdimRouting within Bu v, concatenated with the link (x,x ) and concatenated with the route from x to the destination built using FdimRouting within B u. Of course, we can immediately improve this algorithm by using the algorithm NewFdimRouting instead of the algorithm FdimRouting. However, irrespective of whether we use FdimRouting or NewFdimRouting, the algorithm outlined above does not necessarily yield shortest paths within BCN(α, β, h, γ). For example, consider BCN(4, 3,, ) (we adopt the nomenclature of Case (b) of Section III). We have that s = α h β = 48. Suppose that we adopt slaveconstruction and the source is the slavenode (0,48) {0,,...,48} {,,..., 48} with the destination the slavenode (,48) {0,,..., 48} {,,...,48}. According to the routing algorithm BdimRouting, we first compute the shortest path from(0, 48) to (0,) within. Denoting the masternodes of as {,,3,4} 3, the following is such a path: (0,48), (4,4,), (4,,4),(4,,),(,4,4),(,4,),(,,4),(0,). We then concatenate on the link ((0,),(,)), and a shortest path from (,) to (,48) in B. Denoting the masternodes of B as {,,3,4} 3, the following is such a shortest path: (,),(,,4),(,4,),(,4,4),(4,,),(4,,4),(4,4,), (, 48). This results in a path of length 5. However, the following is a path of length 3: (0,48),(48,),(48,),(,48). Suppose that we adopt slaveconstruction and the source is the slavenode (0,) {0,,...,48} {,,...,48} with the destination the slavenode (48,48) {0,,...,48} {,,...,48}. According to the algorithm BdimRouting, we first compute the shortest path from (0,) to (0,48) within. Denoting the masternodes of as {,,3,4} 3, the following is such a path: (0,), (,,4), (,4,), (,4, 4), (4,,), (4,,4), (4,4,), (0,48). We then concatenate on the link ((0, 48),(48, )), and a shortest path from (48,) to (48,48) in B 48. Denoting the masternodes of B 48 as {,,3,4} 3, the following is such a shortest path: (48,), (,,4), (,4,), (,4,4), (4,,), (4,,4), (4,4, ), (48, 48). This results in a path of length 5. However, the following is a path of length 0: (0,), (,48), (,47), (48,), (,,4), (,4,), (,4,4), (4,,), (4,, 4), (4, 4, ), (48, 48) (here, the masternodes are masternodes within B 48 ). B. Improved routing in BCN The routing algorithm BdimRouting for BCN(α, β, h, γ), where h = γ or h γ as appropriate, from [8], outlined above, is such that if the source is in B u and the destination is in B u, where u u, then the route derived remains entirely within B u and B u. The shorter paths in the examples given above do not have this property. Our improved routing algorithm in BCN(α,β,h,γ) is as follows. First, suppose that: h = γ; the source is in B u ; and the destination is in B u, where u u. Algorithm: NewBdimRouting for every u {0,,...,s}\{u,u }: find the unique bridgelinks (x,x ) and (y,y ) from B u to B u and from B u to B u, respectively; piece together shortest paths joining the source to x in B u, x to y in B u and y to the destination in B u to get the path ρ u from the source
6 to the destination; build the path ρ using BdimRouting; choose the path ρ from all these paths so that its length is minimal; Suppose that: h > γ; the source is in Bu v ; and the destination is in Bu v, where u u. Algorithm: NewBdimRouting for every u {0,,...,s}\{u,u }: find the unique bridgelinks (x,x ) and (y,y ) from Bu v to Bu v and from B v u to Bv u, respectively; piece together shortest paths joining the source to x in Bu, v x to y in B u and y to the destination in Bu v to get the path ρ u from the source to the destination; build the path ρ using BdimRouting; choose the path ρ from all these paths so that its length is minimal; Of course, Theorem makes the implementation of NewBdimRouting trivial. When implemented as a sourcerouting algorithm, and given our comments earlier as regards the implementation of NewFdimRouting, NewBdimRouting has time complexity O(h h ); for it is essentially 3 repetitions of NewFdimRouting. As regards the implementation of NewBdimRouting as a distributedrouting algorithm, again the time complexity is O(h). However, the packet header must also carry the 3 different z s corresponding to the 3 executions of NewFdimRouting as well as a parameter detailing which B u NewBdimRouting transits through. lengths of these paths in terms of the number of (servernode to servernode) hops. We also count the number of times savings have been made. Our results can be visualized in Figs. 36 for BCN (due to space limitations we do not detail the graphs for BCN(α, β, 3, γ) and HCN but just report these results). In Fig. 4, for example, h = 4 and γ = with n = 9. For each instantiation of α and β from {(7,),(6,3),(5,4),(4,5),(3,6),(,7)} and under slaveconstruction and slaveconstruction: the total saving in pathlength expressed as a percentage of the total pathlength given by BdimRouting is detailed via the columns; and the total number of iterations leading to a reduction in pathlength when we employ NewBdimRouting rather than BdimRouting expressed as a percentage of the number of iterations (namely 000) is detailed via the lines. We also give the number of servernodes in BCN(α,β,4,). Figure 3. Experimental results for BCN(α, β, 4, ). VI. AN EMPIRICAL EVALUATION In this section we undertake an empirical evaluation so as to ascertain both the breadth and extent of the savings to be made by employing our new routing algorithms. A. Our experiments In what follows we describe an experiment for a particular DCN BCN(α,β,h,γ) (experiments for a DCN HCN(n,h) are analogous and more straightforward). We choose the parameters α, β, h and γ as well as the construction method (that is, slaveconstruction or slaveconstruction ). We have chosen h = 3,4, γ h and (α,β) {(7,),(6,3),(5,4),(4,5),(3,6),(7)} to get practically reasonable DCN sizes. Next, we decide upon the number of iterations to be undertaken (we choose 000) and in each iteration we randomly generate a source servernode and a destination servernode (so that they are distinct; these servernodes can be either master or slavenodes) before employing the algorithm BdimRouting from [8] and the algorithm NewBdimRouting so as to find a path from the source to the destination. We derive the cumulative Figure 4. Experimental results for BCN(α, β, 4, ). B. Our evaluation Our evaluations of our experimental results for the DCNs HCN(n,h) and BCN(α,β,h,γ) are as follows. The percentage savings in terms of the number of hoplengths made by employing NewBdimRouting rather than BdimRouting in BCN(α,β,h,γ) is very similar in all figures in that as the value of α in (α, β) decreases, the percentage savings made increases (but only marginally);
7 some permanence or were to be used to transmit a significant amount of data, the transferral of which had some cost attached, then it might be worthwhile expending resource in computing a shorter path. Of course, we cannot be sure that a shorter path than that computed using BdimRouting would result but as we have seen this can be the case in over in paths in practice. Figure 5. Experimental results for BCN(α, β, 4, 3). REFERENCES [] M. AlFares, A. Loukissas, and A. Vahdat, A Scalable, Commodity Data Center Network Architecture, Proc. of ACM SIGCOMM, pp , 008. [] C.H. Chen and D.R. Duh, Topological Properties, Communication, and Computation on WKrecursive Networks, Networks, vol. 4, no. 6, pp , 994. [3] K. Chen, C. Hu, Z. Xin, K. Zheng, Y. Chen and A.V. Vasilakos, Survey on Routing in Data Centers: Insights and Future Directions, IEEE Networks, vol. 5, no. 4, pp. 6 0, 0. [4] G. Delia Vecchia and C. Sanges, Recursively Scalable Networks for Message Passing Architectures, Proc. of Int. Conf. on Parallel Processing and Applications, pp , 987. Figure 6. Experimental results for BCN(α, β, 4, 4). moreover, the closer γ is to h, the better the savings made. These savings can be substantial, e.g., over 0% in BCN(4,3,3,6). The percentage savings in terms of the number of iterations where path lengths are reduced by employing NewBdimRouting rather than BdimRouting in BCN (α,β,h,γ) is very similar in all figures in that as the value of α in (α,β) decreases, the extent to which savings are made increases but there is a decline from (α,β) = (3,6) to (α,β) = (,7). These savings can be substantial, e.g., over in every sourcedestination pairs in BCN(4,4,5,4) results in a reduced path length. There appears to be no advantage in using the method slaveconstruction over slaveconstruction in the DCNs BCN and vice versa. The percentage savings in terms of the number of hoplengths made by employing NewFdimRouting rather than FdimRouting in HCN(n, h) are relatively modest as are the percentage savings in terms of the number of iterations where path lengths are reduced. In summary, there are real gains to be made in employing NewBdimRouting in BCN(α, β, h, γ) rather than BdimRouting. C. Significance As ever, there are tradeoffs to be made in that when the value of s = α γ β is large, there are s alternative routes in BCN(α,β,h,γ) to try within NewBdimRouting and it can be computationally expensive to execute the algorithm. However, if the actual route to be computed were to have [5] A. Greenberg, J.R. Hamilton, N. Jain, S. Kandula, C. Kim, P. Lahiri, D.A. Maltz, P. Patel and S. Sengupta, VL: A Scalable and Flexible Data Center Network, ACM SIGCOMM Comput. Commun. Rev., vol. 39, no.4, pp. 5 6, 009. [6] C. Guo, G. Lu, D. Li, H. Wu, X. Zhang and Y. Shi, BCube: A High Performance, Servercentric Network Architecture for Modular Data Centers, ACM SIGCOMM Comput. Commun. Rev., vol. 39, no. 4, pp , 009. [7] C. Guo, H. Wu, K. Tan, L. Shi, Y. Zhang and S. Lu, DCell: A Scalable and Faulttolerant Network Structure for Data Centers, ACM SIGCOMM Comput. Commun. Rev., vol. 38, no. 4, pp , 008. [8] D. Guo, T. Chen, D. Li, M. Li, Y. Liu and G. Chen, Expandible and Costeffective Network Structures for Data Centers using Dualport Servers, IEEE Trans. Comput., vol. 6, no. 7, pp , 03. [9] D. Li, C. Guo, H. Wu, K. Tan, Y. Zhang, S. Lu and J. Wu, Scalable and Costeffective Interconnection of Datacenter Servers using Dual Server Ports, IEEE/ACM Trans. Network., vol. 9, no., pp. 0 4, 0. [0] Y. Liu, J.K. Muppala, M. Veeraraghavan, D. Lin and J. Katz, Data Centre Networks: Topologies, Architectures and Fault Tolerance Characteristics, Springer, 03. [] R.N. Mysore, A. Pamboris, N. Farrington, N. Huang, P. Miri, S. Radhakrishnan, V. Subramanya and A. Vahdat, PortLand: A Scalable FaultTolerant Layer Data Center Network Fabric, ACM SIGCOMM Comput. Commun. Rev., vol. 39, no. 4, pp , 009. [] H. Wu, G. Lu, D. Li, C. Guo and Y. Zhang, MDCube: A High Performance Network Structure for Modular Data Center Interconnection, Proc. of 5th Int. Conf. on Emerging Networking Experiments and Technologies, pp. 5 36, 009.
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