PREDICTION OF POSSIBLE CONGESTIONS IN SLA CREATION PROCESS

Transcription

1 PREDICTIO OF POSSIBLE COGESTIOS I SLA CREATIO PROCESS Srećko Krile University of Dubrovnik Departent of Electrical Engineering and Coputing Cira Carica 4, Dubrovnik, Croatia Tel , Fax , e-ail sreckokrile@uniduhr Ivana Pezelj CARet - Croatian Acadeic and Research etwork J Marohnica bb, 0000 Zagreb, Croatia Tel , Fax , e-ail ivanapezelj@carnethr Abstract - In our paper we propose efficient tool to predict possible traffic congestions caused by contracting of a new SLA (Service Level Agreeent) It could be an iportant part of adission control process in SLA creation (DiffServ networks) aturally, it is off-line algorith and we can use it only through negotiation process (SLA creation), just to ensure better QoS end-to-end routing in the oent of service invocation The proble is seen as an expansion proble of link capacities in given liits for given traffic deands (SLAs) Such explicit traffic engineering technique provides the possibility to intelligently tailor the route such that different parts of the network reain equally loaded It helps to avoid the creation of bottleneck links on the path and aintains high network resource utilization efficiency Also, it is capable to consider the ipact of end-toend delay on network resource provisioning, in the oent of SLA creation Index Ters - adission control in DiffServ networks, SLA creation, end-to-end QoS routing, traffic routing of aggregate flows I ITRODUCTIO In DiffServ networks the classification of the aggregated flows is perfored according to the SLA (Service Level Agreeent) signed between a custoer and the network operator (ISP) Each SLA contract specifies how uch traffic ay be sent (service class, bandwidth, delay liit etc) and defines a tie period for utilization of that service We know that such period can be very long soeties (eg for VoIP) To obtain quantitative end-toend guarantees in DiffServ architecture, based on traffic handling echaniss with aggregate flows, soe kind of congestion control in the phase of the negotiation process (SLA creation) is necessary Such approach we need in MPLS-based networks, too Multi-protocol label switching (MPLS) has gained popularity as a technology for anaging network resources and providing of perforance guarantees owadays, IP operators don't use any appropriate tool in that phase of adission control process They only use effective end-to-end routing technique in the oent of service invocation Such approach cannot help to prevent bottlenecks or ensure QoS In this paper we are looking for optial path provisioning in context of all existing traffic deands participating in the sae tie period (forer contracted SLAs); see fig and fig 2 The ain condition is: the sufficient network resources ust be available at any oent of that period In the worse case it ust be sufficient for the first service class (guaranteed QoS) So, in the process of SLA creation the proble of new SLA acceptance exists It eans that soe kind of congestion control ust be done, taking care of all existing flows in the sae tie If it is possible to find routing sequence without lack of capacity (shortage), the possibility of traffic congestion will be significantly reduced in the oent of service invocation The heuristic algorith we proposed in this paper is off-line algorith and we can use it only in the negotiation process of SLA creation, just to ensure better QoS end-to-end routing in the oent of service invocation The probles of new SLA creation and correlation with optial resource anageent are investigating in section II Explanation of the atheatical odel and heuristic approach for traffic routing technique is given in section III uerical exaples, testing results and algorith application are discussed in section IV II EW SLA CREATIO PROBLEM The network operator (eg ISP) wants to accept new SLA (traffic flow) that generates the traffic flow between s In DiffServ/MPLS architecture we call the

2 SLA_3 egress SLA_5 ingress ingress SLA_ ingress SLA_2 ingress 2 4 egress SLA_2 egress SLA_3 ingress SLA_4 egress s 6 d 3 core network 5 SLA_ egress SLA_4 ingress SLA_5 egress Figure An exaple of nuber of SLAs in context of new SLA creation In service specifications (service activation and duration) they share the sae period of tie; see fig 2 The optial routing sequence for new SLA need not to be the shortest path solution LER (Label Edge Router) Traffic deand on exit of LER represents the su of all ingress and egress flows Interior s in core network, capable to forward traffic in equivalent classes (FEC), are called LSR (Label Switching Routers) The network that is shown in figure can be a representation of such architecture Such aggregated flow is coing to LSR and has to be routed to destination Packets of the sae FEC are assigned the sae label and generally traverse trough the sae path across the MPLS network An FEC ay consist of packets that have coon ingress and egress nodes, or the sae service class and sae ingress/egress nodes or any other cobination In this case, the FEC of appropriate service class aggregates traffic deands (new SLA and forer contracted SLAs) of the sae QoS level A path traversed by an FEC is called a label switching path (LSP) In that sense network operator has to find optial path for the FEC but without any congestion in the network Possibility of congestion on soe link exists specially for definite period of tie; see fig 2 It should not be anaged anually so we need very effective tool to check such congestion possibility in the network We need congestion control algorith related on liited link resources and predicted traffic (caused with forer accepted SLAs) For each counication link in the network given traffic deands can be satisfied on different QoS levels (eg with different bandwidth) Fro exaples in fig and fig 2 we have three different QoS levels (service classes) Traffic can be satisfied on appropriate QoS level or higher, but not on lower QoS level Figure 2 An exaple of nuber of SLAs that participate in the sae period of tie Optial routing have to be done for that critical period (duration of new SLA)

3 In fact, we use different LSP for different service class But we don t want that physical network be divided into ultiple virtual networks, one per service class We strongly need the optial utilization of liited capacity The end effect is that we want to give to preiu traffic (fully satyisfied) ore resources, but exactly that is necessary, no less no ore The optial resource anageent proble can be seen as the link capacity expansion proble (CEP) fro the coon source with expansion values in allowed liits (link capacity); see [2] and [4] If the optial routing sequence has any link expansion with value that exceeds allowed liits, it eans that link capacity on the path is not sufficient for such traffic It eans that new SLA cannot be accepted or ust be redefined through negotiation process For exaple, the custoer can decide to take the adaptive QoS service class instead of fully satisfied (guaranteed) service class Soe iportant papers about that proble are [], [5] and [6] In the paper [7] we can see influence of delay to optial traffic engineering routing In the paper [3] such algorith is the part of service anageent architecture III THE MATHEMATICAL MODEL AD HEURISTIC APPROACH OF CEP Let G (A, E) denote a network topology, where A is the set of nodes and E the set of links The source and destination nodes (eg s in IP-doain) are denoted by s and d respectively Between the there are M interior s on the path; see fig 3 The nuber of QoS easures (eg bandwidth, delay) is denoted by z QoS easures can be roughly classified into additive (eg delay) and non-additive (eg available bandwidth) In case of an additive easure, the QoS value of the path is equal to the su of the corresponding weights of the link along that path For a non-additive easure, the QoS value of the path is the iniu (or axiu) link weight along the path In this atheatical odel it is assued that the network state inforation (eg traffic deands, link capacities, delay liits) are teporary static Consider a network G (A, E) where each link is characterized by z-diensional link weight vector, consisting of z nonnegative QoS weights {w n (s,d), i =,, z, (s,d) E, n =,, } as coponents We differentiate n service classes (QoS levels) For M interior s on the path we have: M links + 2 = M + links Given constraints for delay on the path for appropriate service class can be denoted by L,n Definition of the ulti-constrained (MCP) proble is to find a path P fro s to d such that: w i ( P ) = in wi, n ( s, d ) Li, n n= ( s, d ) P for i =,, z ; n =,, In this paper we dealt about one-diensional link weight vectors for M+ links, with delay constraints denoted with L,n The link weight (cost) is the function of used capacity and delay: lower used capacity (saller bandwidth and delay in acceptable liits) gives lower weight (cost) The ain condition is that given traffic deands ust be fully satisfied without shortages onlinear cost function is necessary if link weights are not positively correlated () O R, X, X,2 X,M I, =0, I,2,2 I,3 I,M,M I,M+ = 0 r, r,2 r,m QoS level I 2, =0 I, =0 y 2, 2, y 2, I 2,2 y 2,2 2,2 y 2,2 I,2,,2 I 2,3 r 2, y, r 2,2 y,2 r 2,M I,3 I 2,M I,M y 2,M 2,M y 2,M I,M+ = 0,M y,m I 2,M+ = 0 r, interior r,2 r,m link link 2 link M+ interior M Figure 3 A network flow representation of the CEP odel applied for congestion contol in SLA cration proces Service classses are differentiated but possible traffic conversion is possible only in direction toward higher QoS level

4 A Capacity Expansion Proble (CEP) The proble of the optial QoS routing for given traffic with different service classes can be seen as the iniu cost network flow proble in the ulticoodity single (coon) source ultiple destination network Such proble can be solved as the capacity expansion proble (CEP) without shortages Partially expansions for each link are ade fro coon source in given liits (existing link capacity) Transission link capacities on the path between s are capable to serve traffic deands for different QoS levels (service class) for i =,2,, Fig 3 gives an exaple of network flow representation for ultiple QoS levels () and M internal (core) s included in the path Link capacity that is capable to serve traffic deands of service class i we call facility It is used priarily to serve deands for QoS level but it can be used to satisfy traffic deands for QoS level j (j > i) Rerouting of traffic deands towards higher QoS level is the sae thing as facility conversion to lower QoS level In this odel conversion of traffic deand is peritted only in the direction toward higher QoS level It eans that alost satisfied service class (adaptive or best effort) can be treated as fully satisfied (guaranteed) service class, but not vice versa Explanation of diagra in fig 3: the -th row of nodes represents a possible link capacity state of each transission link between s for i-th QoS level Link capacity values are positive only and shortages are not allowed Horizontal links between the represent the traffic flow between s Coon node O is the source for used capacity (expansions), introducing the new traffic on the link Vertical links represent facility conversion that is equal to rerouting of traffic deands in opposite direction If the link expansion cost corresponds to weight of used capacity, the objective is to find optial routing policy that iniizes the total cost incurred over the whole path between s (M interior s and M + transission links) and to satisfy given traffic deands The flow theory enables separation of these extree flows, which can be a part of an optial expansion solution, fro those which cannot be With such heuristic approach we can obtain the optial result with significant coputational savings; see [4] In the atheatical odel of CEP the following notation is used: j and k = QoS level The levels are ranked fro i =,2,,, and quality level decreases with higher i = the order nuber of link on the path, connecting two successive LSR s Path consists of M + links ( =,, M+) connecting M interior s and s u,v = the order nuber of capacity points in the subproble, u,, v M+ r = traffic deand increent for additional capacity of facility i (appropriate QoS level) on link For convenience, the r are assued to be integer I = the relative aount of idle capacity of facility i on the link, related on the link before Initially there is no capacity shortage between and the interior outer, I i = 0, I M+ = 0 x = the aount of used capacity for facility i on the transission link y = the aount of capacity of facility i on the link, redirected to satisfy the traffic of lower level j CEP proble can be forulated as follows: in M + = i= c + ( ) + ( ) x h I + g y (2) D i x,u+ x,v x,u Guaranted (fully satisfied) I,u,u I,u+ I,u+2,u+,v I,v+ r,u r,u+ r,v+ QoS service classes y 2,u y 2,u+ y 2,v adaptive (alost satisfied) I 2,u 2,u I 2,u+ 2,u+ I 2,u+2 2,v I 2,v+ r 2,u y 3,u r 2,u+ y 3,u+ r 2,v+ y 3,v y 23,u y 23,u+ y 23,v best-effort I 3,u I 3,u+ 3,u 3,u+ I 3,u+2 3,v I 3,v+ r 3,u r 3,u+ r 3,v+ links Figure 4 A network flow representation of a sub-proble for =3

5 so that we have: I = I + x y ri, j= i+ + (3) I I 0 (4) = M + = for =, 2,, M; i =, 2,, ; j = i +,, In the objective function the total cost (weight) on the path fro to includes soe costs: the cost for capacity expansion c (x ), the idle capacity cost h (I + ) as penalty cost to force the usage of the iniu link capacity (prevention of unused/idle capacity) For the link expansion in allowed liits we can set the expansion cost to zero or we can introduce different cost to each service class Also we can introduce facility conversion cost g (y ) that can control non-effective usage of link capacity Costs are often represented by the fix-charge cost or with constant value We assue that all cost functions are concave and nondecreasing, reflecting econoies of scale, and they can change for appropriate link With different cost paraeters we can influence on the optiization process, looking for the ost appropriate routing solution Generalizing the concept of the capacity state for transission link in which the capacity state of each link is known within defined liits we define a capacity point - α In (5) α denotes the vector of capacities I for all QoS levels (facility types) on link, and we call it capacity point α = (I,, I 2,,, I, ) (5) α 0 = α M+ = (0, 0,, 0) (6) Let C be the nuber of capacity point values at position (link between core s), C = C M+ =, and the total nuber of capacity points is: M = + p C = C () In CEP we have to find any cost values d u,v (α u, α v+ ) that eanate two capacity points, fro each node (u, α u ) to node (v+, α v+ ) for v u The total nuber of all connections between capacity points is: = M d i M + Ci C j = j= i+ (2) The approach described in [4] requires solving repeatedly a certain single location expansion proble (SLEP) Many different expansions and rerouting solutions can be derived, depending on D i polarity Lot of the are not acceptable and are not part of the optial sequence, that is the key for the heuristic approach Most of the coputational effort is spent on coputing the subproble values Any of the, if it cannot be a part of the optial sequence, is set to infinity C Algorith coplexity Suppose that all links are known, the optial solution for CEP can be found by searching for the optial sequence of capacity points and their associated link state values of interior s The proble on that level can be forulated as a shortest path proble for an acyclic network in which the nodes represents all possible values of capacity points (states) Than Dijkstra s algorith or Each colun on the flow diagras (fro fig 3 and 4) represents a capacity point, consisting of capacity state values Forulation (6) iplies that idle capacities or capacity shortages are not allowed on the link between and interior B Sub-proble (CES) The expansion proble for facilities i =, 2,, on the path between s u, u +,, v is as follows (see fig 4): v in c ( x ) + hi, ( I + ) + g ( y ) (7) = u i= j= + Where I v+ = I u + Di ( u, v) Ri ( u, v) (8) v = u D i (u,v)= v R i u, v) = r i, = u ( (9) j = x i, y ; i j (0) for =, 2,, M+; i =, 2,, ; j = i +,, Associated value between two capacity points, that represents iniu cost d u,v (α u, α v+ ) we denoted as CES (Capacity Expansion Sub-proble) Figure 5 In this nuerical test-exaple link capacities are sufficient to satisfy traffic deands (SLAs)

6 can see that traffic deands can be fully satisfied and capacity surplus for all links are positive or zero There are no negative values; see lower diagra in fig 5 In exaple fro figure 6: we can notice the lack of capacity on the second link for second QoS level It eans that traffic deands cannot be fully satisfied although the surplus is obvious on third (lower) QoS level There is no free capacity on the first QoS level (for guaranteed service class) to be used instead In that case new SLA cannot be accepted or ust be redefined through negotiation process With such anageent tool we can predict possible congestion on the path very efficiently Figure 6 In this nuerical test-exaple the lack of capacity is obvious and new SLA cannot be accepted any siilar algorith can be applied It has to be noted that the optial routing sequence for traffic flow (new SLA) between s need not to be the shortest path solution On this level of algorith calculation it is very easy to introduce delay liits on the path The nuber of all possible d u,v values depends on the total nuber of capacity points It is very iportant to reduce that nuber (C p ) and that can be done through iposing of appropriate capacity bounds or by introduction of adding constraints The required effort for one sub-proble is O( 2 M) The nuber of all possible d u,v values depends on the total nuber of capacity points If there are no liitations on capacity state (WI ) and expansion aount (Wx ) the coplexity of such heuristic approach is pretty large and increases exponentially with Proble requires the coputation effort of O(M 3 4 R 2(-) i ) In real application we norally apply definite granularity of capacity values IV TESTIG RESULTS We tested our algorith on any nuerical testexaples, looking for optial routing sequence on the path Between s there are six LSR (core s) and they are connected with seven links Traffic deands (SLAs) are overlapping in tie The heuristic algorith in all test-exaples can achieve near-optial expansion sequence with inial total cost, with equal or very close value to that one we can get with algorith based on exact approach If the expansions of link capacities are in allowed liits and the capacity surplus is obvious, the traffic deands can be satisfied In exaple fro figure 5: we V COCLUSIOS In the process of new SLA creation in DiffServ networks possible congestion on the path can be checked with proposed heuristic algorith Algorith is based on atheatical odel for the capacity expansion proble (CEP) The first aspect of algorith is that load balancing leads to higher resource usage efficiency It can prevent critical network resources, not to be exhausted early and becoing a bottleneck for the network in the oent of service invocation The second unique aspect of this algorith is that it incorporates the ipact of end-to-end delay partitioning in network-level route selection decisions It eans that such heuristic approach can be successfully applied for adission control in the SLA creation process, that is in fir correlation with resource reservation echaniss and adission control process REFERECES [] Giordano, S, Salsano, S, Ventre, G, Advanced QoS Provisioning in IP etworks : The European Preiu IP Projects, IEEE Counications Mag, (2003), Vol4 o, pp30-36 [2] Krile, S, ew SLA Creation and Optial Resource Manageent, Proc of 3rd Conf CSDSP (Counication Systes, etworks and Digital Signal Processing), ew Castle (UK), 2004, pp [3] Kagklis, D, Tsakiris, C, Liapotis,, Quality of Service: A Mechanis for explicit activation of IP Services Based on RSVP, Journal of Electrical Engineering, Vol 54, o 90, Bratislava, 2003, pp [4] Krile, S, Kos, M, A Heuristic Approach for Path Provisioning in Diff-Serv etworks, Proc 7 th ISSSTA (Int Syp On Spread-Spectru Tech & Application) - IEEE, Prag, 2002, pp [5] Biswas, K, Ganguly, S and Izailov, R, Path provisioning for service level agreeents in differentiated services networks, ICC IEEE International Conference on Counications, vol 25, no, (2002), pp [6] Bouillet, E, Mitra, D, Raakrishnan, G K, The structure and anageent of service level agreeents in networks, IEEE Journal on Selected Areas in Counications, Vol 20, no 4, (2002), pp [7] Gopalan, K, Chiueh, Tzi-cker, Lin, Yow-Jian, Load Balancing Routing with Bandwidth-Delay Guarantees, IEEE Counicattion Mag, (2004), Vol42 o 6, pp08-3