A novel solution of QoS routing with multi constraint algorithm

In providing new telecommunication (telecom) services, the requisition for quality of network is more and more popular and sophisticated with high bandwidth, small value of delay time or packet loss etc. To assure the quality of network, the scheme of Quality of Service (QoS) routing algorithm based on local state information have recently been researched as a promising alternative to the currently deployed global QoS routing schemes.

Electronics and Automation A NOVEL SOLUTION OF QoS ROUTING WITH MULTI-CONSTRAINT ALGORITHM Tran Minh Anh1,*, Nguyen Chien Trinh1, BuiThi Minh Tu2 Abstract: In providing new telecommunication (telecom) services, the requisition for quality of network is more and more popular and sophisticated with high bandwidth, small value of delay time or packet loss etc To assure the quality of network, the scheme of Quality of Service (QoS) routing algorithm based on local state information have recently been researched as a promising alternative to the currently deployed global QoS routing schemes Different from the traditional QoS routing algorithms that use global state information, the localized routing algorithms use local information collected from source node to make routing decisions These localized routing algorithms can be a solution to meet the demand of telecom market in the near future.In this paper, we introduce a new localized QoS routing algorithm that uses bandwidth and delay as constraints; and research the impacts of QoS routing algorithms on the network bandwidth balancing through a proposed parameter of evaluating network bandwidth disparity We also perform our algorithm with experiments, compare and realize the more considerable performance of this algorithm than other algorithms wherein we use the same type of network topology, QoS requirements and traffic patterns Keywords:Algorithm, Balancing, Bandwidth, Delay, Localized INTRODUCTION Nowadays, telecom network develops very strongly and the guarantee of quality for large-scale networks becomes much challenged Therefore, the approach of using local information to make routing decisions is an effective way of communicating in network Due to using local information, this routing technique will decrease the average overall blocking probability of packets transmitted through the network, and hence makes the overall performance of network better than global QoS routing schemes that update network state periodically by a link-state algorithm and maintain it up-to-date The way that global QoS routing schemes will lead to a large communication overhead, the inexact of global state and the out-of-date information due to large update intervals.Localized QoS routing helps to avoid these problems by routing based on local information By this approach, each node maintains a predetermined set of candidate paths to each possible destination and routes flow along these paths This will help network run more effectively In this paper, we propose a new localized QoS routing which uses bandwidth and delay as constraints for routing, and analyze the network bandwidth balancing with this algorithm through simulations witha proposed parameter of evaluating network bandwidth disparity We compare and realize better performance against some other localized routing algorithms when we perform simulations with the same types of topology, traffic patterns and under the same range of traffic loads The rest of the paper is structured as follows Section surveys some QoS routing schemes as related works Section 3studies a parameter ofevaluating network bandwidth disparity,researches the impacts of QoS routing on network bandwidth balancing; and describes our novel routing algorithm Section studies the performances of simulations that show the more favourable results of our routing algorithm In the end, section concludes the paper and proposes the next works 58 T.M.Anh,N.C.Trinh, B.T.M.Tu, “A novel solution of QoSrounting…” Research RELATED WORKS The parameters of QoS like bandwidth, delay … have been used to estimate the quality of transmission for telecom services in some years recently and there are a lot of researches for QoS routing which have been published on many different areas as [1-2] In the future, when the convergence of services becomes stronger, QoS routing will be more important As explained in the previous sections, localized QoS routing algorithm has been proposed recently as a viable alternative to global QoS routing In localized routing algorithm, nodes firstly build the set of candidate paths between all pair of nodes from itself It maintains this set and makes routing decisions by using only local state information collected at that node Therefore, no global-state information needs to be exchanged, then ithelps to reduce the overhead computation, and helps routing better To expose some advanced points above, we now survey some localized QoS routing schemes as follows The first scheme we mention is the localized Credit Based Routing algorithm (CBR) as in [3] The CBR uses a simple routing procedure to route traffic across the network by using crediting scheme for each candidate path that rewards a path upon flow acceptance and penalizes it upon flow rejection The CBR algorithm keeps updating each path's credit upon flow acceptance and It is also keeps monitoring the flow blocking probabilities for each path and conveys the data to the credit scheme to select path The CBR predetermines a set of candidate paths R between each pair of source and destination where R = R ∪ R (Rmin: a minimum hop set and Ralt: an alternative paths set) The CBR selects the largest credit path P.credits in each set, minimum hop (minhop) paths set Rmin and alternative paths set Ralt upon flow arrival The flow is routed along the minimum hop path that has the largest credit Pmin that is larger than the alternative path that has the largest credits Palt following the formula (1): P Credits ≥ Ф x P Credits , where Ф≤ (1) Otherwise, Palt will be chosen The CBR uses the parameter Ф as a system parameter that controls the usage of alternative paths The CBR also uses blocking probability in crediting schemes to improve the performance of the algorithm The path credits are incremented or decremented upon flow acceptance or rejection using statistics of the path blocking probability Besides, the CBR uses a MAX_CREDITS system parameter to determine the maximum attainable credits for each path by computing the blocking probability ≤ P Credits ≤ MAX_CREDITS (2) The CBR algorithm records rejection and acceptance for each path and uses a moving window for a predetermined period of M connection requests It uses for flow acceptance and for flow rejection, dividing the number of 0's by M to calculate each path blocking probability for the period of M connection requests The main problem with CBR is that a path’s credits are only updated each time that path is selected If a path is selected infrequently, then its credit value will become stale leading to errors in the selection process Another scheme of QoS Routing using local information is the scheme of Highest Minimum Bandwidth routing algorithm (HMB), see [4] The HMB algorithm also requires every node in the network to predetermine and maintain a set of candidate paths between all pairs of source and destination node Then, each path will be associated with a variable Journal of Military Science and Technology, Special Issue, No 48A, - 2017 59 Electronics and Automation that is the minimum residual bandwidth (minResBw) link in that path This value is taken, compared with the one of other paths among the set candidate paths After the comparison, the HMB will choose the best path that has the largest residual bandwidth among the paths Then, the HMB will route the flow-in through the selected path.As well as other localized algorithm, in the HMB algorithm, a predefined set of candidate paths R must be required and maintained first However, in the HMB, the difference is that the algorithm must specify the highest value of minResBw among candidate path set The minResBw link of selected path will have preference that will help itself to be reselected next time With the comparison like that, HMB requires to collect all of residual bandwidth of all paths to each destination This will lead to a high latency if there are a lot of long paths between that pair CBR and HBM are quite simple and they have the good performances as described in the references [3-4], so we choose them to compare with our proposed algorithm through simulations in which we use the same patterns of experiments and collect results We also choose the traditional global QoS routing algorithm (WSP) in some simulations as well The results of simulations will be showed in the section A NEW QoS ROUTING ALGORITHM AND NETWORK BANDWIDTH BALANCING EVALUATION 3.1 QoSrouting and network bandwidth balance evaluation QoS routing is gradually becoming an essential part of today’s data networks as many applications rely on QoS routing to provide the promised quality services QoS routing uses network state information and resource availability in addition to the QoS requirements to meet such demands QoS routing algorithms that select paths with sufficient residual resources to meet the QoS constraints have played a critical role in meeting the required service level In addition, that, QoS routing algorithms should also compromise between resource utilization and network traffic balancing to achieve high routing performance To evaluate the network balance, there are many ways as in [5-7] In this paper, we can build the formula to evaluate the bandwidth balancing in the network This formula is derived from the value of evaluation of differential bandwidth in all links of network To count the “differential bandwidth” value, it is required for us to collect the residual bandwidth of all links in the network With these data, we build the connection matrix {mrBrij} of all the paths available for data in that network as follows: mrBRij N Qj mrBw21 mrBwN1 mrBw mrBw12 mrBwN2 mrBw N mrBw1N mrBw2N mrBw Where:mrBwijis the minimum residual bandwidth of links in the path that has the largest residual bandwidth among the set of candidate paths between the two nodes i and j in the network; N is the number of nodes in network 60 T.M.Anh,N.C.Trinh, B.T.M.Tu, “A novel solution of QoSrounting…” Research From this matrix, we calculate the value of differential bandwidth (value of DB) as the formula below: ∑ ValueofDB = ( − ) − ∑ − (3) Where: Q = ∑ = mrBw ∑ ( − ) (4) (5) The value (3) can be named as “Value of DB” or VDB It expresses the differential usable bandwidth in the network The routing algorithm is more effective when it makes the network less differential It means that if the network using routing algorithm A has lower value of VDB (less differential in bandwidth) than that network using routing algorithm B, we can conclude that the algorithmA routes flows more effectively than the other does That is becausethe routing algorithm A makes network less disparity of residual bandwidth than the other Therefore, the smaller this value of VDB is, the more effective the network is We will use this metric to evaluate the effectiveness of routing algorithms as well as the impacts of routing algorithm on the bandwidth balance of network in the section 3.2 Description of the proposed algorithm The QoS requirement of a flow is given as a set of constraints that the routing algorithm should meet to find out a feasible path A QoS routing algorithm usually uses one or more types of metrics to be the constraints for path selection In the QoS metrics, bandwidth and delay time are currently the most significant QoS performance metrics for customers’ services Hence, we use Bandwidth and Delay time as criteria in our scheme, and call our scheme as Bandwidth-delay-Constraint QoS Routing Algorithm or BQRA Like other localized routing algorithms, our algorithm (BQRA) also requires every node to maintain a predetermined set of candidate paths R to each possible destination Moreover, every path PR associated with a variable P.Quality=P.[Bandwidth,Delay] and an index of that path which called βi Call Bi as the number of flows blocked on that path and Ti as the number of flows used by that path We reckon: βi = (1-Bi/Ti) To choose path, the set R (all of candidate paths) will be ranged for the value βi Upon flow arrival, BQRA selects the path P with maximum value of βior max(βi), checks the demand of the flow from SLA (Service Level Agreement) and uses it for choosing path In the algorithm of BQRA, we call that demand as SLA.[Bandwidth,Delay] or SLA If there is not any QoS requirement from flow, it will choose the path P to route Otherwise, it compares P Quality and SLA (the demand) From the comparison between P.Quality and SLA, we have some cases: If P.Quality ≥ SLA, path P will be chosen and BQRA send the flow-in through path P If P.Quality< SLA, BQRA selects the next P in the R (the max βi of the rest of R) The loop will be done until finding out the path has maximum value of βi and P.Quality≥SLA (The way to compare will be discussed in part of 3.5) Journal of Military Science and Technology, Special Issue, No 48A, - 2017 61 Electronics and Automation If in the set R, there is not any path that has P.Quality larger than SLA, the arriving flow obviously is cancelled In that case, the index Ti, and Bi will increase When transmitting successfully a flow to destination node, only value of Ti increases The index βi is a value for evaluating the path When the path P is selected and P.Quality is greater than the value of requested quality (SLA), this indicates that the candidate path has good quality, and the path will be ready to transmit that flow The value of Ti of that path increases, that leads to the increment of βi After that, the following flow will use this index βi to be criterion to choose path, and next loop re-begins Therefore, after one flow processed, βi changes accordingly to the capacity of that path and probability of that path changed correspondingly Due to the fact that the distribution of network resource is based on probability of successful transmitting of flows in network, BQRA itself affects to the balancing of network as well 3.3 The flowchart of BQRA From the steps described in the previous part, we can study the flowchart as follows: Figure The BQRA flowchart 3.4 The complexity and overhead The overhead of global QoS routing can be attributed to two factors: The computation complexity required to find feasible paths and the excessive signaling overhead resulting from network state information exchange And as analyzed in [2], the scheme of global QoS routing algorithm like WSP which uses Dijikstra algorithm takes at least O(NlogN+L) time, where N is the number of nodes, and L is the number of links from that network At the same time, the localized schemes such as CBR, HMB or BQRA use the way of routing that selects path from the pre-determined set of candidate paths R, with the size of R is |R| The path selection is an invocation of a weighted-round-robin like path selector (wrrps), whose worst-case time complexity is O(|R|), as in [6] In addition, these localized 62 T.M.Anh,N.C.Trinh, B.T.M.Tu, “A novel solution of QoSrounting…” Research schemes require updating information, which takes a constant time O(1) Hence, with communication overhead, BQRA or other localized schemes requires very little over and above computing the blocking probability based on acceptance or rejection of a path, while at the same time, global algorithms require a huge amount of overhead to keep the link state information updated In conclusion, the computation of our case at source node anyway is much smaller than the one of traditional WSP cases 3.5 The metric selection and comparison We can make the comparison of the two metrics (Bandwidth and Delay) successively Firstly, we compare the bandwidth of the path with the demanded bandwidth of flow Then, we continue to compare the value of Delay between path and flow The path is only chosen when we have two values of true for two comparisons Note: We compare P.Bandwidth ≥ F.Bandwidth and P.Delay ≤ F.Delay, where Bandwidth is the minimum residual bandwidth of any link on path and Delay is the sum of all delay of propagation from all links on this path The procedure is as follows: PROCEDURE Compare(p,f) If p.Bandwidth ≥ f.Bandwidth If p.Delay ≤ f.Delay p is chosen Else p is discarded Else p is discarded Figure 2.Comparison between two constraints bandwidth and delay PERFORMANCE EVALUATION In this section, we realize the performance of the BQRA scheme and compare it with the CBR, HMB and the global QoS routing scheme widest shortest path (WSP) All the experiments will be set in the same condition Next, we analyze the results of our simulation model and performance metrics of these schemes 4.1 Simulation Model Using the popular simulator OMNeT++ 4.5 in [10], we simulate a network with 18 nodes and 108 links Links between these nodes are all bidirectional with the capacity C = 20Mbps in each direction and the same value of delay D = 20ms Flows arrive to each source node according to a Poisson process with rate λ and destination nodes are selected randomly (each node can be source or destination) Flow duration is exponentially distributed with mean Bandwidth of flows is uniformly distributed within [15MBytes], and the required value set for Delay of each flow is randomly distributed between 20ms and 250ms Network is performed in Figure As analyzed in [8-9], the offered network load is: bh/LC (6) where N is the number of nodes, b is the average bandwidth required by a flow, h is the average path length (in number of hops) and L is the number of links in the network In the experiments, we set N=18, L=60, h=2.36, 1/= 60s Since the performance of routing algorithms may vary across different load conditions, Journal of Military Science and Technology, Special Issue, No 48A, - 2017 63 Electronics and Automation our simulation experiments consider several types of different load conditions through the value of λ according to experiments of from low loads to high loads Figure Network of 18 nodes 4.2 Evaluation Parameters To compare with other schemes, we choose flow blocking probability as criteria as well as the simulation in [3-4] The blocking probabilities are calculated based on the most recent 100,000 flows The time of simulation is set about of 20 minutes, with more than 2.5 million of flows emitted We take the ratio as follows: Flow Blocking Probability = |B|/|T| (7) Where: |T| is the total of all flows, |B| is total of blocked flows We also calculate the overall end-to-end delay of network when we run the scheme BQRA and the scheme of WSP with different load Next, as introduced in the part 3.1, we collect the values of residual bandwidth from the network, and calculate the values of DB (VDB) of the network as the formula (3) From these values, we can analyze the impacts ofthe routing algorithms on the performance of that network 4.3 Simulation Results With the results of performances collected from the simulation, we compare among schemes, as shown in some figures below Figure Flow Blocking Probability Figure shows the performance of BQRA against CBR, HMB and WSP in terms of flow blocking probability under load  varies from 0.2 to 0.5 From these values, we see that under low load ( ≤ 0.25), the difference in the performance of the routing algorithms is quite small, because finding available path with sufficient bandwidth is easy and flows are almost accepted.When  is high (more than 0.3), there are some differences as showed 64 T.M.Anh,N.C.Trinh, B.T.M.Tu, “A novel solution of QoSrounting…” Research above Many flows drop or fail to get destination node, then flow blocking probability grows rapidly as showed in Figure Moreover, the setting index for choosing path helps to avoid the congestion of flows come at nodes simultaneously, particularly when the load increases and the links begins to become congested If congestion happens, flows will be redirected to other path and the index decreases at once Then, the source node might reduce the using of these paths, which have low index Therefore, the probability of flow blocking is considerably low against the case of CBR, HMB and WSP as well From the Figure 5, we can see that when the number of flows increases, the Average End-to-End Delay will keep a stable value and the BQRA expresses the more efficiently The margin between two cases is slightly low at  = 0.2, but when the load  = 0.5, it becomes higher as showed below It means that with high load, the congestion happens more frequently, so, this value is higher In our case, the BQRA changes path more frequently based on the index βi When congestion happens, the index of the candidate path diminishes, then, our case changes path Therefore, the average End-to-End Delay is better than case of WSP as well Figure Average End-to-End Delay of flows when =0.2 and 0.5 Next, with the parameters of residual bandwidth of every link, we can calculate the VDB of network at each value of the load  (from 0.2 to 0.5) And the results is showed in the Figure Figure The values of DB when  varies from 0.2 to 0.5 From that, we can see that with high , the difference of bandwidth becomes higher The disparity of bandwidth among links is more, and therefore it makes the VDB of network higher With the disparity of the bandwidth of links in the network is smaller, the scheme BQRA shows that it makes the bandwidth balancing smoother and more flexible The reason is that with high load, the congestion happens more frequently, so this VDB increases, especially in the WSP case In our case, the flows will change path frequently Journal of Military Science and Technology, Special Issue, No 48A, - 2017 65 Electronics and Automation based on the index βi Hence, it makes the bandwidth usage more equally, and the VDB is little smaller than any scheme else In concluding, the case of BQRA has better performance than other cases such as CBR, HMB or WSP in the experiments that have been done CONCLUSION AND ONGOING WORK In this paper, we proposed a new localized QoS routing algorithm to choose path using only flow information collected locally at source node We have done many experiments to compare the performance among BQRA, CBR, HMB and WSP algorithms These experiments have already showed a better performance of BQRA with better time complexity and very low communication overhead We collect the local information of blocking probability to update the path index This index directly decides the routing, hence makes better quality of routing, on the other hand, better working of network As part of future work, we will investigate the effect of using more QoS parameters to computation at nodes From that, we will adjust the algorithm to make routing more flexible Finally, as proposed in the paper, we will care for the way of building sets of candidate paths, based on knowledge of network as well as reduction of computing at nodes In the future network, with the more sophisticated selection of set of paths, the algorithm will operate more effectively and more reliably REFERENCES [1] C Pornavalai, G Chakraborty, N Shiratori, "QoS based routing algorithm in integrated services packet networks", Proceedings of the IEEE ICNP, 1997 [2] R Guerin, S Kamat, A Orda, T Przygienda, D Williams, “QoS Routing Mechanisms and OSPF Extensions”, Work in Progress, Internet Draft, March 1997 [3] S Alabbad, M E Woodward “Localized Credit Based Routing: Performance Evaluation Using Simulation", Proc of IEEE 39th Annual Simulation Symposium, Huntsville, Al USA April 2-6 2006 [4] T A Al Ghamdi and M E Woodward, "Novel localized QoS routing algorithms," in Proc IEEE 9th Malaysia International Conference on Communications, Kuala Lumpur, Malaysia, Dec 2009, pp 199-204 [5] A Gonzlez-Ruiz and Y Mostofi, “Distributed load balancing overdirected network topologies,” in Proc ACC 09 , St Louis, Missouri,USA, 2009 [6] Yaling Yang, Jun Wang, Robin Kravets, “MobiCom Poster Abstract: Load-balanced Routing For Mesh Network”, Mobile Computing and Communications Review, Volume 10, No [7] Tran Minh Anh, Nguyen Chien Trinh, “Propose a Metric to Evaluate Network Quality”, in Proceedings of the 06th International Conference on Electronics, Information, and Communication (ICCE 2016), pp 493-503, Jul 2016, Halong, Vietnam [8] A Shaikh, J Rexford, K Shin, “Load-Sensitive Routing of Long-Lived IP Flows”, ACM SIGCOMM 1999 [9] A.Shaikh, J.Rexford, K.G.Shin, “Efficient Precomputation of Quality-of-Service Routes” Proc IEEE NOSSDAV 98, July, 1998 [10].A Varga, “The OMNeT++ Discrete Event Simulation System”, the European Simulation Multiconference, Prague, Czech Republic, 2001 66 T.M.Anh,N.C.Trinh, B.T.M.Tu, “A novel solution of QoSrounting…” Research TÓM TẮT GIẢI PHÁP ĐỊNH TUYẾN ĐẢM BẢO QoS MỚI VỚI THUẬT TOÁN ĐỊNH TUYẾN ĐA RÀNG BUỘC Trongqtrìnhcungcấpdịchvụviễnthơngmớihiện nay, ucầuvềchấtlượngmạnglướingàycàngtrởnênphổbiếnvàphứctạpvớibăngthơngcao, giátrịđộtrễ, độmấtgóinhỏ Đểđảmbảochochấtlượngmạnglưới, kiểuthuậttốnđịnhtuyếnđảmbảoQoSchỉsửdụngthơng tin tạinộibộnútđểđịnhtuyếnthơng tin đãvàđangđượcnghiêncứunhiềuhiện nay, nhưlàmộtgiảiphápbổtrợquantrọngchocáckiểuthuậttốnđảmbảoQoStruyềnthống KhácvớikiểuđịnhtuyếnđảmbảoQoStruyềnthốngdùngthơng tin tồnmạngđểchọnđường, thuậttốnđịnhtuyếndùngthơng tin nộibộchỉdựavàothơng tin thunhặttạinộibộnútđểđưaraquyếtđịnhchọnđường Kiểuthuậttốnnàyhứahẹnlàmộtgiảiphápchoviệcđápứngnhucầucủathịtrườngviễnthơn gtrongtươnglai.BàibáonàygiớithiệumộtthuậttốnđịnhtuyếnđảmbảoQoSdùngthơng tin nộibộmớisửdụngbăngthơngvàđộtrễlàmhairàngbuộc; nghiêncứuảnhhưởngcủacácthuậttốnđịnhtuyếnđảmbảoQoSlênviệccânbằngbăngthơ ngmạng qua mộthệsốđánhgiáđộchênhlệchbăngthơngđượcđềxuấttrongbàibáo Cácthínghiệmmơphỏngthựchiệngiữathuậttốnđềxuấtvàcácthuậttốnliênquankhácvớ icácmơitrườngthínghiệmtươngtự Qua kếtquảmơphỏng, bàibáochothấyhiệuquảtốthơncủagiảithuậtđềxuất Từ khóa: Giải thuật, Cân bằng, Nội bộ, Băng thông, Độ trễ Received date, 16th January 2017 Revised manuscript, 29th March 2017 Published on 26th April 2017 Author affiliations: Posts and Telecommunications Institute of Technology, Hanoi City, Vietnam University of Science and Technology, University of Danang, Vietnam *Correspondingauthor:anhtm.dng@vnpt.vn Journal of Military Science and Technology, Special Issue, No 48A, - 2017 67 ... BANDWIDTH BALANCING EVALUATION 3.1 QoSrouting and network bandwidth balance evaluation QoS routing is gradually becoming an essential part of today’s data networks as many applications rely on QoS routing. .. to maintain a predetermined set of candidate paths R to each possible destination Moreover, every path PR associated with a variable P.Quality=P.[Bandwidth,Delay] and an index of that path which... localized QoS routing algorithm has been proposed recently as a viable alternative to global QoS routing In localized routing algorithm, nodes firstly build the set of candidate paths between all