# Virtual Network Embedding Algorithms Based on Best-Fit Subgraph Detection

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4 Figure 1. Example of VNE (, ) =(,). +.h( ) Where h( ) is the length of the substrate path that the virtual link is mapped to. Objectives: the main objectives are to increase the revenue and decrease the cost of embedding virtual networks in the long run. To evaluate the achievement of these objectives, we use the following metrics: - The long-term average revenue, which is defined by Where =, and is the total time. - The VNR acceptance ratio, which is defined by lim (,) (1) Where is the set of all accepted virtual network requests. - The long term R/Cost ratio, which is defined by (2) 65 lim (,) (3) (,) 4. The Proposed Algorithms We proposed two VNE algorithms to increase the acceptance ratio for fragmented SNs and minimize the remapping process. In the next sub-sections, we describe the motivation behind the proposed algorithms and describe the details of the proposed algorithms. 4.1 Motivation For each VNR, we may have different VNE solutions. For example, table 1 shows some VNE solutions to embed VN in figure 2(a) on the SN in figure 2(c). Although, solution 2 consumes less SN s resources among others, it reduces the possibility to accept more future VN requests, such as VN in figure 2(b). Mapping VN in figure 2(b) on the SN in figure 2(c) requires remapping the VN in figure 2(a) to solution1. Then, the VN in figure 2(b) can be mapped as following: Node mapping:,,, Link mapping: (, ) (, ), (, ) (, ), (, ), (, ) (, ), (, ) (, )

5 From this example, we conclude that increasing the possibility to accept more future VN requests does not only depend on the mapping cost. This conclusion motivates us to propose VNE algorithms for selecting VNE solutions that increase the possibility to accept as many VNRs as possible. Even if the selected solutions maybe increase the embedding cost, the revenue is increased by accommodating more VNRs. Figure 2. Example of VNs and SN Table 1. Some VNE solutions to embed VN in figure 2(a) on the SN in figure 2(c) Node mapping Link mapping Solution1,, (, ) (,), (, ) (, ), (, ) (, ), (, ) Solution2,, (, ) (,), (, ) (, ), (,) (, ) Solution3,, (, ) (, ), (, ), (, ) (,), (, ), (, ) (, ) 4.2 BFSN VNE Algorithm The inputs to BFSN algorithm are: (1) : the virtual network to be embed, (2) G : the substrate network to embed on, (3) Max_hops: the maximum allowed substrate path length, and (4) Max_backtrack: the upper bound of nodes re-mapping operation. The outputs of BFSN algorithm is a map M(G ). Figure 3 shows the steps of BFSN algorithm. The algorithm constructs breadth-first searching tree of. The root node of the constructed tree is the virtual node with the largest resources (sum of CPU and BW). Nodes in each level in the created breadth-first searching tree are sorted in descending order based on their resources. The set of candidate sub-substrate networks = =,,, is built, where N is a set of all substrate nodes in G with available CPU minimum required CPU (the minimum CPU in G ), and P is the set of all loop free substrate paths in G with available BW minimum required BW and with length less than or equal a pre-specified maximum path length. Candidate sub-substrate networks are specified by visiting substrate nodes in N sequentially and creating a breadth-first searching tree from a substrate node if it has not been included in any sub-substrate network yet. Substrate node is added to the sub-substrate network if it has not been included in any sub-substrate network, and has been connected with previously added substrate node through a substrate path in P. G is reduced by removing sub-substrate networks that do not have enough resources to embed. All remaining sub-substrate 66

6 networks in are sorted in ascending order based on their total available resources. The BFSN algorithm embeds VN on a sub-sn using Embed() function, which is described in figure 6. In the Embed() function, candidate substrate node list for each virtual node is built by collecting all substrate nodes that have available CPU capacity at least as large as the virtual node CPU and have a loop free substrate path to each substrate node contains one of the previously mapped neighbors. Each substrate path should satisfy the constraint of the maximum substrate path length, and have available bandwidth greater than or equal the bandwidth of the virtual link between the virtual node and its previously mapped neighbor. Candidate substrate nodes for each virtual node are collected by creating a breadth-first search tree from each substrate node contains one of the previously mapped neighbors, and finding the common substrate nodes between the created trees. In the constructed trees, substrate nodes should satisfy the CPU constraints for virtual node, and substrate paths should satisfy the connectivity constraints to connect the virtual node with its neighbors. By this way, all candidate substrate nodes in the list satisfy all constraints (CPU and connectivity constraints). Substrate nodes in the candidate substrate node list are sorted in ascending order according to the total cost of embedding virtual links from the virtual node to all previously embedded neighbors. If the virtual node is a root node, the candidate substrate node list is a set of all substrate nodes that have enough resources to embed the virtual node. The candidate substrate nodes for the root are sorted in descending order according to the total available resources. Virtual node is sequentially mapped to substrate nodes in its candidate substrate node list. If there is no appropriate substrate node in its candidate substrate node list, we backtrack to the previously mapped node, re-map it to the next candidate substrate node, and continue to the next node. Mappings of the virtual node and its virtual links are added to ( ) by using the function Add() in line 3. In line 6, Delete() function is used to perform the backtracking process. Algorithm 1 The Details of the BFSN VNE algorithm 1: Build breadth-first searching tree of from virtual node with largest resources. 2: Sort all nodes in each level in the created breadth-first tree in descending order according to their required resources. 3: Build candidate sub-substrate networks 4: for each G do 5: backtrack count=0 6: if Embed(, G, ( )) then 7: return true 8: end for 9: return false Algorithm 2 The Details of Embed(, G,( )) Function 1: Build candidate substrate node list for 2: for each in 3: Add,, ( ) 4: if Embed(, G, ( )) then return true 5: else 6: Delete,, ( ) 7: if backtrack count > Max_backtrack then return false 8: end for 9: backtrack count ++ 10: return false 67

7 For example, to map VN in figure 2 (a) on the SN in figure 2 (c), we construct a breadth-first searching tree from the virtual node b, which is the virtual node with largest resources. Virtual nodes in each level are sorted in descending order based on their resources as in figure 3. Figure 4 shows candidate sub-substrate networks that are specified, and sorted in ascending order based on its total available resources. Numbers in triangles represent numbers of hops for links. Figure 5 shows the embedding steps of the VN in figure 2 (a) on the first candidate sub-sn, which is the candidate sub-sn in figure 4 (a). The candidate substrate nodes list for the root node b is built as,,. The root node b is mapped to the substrate node G as shown in figure 5(b). The next virtual node in the breadth first search tree in figure 3 is the virtual node a. The set of candidate substrate nodes for the virtual node a is specified as,,, which is sorted in ascending order based on the cost of mapping the virtual node a to each of them. G substrate node is included because the proposed algorithm allows substrate nodes to include more than one virtual node from the same VN. Figure 5(c) shows the SN after mapping the virtual node a to the substrate node G. Finally, the virtual node c is mapped to the substrate node F, which is the only substrate node that can satisfy the connectivity constraints to the substrate node G (the total required bandwidth is 50). Figure 3. Sorted breadth-first searching tree for the VN in figure 2 (a) Figure 4. Candidate sub-substrate networks for the VN in figure 2 (a) Figure 5. Embedding VN in figure 2 (a) on the candidate sub-substrate network in figure 4 (a) 68

8 4.2 BFSN-HEM VNE Algorithm Allowing substrate nodes to contain more than one virtual node from the same VN coarsens the VN. For example, the VN in figure 2 (a) is coarsened as shown in figure 6. Coarsening VNs reduces the embedding cost by eliminating the cost of embedding virtual links between virtual nodes on the same substrate node. However, VNs coarsening performed by BFSN algorithm is not the optimal coarsening solution. For this reason, we proposed BFSN-HEM algorithm, which coarsens VNs using Heavy Edge Matching (HEM) technique to minimize its sizes and to minimize the edge-cut before embedding them. The Details of the Coarsening() function are shown in algorithm 3. Coarsening() function sorts all links in descending order based on their bandwidth. The incident nodes of each link is merged if the total CPU less than or equal the maximum allowed CPU. After each coarsening step, VN is updated to a new coarsened virtual network with merged nodes and links. In the BFSN-HEM algorithm, coarsened virtual nodes are mapped as in BFSN algorithm. Coarsened virtual links are mapped by mapping its virtual links independently. Figure 6. Example of VN coarsening Algorithm 3 The Details of the Coarsening(, ) function 1: Sort all links in in descending order based on their bandwidth. 2: for each do 3: if the summation of its incident nodes then 4: Update by merging the incident nodes of 5: Coarsening(, ) 6: return 7: end if 8: end for 9: return 5. Performance Evaluation The proposed VN embedding algorithms are evaluated by comparing its performance with some of existing algorithms. First, we implemented four algorithms: BFSN, BFSN-HEM, RW-MaxMatch (Cheng et al, 2012), and RW-BFS (Cheng et al, 2011). Second, we generated SN topology and 3000 VN topologies to be used as inputs to the implemented algorithms. Finally, we compared the results from the implemented algorithms. In the following sub-sections, we describe the evaluation environment settings and discuss the results of the simulations. 5.1 Evaluation Environment Settings In our evaluation, the substrate network topology is configured to have 200 nodes with 1000 links. Substrate network is generated using Waxman generator. Bandwidths of the substrate links are real numbers uniformly distributed between 50 and 100 with average 75. We have selected two server configurations: HP ProLiant ML110 G4 (Intel Xeon 3040, 2 cores X 1860 MHz, 4 GB), and HP ProLiant ML110 G5 (Intel Xeon 3075, 2 cores X 2660 MHz, 4 GB). Each substrate node is randomly assigned one of these server configurations. Virtual network topologies are generated using Waxman generator with average connectivity 50%. Number of 69

9 virtual nodes in each VN is variant from 2 to 20. Each virtual node is randomly assigned one of the following CPU: 2500 MIPS, 2000 MIPS, 1000 MIPS, and 500 MIPS, which are correspond to the CPU of Amazon EC2 instance types. Bandwidths of the virtual links are real numbers uniformly distributed between 1 and 50. VN s arrival times are generated randomly with arrival rate 10 VNs per 100 time units. The lifetimes of the VNRs are generated randomly between 300 and 700 time units with average 500 time units VN topologies are generated and stored in brite format. For each algorithm, we run the simulation for time units with the previously generated VNRs. The generated SN topology, generated VNRs topologies, and outputs are available online at (Note 1). For all algorithms, we set the maximum allowed hops (Max_hops) to 2, and the upper bound of remapping process (Max_backtrack) to 3n, where n is the number of nodes in each VNR. We set the value of in Coarsening() function (Algorithm 3) to the maximum available CPU in the sub-substrate network. Note that the BFSN-HEM algorithm does not guarantee that there are enough substrate nodes with available CPU equal to the to embed coarsened virtual nodes if all virtual nodes are coarsened to nodes with CPU equal to, but our experiments have shown that it works very well. This is because, practically most of coarsened virtual nodes have CPU less than. 5.2 Evaluation Results Three metrics have been used to evaluate the performance of the proposed algorithms: the long-term average revenue, which is defined by Equation (1), the VNR acceptance ratio, which is defined by Equation (2), and the long-term R/Cost ratio, which is defined by Equation (3). Figure 7 shows the simulation results using the VNR acceptance ratio to compare the different VNE algorithms. It can be seen that algorithms that embed VNs on best-fit sub-sns increase the acceptance ratio compared with other algorithms. For example, at time unit 30000, in figure 7, the VNR acceptance ratio for the RW-BFS and RW-MaxMatch are 20 and 16 percent, while the VNR acceptance ratio for the BFSN and BFSN_HEM are 59 and 65 percent. Figure 7 also shows that the BFSN-HEM algorithm that coarsens VNs using Heavy Edge Matching technique has better VNR acceptance ratio than the BFSN algorithm. In other words, the proposed algorithms can embed more VNs on the same SN at the same time. Consequently, the proposed algorithms increase the long-term average revenue compared with other algorithms, as shown in figure 8. For example, at time unit 30000, the average revenue for the RW-BFS and RW-MaxMatch are 72 and 33, while the average revenue for the BFSN and BFSN_HEM are 290 and 336. From figures 7 and 8, we can observe that the proposed algorithms enhanced the acceptance ratio around 40 percent while they tripled the long-term average revenue compared with other algorithms. This is because RW-BFS and RW-MaxMatch algorithms lead to SN s resource fragmentation. Thus, it may accept VNRs with small size and reject VNRs with large size. By accepting VNRs with large size, the average revenue of the BFSN and BFSN_HEM algorithms grows faster than the average revenue of the RW-BFS and RW-MaxMatch algorithms. In addition to the higher revenue, the proposed algorithms are faster than other algorithms, because the proposed algorithms deal only with sub-sns instead of the whole SN. Figure 9 depicts the long-term Revenue/Cost ratio for different VN embedding algorithms. As shown in figure 9, the long-term Revenue/Cost ratio of BFSN and BFSN-HEM are nearly the same, but the proposed algorithms perform slightly better than other algorithms. We believe that the reason behind this is the small value for the maximum allowed hops (Max_hops), which allows only embedding VNs if there are solutions with small cost. 6. Conclusion In this paper, we proposed two virtual network embedding algorithms, which embed virtual networks on best-fit sub-substrate networks. Sub-substrate networks are specified by eliminating substrate links and substrate nodes that do not satisfy the connectivity and the CPU constraints. Virtualization technology is exploited to consolidated more than one virtual node from the same virtual network to one substrate node. Virtual networks are coarsened using Heavy Edge Matching (HEM) technique to minimize the cost of embedding virtual links. Our evaluation results show that the proposed algorithms improve the acceptance ratio and the revenue. Finally, we conclude that embedding virtual networks on best-fit sub-substrate networks allows substrate networks to accommodate more virtual networks. Additionally, coarsening virtual networks to coarsened virtual networks with minimal coarsened virtual links minimizes the embedding cost. For the future work, we plan to investigate other coarsening techniques to find the best coursing technique, which increases the acceptance ratio and the revenue while decreasing the embedding cost. 70

10 Figure 7. The VNR acceptance ratio comparison Figure 8. The long-term average revenue comparison Figure 9. The long-term Revenue/Cost ratio comparison 71

11 References Cheng, X., Su, S., Zhang, Z., Shuang, K., Yang, F., Luo, Y., & Wang, J. (2012). Virtual network embedding through topology awareness and optimization. Computer Networks, 56(6), Cheng, X., Su, S., Zhang, Z., Wang, H., Yang, F., Luo, Y., & Wang, J. (2011). Virtual network embedding through topology-aware node ranking. SIGCOMM Comput. Commun. Rev., 41(2), Chowdhury, M., Rahman, M., & Boutaba, R. (2012). Vineyard: Virtual network embedding algorithms with coordinated node and link mapping. IEEE/ACM Transactions on Networking, 20(1), Chowdhury, N., Rahman, M., & Boutaba, R. (2009). Virtual network embedding with coordinated node and link mapping. In INFOCOM 2009, IEEE, pages Di, H., Li, L., Anand, V., Yu, H., & Sun, G. (2010). Cost efficient virtual infrastructure mapping using subgraph isomorphism. In Communications and Photonics Conference and Exhibition (ACP), 2010 Asia, pages Fajjari, I., Aitsaadi, N., & Pujolle, G. (2013). Cloud networking: An overview of virtual network embedding strategies. In Global Information Infrastructure Symposium, 2013, pages Fajjari, I., Aitsaadi, N., Pujolle, G., & Zimmermann, H. (2011). VNR algorithm: A greedy approach for virtual networks reconfigurations. In Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE, pages Fischer, A., Botero, J., Till Beck, M., de Meer, H., & Hesselbach, X. (2013). Virtual network embedding: A survey. Communications Surveys Tutorials, IEEE, 15(4), Houidi, I., Louati, W., Ameur, W. B., & Zeghlache, D. (2011). Virtual network provisioning across multiple substrate networks. Computer Networks, 55(4), Special Issue on Architectures and Protocols for the Future Internet. Houidi, I., Louati, W., & Zeghlache, D. (2008a). A distributed and autonomic virtual network mapping framework. In Fourth International Conference on Autonomic and Autonomous Systems, ICAS 2008, pages Houidi, I., Louati, W., & Zeghlache, D. (2008b). A distributed virtual network mapping algorithm. In IEEE International Conference on Communications, ICC 08, pages Leivadeas, A., Papagianni, C., & Papavassiliou, S. (2013). Efficient resource mapping framework over networked clouds via iterated local search-based request partitioning. IEEE Transactions on Parallel and Distributed Systems, 24(6), Lischka, J., & Karl, H. (2009). A virtual network mapping algorithm based on subgraph isomorphism detection. In Proceedings of the 1st ACM Workshop on Virtualized Infrastructure Systems and Architectures, VISA 09, pages 81 88, New York, NY, USA. ACM. Lv, B., Wang, Z., Huang, T., Chen, J., & Liu, Y. (2010). Virtual resource organization and virtual network embedding across multiple domains. In International Conference on Multimedia Information Networking and Security (MINES), 2010, pages Nogueira, J., Melo, M., Carapinha, J., & Sargento, S. (2011). Virtual network mapping into heterogeneous substrate networks. In IEEE Symposium on Computers and Communications (ISCC), 2011, Samuel, F., Chowdhury, M., & Boutaba, R. (2013). Polyvine: policy-based virtual network embedding across multiple domains. Journal of Internet Services and Applications, 4(1). Sun, G., Yu, H., Anand, V., & Li, L. (2013). A cost efficient framework and algorithm for embedding dynamic virtual network requests. Future Generation Computer Systems, 29(5), Special section: Hybrid Cloud Computing. 72

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