Abstract
Flow-level traffic statistics information plays a vital role for many applications, such as network management, attack detection and packet engineering. Compared with TCAM-based counting and packet sampling, sketches can provide flow-level traffic estimation with bounded error using compact data structures, thus have been widely used for traffic measurement. Under many practical applications, several different sketches should be deployed on each switch to support various requirements of traffic measurement. If each arrival packet is measured by all sketches on a switch (i.e., without sketch configuration), it may lead to redundant measurement, and cost massive CPU resource, especially with increasing traffic amount. Due to limited computing capacity on most commodity switches, heavy traffic measurement overhead will seriously interfere with the basic rule operations, especially when some switches need to deal with many new-arrival flows or update routes of existing flows. To address this challenge, we propose a spatial sketch configuration problem for the general case. As a case study, we present optimal sketch configuration for proportional fairness with per-switch computing capacity constraint (SCP), so that each sketch can measure enough flows without unduly restricting the number of flows measured by other sketches in the network. Due to the NP-hardness of this problem, a greedy-based algorithm with approximation ratio 1/3 is presented, and its time complexity is analyzed. We implement the proposed sketch configuration solution on the platform. The extensive simulation results and the experimental results show that the proposed algorithm can measure traffic of 44%–91% more flows compared with the existing solutions with CPU resource constraint.
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References
Hong, C.-Y., et al.: Achieving high utilization with software-driven wan. In: ACM SIGCOMM, pp. 15–26 (2013)
Li, D., Shang, Y., Chen, C.: Software defined green data center network with exclusive routing. In: IEEE INFOCOM, pp. 1743–1751 (2014)
Wang, B., Zheng, Y., Lou, W., Hou, Y.T.: DDoS attack protection in the era of cloud computing and software-defined networking. Comput. Netw. 81, 308–319 (2015)
Yan, Q., Yu, F.R., Gong, Q., Li, J.: Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges. IEEE Commun. Surv. Tutorials 18(1), 602–622 (2016)
Agarwal, S., Kodialam, M., Lakshman, T.: Traffic engineering in software defined networks. In: IEEE INFOCOM, pp. 2211–2219 (2013)
Su, Z., Wang, T., Xia, Y., Hamdi, M.: Flowcover: Low-cost flow monitoring scheme in software defined networks. In: Global Communications Conference (GLOBECOM), pp. 1956–1961. IEEE (2014)
Xu, H., Yu, Z., Qian, C., Li, X.-Y., Liu, Z.: Minimizing flow statistics collection cost of sdn using wildcard requests. In: INFOCOM 2017-IEEE Conference on Computer Communications, IEEE. pp. 1–9. IEEE (2017)
Estan, C., Keys, K., Moore, D., Varghese, G.: Building a better netflow. ACM SIGCOMM Comput. Commun. Rev. 34(4), 245–256 (2004)
Phaal, P., Lavine, M.: sflow version 5. http://www.sflow.org/sflow_version_5. txt. Accessed July 2004
Kumar, A., Xu, J., Wang, J.: Space-code bloom filter for efficient per-flow traffic measurement. IEEE J. Sel. Areas Commun. 24(12), 2327–2339 (2006)
Li, T., Chen, S., Ling, Y.: Per-flow traffic measurement through randomized counter sharing. Netw. IEEE/ACM Trans. 20(5), 1622–1634 (2012)
Curtis, A.R., Mogul, J.C., Tourrilhes, J., Yalagandula, P., Sharma, P., Banerjee, S.: DevoFlow: scaling flow management for high-performance networks. ACM SIGCOMM Comput. Commun. Rev. 41(4), 254–265 (2011)
Yu, M., Jose, L., Miao, R.: Software defined traffic measurement with opensketch. In: the 10th USENIX Symposium on Networked Systems Design and Implementation, pp. 29–42 (2013)
Huang, Q., et al.: Sketchvisor: robust network measurement for software packet processing. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pp. 113–126. ACM (2017)
Cormode, G., Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1), 58–75 (2005)
Wang, A., Guo, Y., Hao, F., Lakshman, T., Chen, S.: Scotch: elastically scaling up SDN control-plane using vswitch based overlay. In: Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies, pp. 403–414. ACM (2014)
Sekar, V., Reiter, M.K., Willinger, W., Zhang, H., Kompella, R.R., Andersen, D.G.: csamp: a system for network-wide flow monitoring. In: NSDI, vol. 8, pp. 233–246 (2008)
Yu, Y., Qian, C., Li, X.: Distributed and collaborative traffic monitoring in software defined networks. In: Proceedings of the third workshop on HotSDN, pp. 85–90. ACM (2014)
Li, W., et al.: AP association for proportional fairness in multirate WLANs. IEEE/ACM Trans. Netw. (TON) 22(1), 191–202 (2014)
Wang, X., Kar, K.: Cross-layer rate control for end-to-end proportional fairness in wireless networks with random access. In: Proceedings of the 6th ACM International Symposium on Mobile ad hoc Networking and Computing, pp. 157–168. ACM (2005)
Alizadeh, M., et al.: Data center tcp (dctcp). ACM SIGCOMM Comput. Commun. Rev. 41(4), 63–74 (2011)
Wang, S., Huang, F., Wang, C.: Adaptive proportional fairness resource allocation for of dm-based cognitive radio networks. Wirel. Netw. 19(3), 273–284 (2013)
Open vswitch http://openvswitch.org/
Liu, Z., Manousis, A., Vorsanger, G., Sekar, V., Braverman, V.: One sketch to rule them all: Rethinking network flow monitoring with univmon. In: Proceedings of the 2016 conference on ACM SIGCOMM 2016 Conference, pp. 101–114. ACM (2016)
Wang, T., Liu, F., Guo, J., Xu, H.: Dynamic SDN controller assignment in data center networks: stable matching with transfers. In: Proceedings of INFOCOM (2016)
Li, L., Pal, M., Yang, Y. R.: Proportional fairness in multi-rate wireless lans. In: INFOCOM 2008. The 27th Conference on Computer Communications. IEEE, pp. 1004–1012. IEEE (2008)
Gupta, A.: Approximations algorithms (2005)
Deng, C.S., Liang, C.Y.: Mixed coding greedy differential evolution algorithm for 0–1 knapsack problem. Comput. Eng. 35(23), 24–26 (2009)
Moshref, M., Yu, M., Govindan, R., Vahdat, A.: Scream: sketch resource allocation for software-defined measurement. In: CoNEXT, Heidelberg, Germany (2015)
Kwak, J., Kim, Y., Lee, J., Chong, S.: Dream: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J. Sel. Areas Commun. 33(12), 2510–2523 (2015)
The network topology from the monash university. http://www.ecse.monash.edu.au/twiki/bin/view/InFocus/LargePacket-switchingNetworkTopologies
Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. In: ACM SIGCOMM Computer Communication Review, vol. 38, no. 4, pp. 63–74. ACM (2008)
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Yao, D., Xu, H., Wang, H., Huang, L., Tu, H. (2021). Spatial Sketch Configuration for Traffic Measurement in Software Defined Networks. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_41
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