Abstract
In recent years, in-memory key-value cache systems have become increasingly popular in tackling real-time and interactive data processing tasks. Caching systems are often used to help with the temporary storage and processing of data. Due to skewed and dynamic workload patterns, e.g. data increase/decrease or request changes in read/write ratio, it can cause load imbalance and degrade performance of caching systems.
Migrating data is often essential for balancing load in distributed storage systems. However, it can be difficult to determine when to move data, where to move data, and how much data to move. This depends on the resources required, e.g. CPU, memory and bandwidth, as well as polices on data movement. Since frequent and global rebalance of systems may affect the QoS of applications utilizing caching systems, it is necessary to minimize system imbalances whilst considering the total migration cost. We propose a novel distributed load balancing method for the mainstream Cloud-based data framework (Redis Cluster). We show how distributed graph clustering through load balancing can be used to exploit varying rebalancing scenarios comprising local and global needs. During the rebalancing process, three phrases are adopted — random walk matching load balancing, local round-robin migration and data migration between the trigger node and new added servers. Our experiments show that the proposed approach can reduce migration time compared with other approach by 30s and load imbalance degree can be reduced by 4X when the locality degree reaches 50% whilst achieving high throughput.
Jiangsu Overseas Research & Training Program for University Prominent Young & Middle-aged Teachers and Presidents, Nanjing University of Posts and Telecommuncations Scientific Research Fund (NY215115).
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References
Hong, Y., Thottethodi, M.: Understanding and mitigating the impact of load imbalance in the memory caching tier. In: Lohman, G.M. (ed.) ACM Symposium on Cloud Computing, SOCC 2013, Santa Clara, CA, USA, 1–3 October 2013, pp. 13:1–13:17. ACM (2013)
Cheng, Y., Gupta, A., Butt, A.R.: An in-memory object caching framework with adaptive load balancing. In: Réveillère, L., Harris, T., Herlihy, M. (eds.) Proceedings of the Tenth European Conference on Computer Systems, EuroSys 2015, Bordeaux, France, 21–24 April 2015, pp. 4:1–4:16. ACM (2015)
Karger, D.R., Lehman, E., Leighton, F.T., Panigrahy, R., Levine, M.S., Lewin, D.: Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the world wide web. In: Leighton, F.T., Shor, P.W. (eds.) Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing, El Paso, Texas, USA, 4–6 May 1997, pp. 654–663. ACM (1997)
Fan, B., Lim, H., Andersen, D.G., Kaminsky, M.: Small cache, big effect: provable load balancing for randomly partitioned cluster services. In: Chase, J.S., Abbadi, A.E. (eds.) ACM Symposium on Cloud Computing in Conjunction with SOSP 2011, SOCC 2011, Cascais, Portugal, 26–28 October 2011, p. 23. ACM (2011)
https://redis.io/topics. “Redis cluster specification,” Technical report (2018)
Sun, H., Zanetti, L.: Distributed graph clustering by load balancing. In: Scheideler, C., Hajiaghayi, M.T. (eds.) Proceedings of the 29th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2017, Washington DC, USA, 24–26 July 2017, pp. 163–171. ACM (2017)
Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: Hellerstein, J.M., Chaudhuri, S., Rosenblum, M. (eds.) Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC 2010, Indianapolis, Indiana, USA, 10–11 June 2010, pp. 143–154. ACM (2010)
Huang, Q., Gudmundsdottir, H., Vigfusson, Y., Freedman, D.A., Birman, K., van Renesse, R.: Characterizing load imbalance in real-world networked caches. In: Katz-Bassett, E., Heidemann, J.S., Godfrey, B., Feldmann, A. (eds.) Proceedings of the 13th ACM Workshop on Hot Topics in Networks, HotNets-XIII, Los Angeles, CA, USA, 27–28 October 2014, pp. 8:1–8:7. ACM (2014)
Paiva, J., Ruivo, P., Romano, P., Rodrigues, L.E.T.: AUTOPLACER: scalable self-tuning data placement in distributed key-value stores. TAAS 9(4), 19:1–19:30 (2014)
Jin, X., et al.: NetCache: balancing key-value stores with fast in-network caching. In: SOSP (2017)
Zhang, W., Wood, T., Hwang, J.: NetKV: scalable, self-managing, load balancing as a network function. In: 2016 IEEE International Conference on Autonomic Computing (ICAC), pp. 5–14, July 2016
Fitzpatrick, B.: Distributed caching with memcached. Linux J. 2004(124), 5 (2004)
Atikoglu, B., Xu, Y., Frachtenberg, E., Jiang, S., Paleczny, M.: Workload analysis of a large-scale key-value store. In: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, ser. SIGMETRICS 2012, pp. 53–64. ACM, New York (2012)
Sevilla, M.A., et al.: Mantle: a programmable metadata load balancer for the ceph file system. In: Kern, J., Vetter, J.S. (eds.) Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2015, Austin, TX, USA, 15–20 November 2015, pp. 21:1–21:12. ACM (2015)
Huq, S., Shafiq, Z., Ghosh, S., Khakpour, A., Bedi, H.: Distributed load balancing in key-value networked caches. In: Proceedings of IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 583–593, June 2017
Noghabi, S.A., et al.: Ambry: Linkedin’s scalable geo-distributed object store. In: Proceedings of the 2016 International Conference on Management of Data, ser. SIGMOD 2016, pp. 253–265. ACM, New York (2016). https://doi.org/10.1145/2882903.2903738
Jia, Y., Brondino, I., Jiménez-Peris, R., Patiño-Martínez, M., Ma, D.: A multi-resource load balancing algorithm for cloud cache systems. In: Shin, S.Y., Maldonado, J.C. (eds.) Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC 2013, Coimbra, Portugal, 18–22 March 2013, pp. 463–470. ACM (2013)
Novakovic, S., Daglis, A., Bugnion, E., Falsafi, B., Grot, B.: The case for RackOut: scalable data serving using rack-scale systems. In: Aguilera, M.K., Cooper, B., Diao, Y. (eds.) Proceedings of the Seventh ACM Symposium on Cloud Computing, Santa Clara, CA, USA, 5–7 October 2016, pp. 182–195. ACM (2016)
Menon, H., Chandrasekar, K., Kalé, L.V.: POSTER: automated load balancer selection based on application characteristics. In: Sarkar, V., Rauchwerger, L. (eds.) Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Austin, TX, USA, 4–8 February 2017, pp. 447–448. ACM (2017). http://dl.acm.org/citation.cfm?id=3019033
Saino, L., Psaras, I., Pavlou, G.: Understanding sharded caching systems. In: Proceedings of IEEE INFOCOM 2016 - the 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, April 2016
Acknowledgment
The authors would like to thank the anonymous reviewers for their valuable comments. This work is partially sponsored by the National Basic Research 973 Program of China (No. 2015CB352403), the National Natural Science Foundation of China (NSFC) (No. 61402014, No. 63100240, No. 61572263), the Natural Science Foundation of Jiangsu Province (BK20151511) and Nanjing University of Posts and Telecommunications Scientific Research Fund (NY215115).
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Chen, S., Zhou, X., Zhou, G., Sinnott, R.O. (2018). Distributed Data Load Balancing for Scalable Key-Value Cache Systems. In: Li, C., Wu, J. (eds) Advanced Computer Architecture. ACA 2018. Communications in Computer and Information Science, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-13-2423-9_14
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DOI: https://doi.org/10.1007/978-981-13-2423-9_14
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