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Cluster Computing
Published in: Cluster Computing 3/2015

01-09-2015

Agent-based load balancing in Cloud data centers

Authors: J. Octavio Gutierrez-Garcia, Adrian Ramirez-Nafarrate

Published in: Cluster Computing | Issue 3/2015

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Abstract

Cloud data centers are generally composed of heterogeneous commodity servers hosting multiple virtual machines (VMs) with potentially different specifications and fluctuating resource usages. This may cause a resource usage imbalance within servers that may result in performance degradation and violations to service level agreements. This work proposes a collaborative agent-based problem solving technique capable of balancing workloads across commodity, heterogeneous servers by making use of VM live migration. The agents are endowed with (i) migration heuristics to determine which VMs should be migrated and their destination hosts, (ii) migration policies to decide when VMs should be migrated, (iii) VM acceptance policies to determine which VMs should be hosted, and (iv) front-end load balancing heuristics. The results show that agents, through autonomous and dynamic collaboration, can efficiently balance loads in a distributed manner outperforming centralized approaches with a performance comparable to commercial solutions, namely Red Hat, while migrating fewer VMs.
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Literature
1.
2.
Anderson, P., Bijani, S., Vichos, A.: Multi-agent negotiation of virtual machine migration using the lightweight coordination calculus. In: Jezic, G., Kusek, M., Nguyen, N.-T., Howlett, R.J., Jain, L.C. (eds.) 6th KES International Conference on Agent and Multi-agent Systems: Technologies and Applications. Lecture Notes in Computer Science, vol. 7327, pp. 124–133. Springer, Berlin (2012)CrossRef
3.
Andreolini, M., Casolari, S., Colajanni, M., Messori, M.: Dynamic load management of virtual machines in cloud architectures. In: Avresky, D.R., Diaz, M., Bode, A., Ciciani, B., Dekel, E. (eds.) Cloud Computing. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, vol. 34, pp. 201–214. Springer, Berlin (2010)CrossRef
4.
Bansal, N., Lodi, A., Sviridenko, M.: A tale of two dimensional bin packing. In: Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science, pp. 657–666 (2005)
5.
Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE—a white paper. EXP Search Innov. 3(3), 6–19 (2003)
6.
Bobroff, N., Kochut, A., Beaty K.: Dynamic placement of virtual machines for managing sla violations. In: Proceedings of the 10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 119–128 (2007)
7.
Breitgand, D., Marashini, A., Tordsson, J.: Policy-driven service placement optimization in federated clouds. Technical Report, IBM Research Division (2011)
8.
Buyya, R., Beloglazov, A., Abawajy, J.: Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. In: Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Application, pp. 12–15 (2010)
9.
Canturk, I., Chengwei, W., Bhatt, C., Shanmuganathan, G., Holler, A.M.: Process demand prediction for distributed power and resource management. U.S. Patent Application 13/281,234, 2011
10.
11.
12.
Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I., Warfield, A.: Live migration of virtual machines. In: Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, USENIX Association, vol. 2, pp. 273–286 (2005)
13.
Coffman, E.G., Garey, M.R., Johnson, D.S., Tarjan, R.E.: Performance bounds for level-oriented two-dimensional packing algorithms. SIAM J. Comput. 9(4), 808–826 (1980)MathSciNetCrossRefMATH
14.
15.
Das, B., Bharghavan, V.: Routing in ad-hoc networks using minimum connected dominating sets. In: Proceedings of the IEEE International Conference on Communications, pp. 376–380 (1997)
16.
Gutierrez-Garcia, J.O., Ramirez-Nafarrate, A.: Policy-based agents for virtual machine migration in cloud data centers. In: Proceedings of the IEEE International Conference on Services Computing, pp. 603–610 (2013)
17.
Gutierrez-Garcia, J.O., Sim, K.M.: A family of heuristics for agent-based elastic cloud bag-of-tasks concurrent scheduling. Future Gener. Comput. Sys. 29(7), 1682–1699 (2013)CrossRef
18.
Gutierrez-Garcia, J.O., Sim, K.M.: Agent-based Cloud Service Composition. Appl. Intell. 38(3), 436–464 (2013)CrossRef
19.
Hyser, C., Mckee, B., Gardner, R., Watson, B.J.: Autonomic virtual machine placement in the data center. Hewlett Packard Laboratories, Technical Report HPL-2007-189, 2007–189 (2007)
20.
Ji, M., Waldspurger, C.A., Zedlewski, J.: Method and system for determining a cost-benefit metric for potential virtual machine migrations. U.S. Patent No. 8,095,929, 2012. Washington, DC: U.S. Patent and Trademark Office
21.
Kerr, A., Diamos, G., Yalamanchili, S.: A characterization and analysis of GPGPU kernels. Technical Report GIT-CERCS-09-06, Georgia Institute of Technology (2009)
22.
Kleiminger, W., Kalyvianaki, E., Pietzuch, P.: Balancing load in stream processing with the cloud. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops, pp. 16–21 (2011)
23.
Lang, W., Patel, J.M., Naughton, J.F.: On energy management, load balancing and replication. ACM SIGMOD Rec. 38(4), 35–42 (2010)CrossRef
24.
Lin, C.C., Liu, P., Wu, J.J.: Energy-efficient virtual machine provision algorithms for cloud systems. In: Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing, pp. 81–88 (2011)
25.
Liu, H., Xu, C.Z., Jin, H., Gong, J., Liao, X.: Performance and energy modeling for live migration of virtual machines. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing, pp. 171–182 (2011)
26.
Metri, G., Srinivasaraghavan, S., Shi, W., Brockmeyer, M.: Experimental analysis of application specific energy efficiency of data centers with heterogeneous servers. In: Proceedings of IEEE 5th International Conference on Cloud Computing, pp. 786–793 (2012)
27.
Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting. Wiley, Hoboken (2011)
28.
Patel, P., Bansal, D., Yuan, L., Murthy, A., Greenberg, A., Maltz, D. A., Kern, R., Kumar, H., Zikos, M., Wu, H., Kim, C., Karri, N.: Ananta: Cloud scale load balancing. In: Proceedings of the ACM Conference on SIGCOMM, pp. 207–218 (2013)
29.
30.
31.
32.
33.
Ren, G., Tune, E., Moseley, T., Shi, Y., Rus, S., Hundt, R.: Google-wide profiling: a continuous profiling infrastructure for data centers. IEEE Micro 30(4), 65–79 (2010)CrossRef
34.
Ren, X., Lin, R., Zou, H.: A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast. In: Proceedings of the 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, pp. 220–224 (2011)
35.
36.
Sim, K.M.: Agent-based cloud computing. IEEE T. Serv. Comput. 5(4), 564–577 (2012)CrossRef
37.
Skiena, S.: The Algorithm Design Manual: Text, vol. 1. Springer, Berlin (1998)
38.
39.
Speitkamp, B., Bichler, M.: A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE T. Serv. Comput. 3(4), 266–278 (2010)CrossRef
40.
Tsai, Y.L., Huang, K.C., Chang, H.Y., Ko, J., Wang, E.T., Hsu, C.H.: Scheduling multiple scientific and engineering workflows through task clustering and best-fit allocation. In: Proceedings of the 2012 IEEE Eighth World Congress on Services, pp. 1–8 (2012)
41.
Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review 39(1), 50–55 (2008)CrossRef
42.
43.
44.
Von Laszewski, G., Diaz, J., Wang, F., Fox, G.C.: Comparison of multiple cloud frameworks. In: Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing, pp. 734–741 (2012)
45.
Voorsluys, W., Broberg, J., Venugopal, S., Buyya, R.: Cost of virtual machine live migration in clouds: a performance evaluation. Cloud Computing, pp. 254–265. Springer, Berlin (2009)CrossRef
46.
Wood, T., Shenoy, P., Venkataramani, A., Yousif, M.: Black-box and gray-box strategies for virtual machine migration. In: Proceedings of the Fourth USENIX Symposium on Networked Systems Design & Implementation, pp. 701–708 (2007)
47.
Wu, Y., Yuan, Y., Yang, G., Zheng, W.: Load prediction using hybrid model for computational grid. In: Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, pp. 235–242 (2007)
48.
Xing, L.: A self-management approach to service optimization and system integrity through multi-agent systems. Master’s Thesis, University of Oslo (2008)
49.
Zhang, H., Jiang, F., Yoshihira, K., Chen, H., Saxena, A.: Intelligent workload factoring for a hybrid cloud computing model. In: Proceedings of the 2009 World Conference on Services-I, pp. 701–708 (2009)
Metadata
Title
Agent-based load balancing in Cloud data centers
Authors
J. Octavio Gutierrez-Garcia
Adrian Ramirez-Nafarrate
Publication date
01-09-2015
Publisher
Springer US
Published in
Cluster Computing / Issue 3/2015
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-015-0460-x

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