Abstract
The geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method.
This work is funded by the European Commission under FP7 Grant 608826 (GENiC - Globally Optimised Energy Efficient Data Centres).
This work is funded by Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Cost functions can be implemented using element constraints [31].
- 2.
A complete solution here is a complete assignments of all agents’ external variables.
- 3.
Only external variables linked to unassigned neighbors are needed.
- 4.
References
America’s Data Centres Consuming and Wasting Growing Amounts of Energy (2015). https://www.nrdc.org/resources/americas-data-centers-consuming-and-wasting-growing-amounts-energy
Data centres to consume three times as much energy in next decade, experts warn (2016). http://www.independent.co.uk/environment/global-warming-data-centres-to-consume-three-times-as-much-energy-in-next-decade-experts-warn-a6830086.html
Armstrong, A.A., Durfee, E.H.: Dynamic prioritization of complex agents in distributed constraint satisfaction problems. In: Proceedings of AAAI 1997/IAAI 1997, pp. 822–822 (1997)
Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 826–831. IEEE Computer Society (2010)
Bessiere, C., Brito, I., Gutierrez, P., Meseguer, P.: Global constraints in distributed constraint satisfaction and optimization. Comput. J. 57(6), 906–923 (2013)
Bessiere, C., Maestre, A., Brito, I., Meseguer, P.: Asynchronous backtracking without adding links: a new member in the ABT family. Artif. Intell. 161, 7–24 (2005)
Bonnet-Torrés, O., Tessier, C.: Multiply-constrained DCOP for distributed planning and scheduling. In: AAAI Spring Symposium: Distributed Plan and Schedule Management, pp. 17–24 (2006)
Brito, I., Meisels, A., Meseguer, P., Zivan, R.: Distributed constraint satisfaction with partially known constraints. Constraints 14, 199–234 (2009)
Burke, D.A., Brown, K.N.: Efficient handling of complex local problems in distributed constraint optimization. In: Proceedings of ECAI 2006, Riva del Garda, Italy, pp. 701–702 (2006)
Chechetka, A., Sycara, K.: No-commitment branch and bound search for distributed constraint optimization. In: Proceedings of AAMAS 2006, pp. 1427–1429 (2006)
Faltings, B., Yokoo, M.: Editorial: introduction: special issue on distributed constraint satisfaction. Artif. Intell. 161(1–2), 1–5 (2005)
Gershman, A., Meisels, A., Zivan, R.: Asynchronous forward-bounding for distributed constraints optimization. In: Proceedings of ECAI 2006, pp. 103–107 (2006)
Gershman, A., Meisels, A., Zivan, R.: Asynchronous forward-bounding for distributed COPs. JAIR 34, 61–88 (2009)
Haralick, R.M., Elliott, G.L.: Increasing tree search efficiency for constraint satisfaction problems. Artif. Intell. 14(3), 263–313 (1980)
Hirayama, K., Yokoo, M.: Distributed partial constraint satisfaction problem. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 222–236. Springer, Heidelberg (1997). doi:10.1007/BFb0017442
Hirayama, K., Yokoo, M.: The distributed breakout algorithms. Artif. Intell. 161, 89–116 (2005)
Léauté, T., Faltings, B.: Coordinating logistics operations with privacy guarantees. In: Proceedings of the IJCAI 2011, pp. 2482–2487 (2011)
Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann Series (1997)
Maestre, A., Bessiere, C.: Improving asynchronous backtracking for dealing with complex local problems. In: Proceedings of ECAI 2004, pp. 206–210 (2004)
Maheswaran, R.T., Tambe, M., Bowring, E., Pearce, J.P., Varakantham, P.: Taking DCOP to the real world: efficient complete solutions for distributed multi-event scheduling. In: Proceedings of AAMAS 2004, Washington, DC, USA, pp. 310–317. IEEE Computer Society (2004)
Meisels, A., Zivan, R.: Asynchronous forward-checking for DisCSPs. Constraints 12(1), 131–150 (2007)
Miller, S., Ramchurn, S.D., Rogers, A.: Optimal decentralised dispatch of embedded generation in the smart grid. In: Proceedings of AAMAS 2012, pp. 281–288. International Foundation for Autonomous Agents and Multiagent Systems (2012)
Modi, P.J., Shen, W.-M., Tambe, M., Yokoo, M.: ADOPT: asynchronous distributed constraint optimization with quality guarantees. Artif. Intell. 161, 149–180 (2005)
Petcu, A., Faltings, B.: A value ordering heuristic for local search in distributed resource allocation. In: Faltings, B.V., Petcu, A., Fages, F., Rossi, F. (eds.) CSCLP 2004. LNCS, vol. 3419, pp. 86–97. Springer, Heidelberg (2005). doi:10.1007/11402763_7
Petcu, A., Boi Faltings, D.: A scalable method for multiagent constraint optimization. In: Proceedings of IJCAI 2005, pp. 266–271 (2005)
Prud’homme, C., Fages, J.-G., Lorca, X.: Choco Documentation. TASC, INRIA Rennes, LINA CNRS UMR 6241, COSLING S.A.S. (2016)
Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., Maggs, B.: Cutting the electric bill for internet-scale systems. In: ACM SIGCOMM Computer Communication Review, vol. 39, pp. 123–134. ACM (2009)
Rahman, A., Liu, X., Kong, F.: A survey on geographic load balancing based data center power management in the smart grid environment. IEEE Commun. Surv. Tutorials 16(1), 214–233 (2014)
Rao, L., Liu, X., Xie, L., Liu, W.: Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In: INFOCOM, 2010 Proceedings IEEE, pp. 1–9. IEEE (2010)
Régin, J.-C.: A filtering algorithm for constraints of difference in CSPs. In: Proceedings of AAAI 1994, pp. 362–367 (1994)
Van Hentenryck, P., Deville, Y., Teng, C.-M.: A generic arc-consistency algorithm and its specializations. Artif. Intell. 57(2–3), 291–321 (1992)
Wahbi, M., Brown, K.N.: Global constraints in distributed CSP: concurrent GAC and explanations in ABT. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 721–737. Springer, Cham (2014). doi:10.1007/978-3-319-10428-7_52
Wahbi, M., Ezzahir, R., Bessiere, C., Bouyakhf, E.H.: DisChoco 2: a platform for distributed constraint reasoning. In: Proceedings of Workshop on DCR 2011, pp. 112–121 (2011)
Wahbi, M., Ezzahir, R., Bessiere, C., Bouyakhf, E.H.: Nogood-based asynchronous forward-checking algorithms. Constraints 18(3), 404–433 (2013)
Wallace, R.J., Freuder, E.C.: Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving. Artif. Intell. 161, 209–228 (2005)
Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm. J. Artif. Intell. Res. (JAIR) 38, 85–133 (2010)
Yokoo, M.: Distributed Constraint Satisfaction: Foundation of Cooperation in Multi-agent Systems. Springer, Berlin (2001)
Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: Distributed constraint satisfaction for formalizing distributed problem solving. In: Proceedings of 12th IEEE International Conference on Distributed Computing Systems, pp. 614–621 (1992)
Yokoo, M., Hirayama, K.: Distributed constraint satisfaction algorithm for complex local problems. In: International Conference on Multi Agent Systems, pp. 372–379 (1998)
Zhang, W., Wang, G., Xing, Z., Wittenburg, L.: Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks. Artif. Intell. 161, 55–87 (2005)
Zivan, R., Meisels, A.: Synchronous vs Asynchronous search on DisCSPs. In: Proceedings of EUMAS 2003 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wahbi, M., Grimes, D., Mehta, D., Brown, K.N., O’Sullivan, B. (2017). A Distributed Optimization Method for the Geographically Distributed Data Centres Problem. In: Salvagnin, D., Lombardi, M. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2017. Lecture Notes in Computer Science(), vol 10335. Springer, Cham. https://doi.org/10.1007/978-3-319-59776-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-59776-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59775-1
Online ISBN: 978-3-319-59776-8
eBook Packages: Computer ScienceComputer Science (R0)