於離峰期間合併運行虛擬機器能有效的增進資料中心的資源使用效率，但是在伺服器間移動虛擬機器時，常常會造成整體運算效率的下降與系統不穩定等問題。許多演算法致力於優化虛擬機器搬遷所造成的效能消耗，而我們這篇論文所設計的演算法透過預先的虛擬機器合併策略，以及運行時的虛擬機器擺放措施來主動減少虛擬機器的搬遷。一方面透過流量的預測來最佳化系統的資源使用效率；另一方面，基於伺服器的運作時間以及工作的執行時間來控制並降低虛擬機器在伺服器間傳送的頻率，並同時減少伺服器間的資料傳輸量。在實際的高效運算測資以及模擬測資實驗中，我們的演算法能在相同的效能要求下減少約37%~46%的虛擬機器搬遷數量，5%~25%的資料傳輸量以及21%~41%的能量消耗。

VM consolidation has been shown as a promising technique for saving energy costs and improving resource utilization of a data center. It relies on VM migration to move user tasks onto fewer numbers of physical servers during o peak hour, and then shutdown those idle servers. However, VM migration is a costly operation that could cause several concerns, such as performance degradation, system instability,
etc. Hence many existing algorithms were proposed to minimize the migration cost at runtime after consolidation is triggered by SLA violation or resource utilization
condition. In contrast, this paper aims to pro-actively prevent VM migration in-advanced through a combination of semi-static VM consolidation strategy and runtime VM placement strategy. On one hand, our VM consolidation strategy aims to minimize SLA violation and maximize resource utilization according to the periodic workload pattern. On the other hand, our VM placement strategy attempts to minimize VM migration and communication cost based on the knowledge server turn-on/o time and task execution time. We evaluate our approach using a real
HPC cluster trace as well as a set of synthetic generated workloads. The results show that our VM management approach can signicantly reduce the number of migrations by 37~46% and communication cost by 5~25% while reducing the
energy cost by 21~41% without causing SLA violation.

1] S. Akoush, R. Sohan, A. Rice, A. Moore, and A. Hopper. Predicting the
performance of virtual machine migration. pages 37{46, 2010.
[2] K. Andreev and H. Racke. Balanced graph partitioning. In Proceedings of the
Sixteenth Annual ACM Symposium on Parallelism in Algorithms and Architec-
tures, SPAA '04, pages 120{124, 2004.
[3] G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao. Energyaware
server provisioning and load dispatching for connection-intensive internet
services. In ACM/USENIX NSDI, pages 337{350, 2008.
[4] M. Chen, H. Zhang, Y.-Y. Su, X. Wang, G. Jiang, and K. Yoshihira. Eective
VM sizing in virtualized data centers. In Proceedings of the 12th IFIP/IEEE
International Symposium on Integrated Network Management, pages 594{601,
May 2011.
[5] S. Choudhury, D. Gaur, and R. Krishnamurti. An approximation algorithm
for max k-uncut with capacity constraints. In Computational Sciences and
Optimization, 2009. CSO 2009. International Joint Conference on, volume 2,
pages 934{938, April 2009.
[6] P. Christian L. Belady. In the data center, power and cooling costs more than
the it equipment it supports. Electronics Cooling Magazine, 2007.
[7] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and
A. Wareld. Live migration of virtual machines. In ACM/USENIX NSDI,
pages 273{286, 2005.
[8] U. Feige and R. Krauthgamer. A polylogarithmic approximation of the minimum
bisection. SIAM J. Comput., 31(4):1090{1118, Apr. 2002.
[9] M. R. Garey, D. S. Johnson, and L. Stockmeyer. Some simplied np-complete
problems. In Proceedings of the Sixth Annual ACM Symposium on Theory of
Computing, STOC '74, pages 47{63, New York, NY, USA, 1974. ACM.
[10] M. Hossain, J.-C. Huang, and H.-H. S. Lee. Migration energy-aware workload
consolidation in enterprise clouds. pages 405{410, 2012.
[11] N. K. James M. Kaplan, William Forrest. Revolutionizing data center energy
eciency. Technical report, McKinsey&Company, 2008.
[12] D. Jayasinghe, C. Pu, T. Eilam, M. Steinder, and I. Whalley. Improving
performance and availability of services hosted on iaas clouds with structural
constraint-aware virtual machine placement. In in IEEE SCC, 2011.
[13] J. G. Koomey. Worldwide electricity used in data centers. Environmental
Research Letters, 2008.
[14] D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang. Power and
performance management of virtualized computing environments via lookahead
control. In IEEE International Conference on Autonomic Computing, pages 3{
12, 2008.
[15] C.-H. Lee, D. Lee, and M. Kim. Optimal task assignment in linear array
networks. IEEE Trans. Comput., 41(7):877{880, Jul. 1992.
[16] C.-H. Lee and K. G. Shin. Optimal task assignment in homogeneous networks.
IEEE Trans. Parallel Distrib. Syst., 8(2):119{129, Feb. 1997.
[17] M. Y. Lim, F. Rawson, T. Bletsch, and V. W. Freeh. PADD: Power Aware
Domain Distribution. In International Conference on Distributed Computing
Systems, pages 239{247, 2009.
[18] H. Liu, C.-Z. Xu, H. Jin, J. Gong, and X. Liao. Performance and energy
modeling for live migration of virtual machines. pages 171{182, 2011.
[19] V. M. Lo. Heuristic algorithms for task assignment in distributed systems.
IEEE Trans. Comput., 37(11):1384{1397, Nov. 1988.
[20] U. Lublin and D. G. Feitelson. The workload on parallel supercomputers: modeling
the characteristics of rigid jobs. 63(11):1105{1122, Nov. 2003.
[21] M. Nelson, B.-H. Lim, and G. Hutchins. Fast transparent migration for virtual
machines. pages 391{394, 2005.
[22] The Potsdam Institute for Climate Impact Research (PIK) IBM iDataPlex
Cluster log.
[23] C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M. A. Kozuch. Towards
understanding heterogeneous clouds at scale: Google trace analysis. Technical
report, Intel science and technology center for cloud computing, Carnegie
Mellon University, Apr. 2012.
[24] H. Saran and V. V. Vazirani. Finding k-cuts within twice the optimal. In Pro-
ceedings of the 32Nd Annual Symposium on Foundations of Computer Science,
SFCS '91, pages 743{751, 1991.
[25] S. Srikantaiah, A. Kansal, and F. Zhao. Energy aware consolidation for cloud
computing. In Proceedings of the USENIX International Conference on Hot-
Power, 2008.
[26] M. Tarighi, S. A. Motamedi, and S. Sharian. A new model for virtual machine
migration in virtualized cluster server based on fuzzy decision making. CoRR,
2010.
[27] A. N. Toosi, R. N. Calheiros, R. K. Thulasiram, and R. Buyya. Resource
provisioning policies to increase iaas provider's prot in a federated cloud environment.
pages 279{287, 2011.
[28] N. M. Tran and L. Wolters. Towards a profound analysis of bags-of-tasks in
parallel systems and their performance impact. pages 111{122, 2011.
[29] N. Tziritas, S. U. Khan, C.-Z. Xu, T. Loukopoulos, and S. Lalis. On minimizing
the resource consumption of cloud applications using process migrations. J.
Parallel Distrib. Comput., 73(12):1690{1704, Dec. 2013.
[30] N. Tziritas, S. U. Khan, C.-Z. Xu, T. Loukopoulos, and S. Lalis. On minimizing
the resource consumption of cloud applications using process migrations. 2013.
[31] N. Tziritas, C.-Z. Xu, T. Loukopoulos, S. U. Khan, and Z. Yu. Applicationaware
workload consolidation to minimize both energy consumption and network
load in cloud environments. 2013.
[32] N. Tziritas, C.-Z. Xu, T. Loukopoulos, S. U. Khan, and Z. Yu. Applicationaware
workload consolidation to minimize both energy consumption and network
load in cloud environments. In Proceedings of the 2013 42Nd International
Conference on Parallel Processing, ICPP '13, pages 449{457, 2013.
[33] G. Utrera, S. Tabik, J. Corbalan, and J. Labarta. A job scheduling approach for
multi-core clusters based on virtual malleability. In Euro-Par, pages 191{203,
2012.
[34] A. Verma, P. Ahuja, and A. Neogi. pmapper: power and migration cost aware
application placement in virtualized systems. In Proceedings of the USENIX
International Conference on Middleware, pages 243{264, 2008.
[35] A. Verma, P. Ahuja, and A. Neogi. Power-aware dynamic placement of HPC
applications. pages 175{184, 2008.
[36] A. Verma, G. Dasgupta, T. K. Nayak, P. De, and R. Kothari. Server workload
analysis for power minimization using consolidation. pages 28{28, 2009.
[37] W. Voorsluys, J. Broberg, S. Venugopal, and R. Buyya. Cost of virtual machine
live migration in clouds: A performance evaluation. pages 254{265, 2009.
[38] Y. Ye and J. Zhang. Approximation of dense-n/2-subgraph and the complement
of min-bisection. J. of Global Optimization, 25(1):55{73, Jan. 2003.
[39] Q. Zhang, M. F. Zhani, S. Zhang, Q. Zhu, R. Boutaba, and J. L. Hellerstein. Dynamic
energy-aware capacity provisioning for cloud computing environments.
In IEEE International Conference on Autonomic Computing, pages 145{154,
2012.