Adaptive thresholds determination for saving cloud energy using three-way decisions
Determining the two thresholds in three-way decisions is one of hot issues, it needs to be considered in a specific field and targetedly. In the research of cloud computing, on the one hand, it is necessary to pursue service quality and economy, on the other hand, there is a large number of problems...
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| Published in | Cluster computing Vol. 22; no. Suppl 4; pp. 8475 - 8482 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.07.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1386-7857 1573-7543 |
| DOI | 10.1007/s10586-018-1879-7 |
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| Abstract | Determining the two thresholds in three-way decisions is one of hot issues, it needs to be considered in a specific field and targetedly. In the research of cloud computing, on the one hand, it is necessary to pursue service quality and economy, on the other hand, there is a large number of problems such as low utilization of cluster and high energy consumption. In this paper, the adaptive thresholds migration (ATM) algorithm is proposed by introducing three-way decisions into virtual machine migration. First of all, the proposed method evaluate load in cluster according to resource usage, and then dynamically determine two thresholds, thereby the VMs will be divided into three types with the help of ideas of three-way decisions, that is, idle state nodes, the normal nodes and overload nodes. Secondly, for nodes in different regions take different acting. For the normal nodes, we do nothing. For the idle and overloaded nodes in the cluster, ATM determine the virtual machine to be migrated and optimize the choice of target host based on the balance of resource usage and transmission overhead. Experiments based on CloudSim show that the ATM algorithm reduces power consumption by about 20% compared to TCEA and MM. |
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| AbstractList | Determining the two thresholds in three-way decisions is one of hot issues, it needs to be considered in a specific field and targetedly. In the research of cloud computing, on the one hand, it is necessary to pursue service quality and economy, on the other hand, there is a large number of problems such as low utilization of cluster and high energy consumption. In this paper, the adaptive thresholds migration (ATM) algorithm is proposed by introducing three-way decisions into virtual machine migration. First of all, the proposed method evaluate load in cluster according to resource usage, and then dynamically determine two thresholds, thereby the VMs will be divided into three types with the help of ideas of three-way decisions, that is, idle state nodes, the normal nodes and overload nodes. Secondly, for nodes in different regions take different acting. For the normal nodes, we do nothing. For the idle and overloaded nodes in the cluster, ATM determine the virtual machine to be migrated and optimize the choice of target host based on the balance of resource usage and transmission overhead. Experiments based on CloudSim show that the ATM algorithm reduces power consumption by about 20% compared to TCEA and MM. |
| Author | Wu, Junwei Li, Zhicong Jiang, Chunmao |
| Author_xml | – sequence: 1 givenname: Chunmao surname: Jiang fullname: Jiang, Chunmao email: hsdrose@126.com organization: College of Computer Science and Information Engineer, Harbin Normal University – sequence: 2 givenname: Junwei surname: Wu fullname: Wu, Junwei organization: College of Computer Science and Information Engineer, Harbin Normal University – sequence: 3 givenname: Zhicong surname: Li fullname: Li, Zhicong organization: College of Computer Science and Information Engineer, Harbin Normal University |
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| Cites_doi | 10.1109/ClusterW.2012.14 10.1016/j.future.2011.04.017 10.1155/2016/6208358 10.1016/0020-7373(92)90069-W 10.1145/2656204 10.1016/j.sysarc.2013.03.007 10.1109/UCC.2015.97 10.1016/j.ins.2012.10.041 10.1109/CloudCom.2011.25 10.1109/CloudNet.2012.6483650 10.1109/COMST.2015.2481183 10.1007/978-3-319-28034-9_8 10.3233/FI-2011-423 10.1109/IMTC.2006.328685 10.1007/s10586-008-0070-y 10.1007/978-3-319-25754-9_6 10.1007/978-3-642-31900-6_46 10.1109/TPDS.2012.283 10.1109/DSN.2008.4630101 10.1007/978-3-642-32115-3_1 10.1007/s12559-016-9397-5 10.1007/978-1-4757-6217-4_14 10.1007/978-3-642-41299-8_3 |
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| Keywords | Three-way decisions Cloud computing Energy consumption Two thresholds Adaptive |
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| References_xml | – reference: HsuCHSlagterKDChenSCChungYCOptimizing energy consumption with task consolidation in cloudsInf. Sci.20141025845246210.1016/j.ins.2012.10.041 – reference: Xu, Lei, et al.: Smart-DRS: a strategy of dynamic resource scheduling in cloud data center. In: Proceedings of the 2012 IEEE International Conference on Cluster Computing Workshops – reference: XiaoZSongWChenQDynamic resource allocation using virtual machines for cloud computing environmentIEEE Trans. Parallel Distrib. Syst.20132461107111610.1109/TPDS.2012.283 – reference: Deng, X., Yao, Y.: An information-theoretic interpretation of thresholds in probabilistic rough sets. In: RSKT pp. 369–378 (2012) – reference: LuoLWuWJZhangFEnergy modeling based on cloud data centerJ. Softw.201425713711387 – reference: Yao, Y.Y.: An outline of a theory of three-way decisions. In: RSCTC 2012, LNCS (LNAI) 7413, Springer. pp. 1-17 (2012) – reference: GaoYQuality of service aware power management for virtualized data centersJ. Syst. Archit.201359424525910.1016/j.sysarc.2013.03.007 – reference: BeloglazovAAbawajyJBuyyaREnergy-aware resource allocation heuristics for efficient management of data centers for cloud computingFuture Gener. Comput. Syst.201228575576810.1016/j.future.2011.04.017 – reference: Gmach, D., Rolia, J., Cherkasova, L.: An integrated approach to resource pool management: Policies, efficiency and quality metrics. International Conference on IEEE Dependable Systems and Networks With FTCS and DCC 2008, 326–335 (2008) – reference: Panda, S.K., Jana, P.K.: An efficient task consolidation algorithm for cloud computing systems. In: International Conference on Distributed Computing and Internet Technology, 15 Jan 2016 (pp. 61–74) – reference: YaoJHerbertJPA game-theoretic perspective on rough set analysisJ. Chongqing Univ. Posts Telecommun.2008203291298(Natural Science Edition) – reference: YaoYYWongSKA decision theoretic framework for approximating conceptsInt. J. Man-Mach. Stud.199237679380910.1016/0020-7373(92)90069-W – reference: YaoYThree-way decisions and cognitive computingCogn. Comput.20168454355410.1007/s12559-016-9397-5 – reference: MastelicTOleksiakAClaussenHBrandicIPiersonJMVasilakosAVCloud computing: survey on energy efficiencyAcm Comput. Surv. (csur)201547233 – reference: KusicDKephartJOHansonJEKandasamyNJiangGPower and performance management of virtualized computing environments via lookahead controlClust. Comput.20091211510.1007/s10586-008-0070-y – reference: HerbertJPYaoJGame-theoretic rough setsFundam. Inf.20111083–4267862816400 – reference: Choi, H.S., Lim, J.B., Yu, H., et al.: Task classification based energy-aware consolidation in clouds. Sci. Programm. (2016) https://doi.org/10.1155/2016/6208358 – reference: Tsai, L., Liao, W.: Cost-aware workload consolidation in green cloud datacenter. In: 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET), 28 Nov 2012 (pp. 29–34) – reference: Yao, Y.: Granular computing and sequential three-way decisions. In: International Conference on Rough Sets and Knowledge Technology, Springer, Berlin, pp. 16–27 (2013) – reference: Bohrer, P., Elnozahy, E.N., Keller, T., Kistler, M., Lefurgy, C., McDowell, C., Rajamony, R.: The case for power management in web servers. In: Power Aware Computing, Springer, Boston, pp. 261–289 (2002) – reference: Calheiros, R.N., Ranjan, R., De Rose, C.A., Buyya, R.: Cloudsim: A novel framework for modeling and simulation of cloud computing infrastructures and services. (2009). arXiv:0903.2525 – reference: Lien, C. H. et al.: Measurement by the software design for the power consumption of streaming media servers. In: Instrumentation and Measurement Technology Conference (2006) – reference: DayarathnaMWenYFanRData center energy consumption modeling: a surveyIEEE Commun. Surv. Tutor.201618173279410.1109/COMST.2015.2481183 – reference: Hsu, C.H., Chen, S.C., Lee, C.C., Chang, H.Y., Lai, K.C., Li, K.C., Rong, C.: Energy-aware task consolidation technique for cloud computing. In: 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science (CloudCom), 29 Nov 2011 (pp. 115–121) – reference: Aldulaimy, A., Zantout, R., Zekri, A., Itani, W.: Job classification in cloud computing: the classification effects on energy efficiency. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), 7 Dec 2015 (pp. 547–552) – reference: Yao, Y.Y.: Rough sets and three-way decisions. 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| SubjectTerms | Algorithms Cloud computing Clusters Computer Communication Networks Computer Science Decisions Energy consumption Information theory Nodes Operating Systems Overloading Power consumption Processor Architectures Regression analysis Thresholds Virtual environments |
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| Title | Adaptive thresholds determination for saving cloud energy using three-way decisions |
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