Application of virtual machine consolidation in cloud computing systems
Tài liệu tham khảo
Xie, 2018, A novel self-adaptive VM consolidation strategy using dynamic multi-thresholds in IaaS clouds, Future Internet, 10, 52, 10.3390/fi10060052
Zhu, 2012, Special section: green computing, Future Gener. Comput. Syst., 28, 368, 10.1016/j.future.2011.06.011
Qiu, 2019, Energy aware virtual machine scheduling in data centers, Energies, 12, 646, 10.3390/en12040646
Naeen, 2020, A stochastic process-based server consolidation approach for dynamic workloads in cloud data centers, J. Supercomput., 76, 1903, 10.1007/s11227-018-2431-5
Pahlavan, 2014, Power reduction in HPC data centers: a joint server placement and chassis consolidation approach, J. Supercomput., 70, 845, 10.1007/s11227-014-1265-z
Gilesh, 2020, Opportunistic live migration of virtual machines, Concurr. Comput. Pract. Exp., 32, e5477, 10.1002/cpe.5477
Gill, 2020, ThermoSim: deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments, J. Syst. Softw., 110596, 10.1016/j.jss.2020.110596
Khan, 2018, Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers
Casalicchio, 2017, Energy-aware auto-scaling algorithms for Cassandra virtual data centers, Cluster Comput., 20, 2065, 10.1007/s10586-017-0912-6
Witanto, 2018, Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management, Future Gener. Comput. Syst., 87, 35, 10.1016/j.future.2018.04.075
Agency, 2013
Fard, 2017, A dynamic VM consolidation technique for QoS and energy consumption in cloud environment, J. Supercomput., 73, 4347, 10.1007/s11227-017-2016-8
Asad, 2016, A two-way street: green big data processing for a greener smart grid, IEEE Syst. J., 11, 784, 10.1109/JSYST.2015.2498639
Delforge, 2014, 1
Mishra, 2018, Energy-efficient VM-placement in cloud data center, Sustain. Comput. Inform. Syst., 20, 48
Ahmad, 2015, A survey on virtual machine migration and server consolidation frameworks for cloud data centers, J. Netw. Comput. Appl., 52, 11, 10.1016/j.jnca.2015.02.002
Shehabi, 2016
Barroso, 2013, The datacenter as a computer: an introduction to the design of warehouse-scale machines, Synth. Lect. Comput. Archit., 8, 1
Halder, 2012, Risk aware provisioning and resource aggregation based consolidation of virtual machines
Pahlavan, 2012, Data center power reduction by heuristic variation-aware server placement and chassis consolidation
Khalaj, 2017, A review on efficient thermal management of air-and liquid-cooled data centers: from chip to the cooling system, Appl. Energy, 205, 1165, 10.1016/j.apenergy.2017.08.037
Azizi, 2020, GRVMP: a greedy randomized algorithm for virtual machine placement in cloud data centers, IEEE Syst. J.
Xiao, 2018, Maximizing reliability of energy constrained parallel applications on heterogeneous distributed systems, J. Comput. Sci., 26, 344, 10.1016/j.jocs.2017.05.002
Rezaei-Mayahi, 2019, Temperature-aware power consumption modeling in Hyperscale cloud data centers, Future Gener. Comput. Syst., 94, 130, 10.1016/j.future.2018.11.029
Guenter, 2011, Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning
Qian, 2011, Server operational cost optimization for cloud computing service providers over a time horizon, Hot-ICE
El-Sayed, 2012, Temperature management in data centers: why some (might) like it hot, Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, 10.1145/2254756.2254778
Bodík, 2012, Surviving failures in bandwidth-constrained datacenters, ACM SIGCOMM Comput. Commun. Rev., 42, 431, 10.1145/2377677.2377760
Zhang, 2015, Virtual machines consolidation and placement based in constraint satisfaction in the clouds, J. Comput. Inf. Syst., 11, 5251
Malekloo, 2018, An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments, Sustain. Comput. Inform. Syst., 17, 9
Marotta, 2018, A joint power efficient server and network consolidation approach for virtualized data centers, Comput. Netw., 130, 65, 10.1016/j.comnet.2017.11.003
Cao, 2020, Towards tenant demand-aware bandwidth allocation strategy in cloud datacenter, Future Gener. Comput. Syst., 105, 904, 10.1016/j.future.2017.06.005
Xu, 2019, Migration cost and energy-aware virtual machine consolidation under cloud environments considering remaining runtime, Int. J. Parallel Program., 47, 481, 10.1007/s10766-018-00622-x
Zhou, 2018, DADTA: a novel adaptive strategy for energy and performance efficient virtual machine consolidation, J. Parallel Distrib. Comput., 121, 15, 10.1016/j.jpdc.2018.06.011
Sayadnavard, 2019, A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers, J. Supercomput., 75, 2126, 10.1007/s11227-018-2709-7
Sharma, 2016
Kim, 2015, A parallel migration scheme for fast virtual machine relocation on a cloud cluster, J. Supercomput., 71, 4623, 10.1007/s11227-015-1563-0
Aryania, 2018, Energy-aware virtual machine consolidation algorithm based on ant colony system, J. Grid Comput., 16, 477, 10.1007/s10723-018-9428-4
Ilager, 2019, ETAS: energy and thermal‐aware dynamic virtual machine consolidation in cloud data center with proactive hotspot mitigation, Concurr. Comput.: Pract. Exp., 31, e5221, 10.1002/cpe.5221
Kumar, 2016, Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds, J. Comput. Syst. Sci., 82, 191, 10.1016/j.jcss.2015.07.005
Tighe, 2017, Topology and application aware dynamic vm management in the cloud, J. Grid Comput., 15, 273, 10.1007/s10723-017-9397-z
Rahman, 2017, Compatibility-based static VM placement minimizing interference, J. Netw. Comput. Appl., 84, 68, 10.1016/j.jnca.2017.02.004
Khelghatdoust, 2016, GLAP: distributed dynamic workload consolidation through gossip-based learning
Alicherry, 2012, Network aware resource allocation in distributed clouds
Velliangiri, 2020, Hybrid electro search with genetic algorithm for task scheduling in cloud computing, Ain Shams Eng. J.
Thiam, 2019, Energy efficient cloud data center using dynamic virtual machine consolidation algorithm
Masoumzadeh, 2015, A cooperative multi agent learning approach to manage physical host nodes for dynamic consolidation of virtual machines
Shu, 2020, Nash equilibrium based replacement of virtual machines for efficient utilization of cloud data centers, Computing, 1
Tchana, 2016, Software consolidation as an efficient energy and cost saving solution, Future Gener. Comput. Syst., 58, 1, 10.1016/j.future.2015.11.027
Lee, 2011, Validating heuristics for virtual machines consolidation, Microsoft Res., 1
Wang, 2019, Bio-inspired heuristics for vm consolidation in cloud data centers, IEEE Syst. J., 14, 152, 10.1109/JSYST.2019.2900671
Beloglazov, 2012, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing, Future Gener. Comput. Syst., 28, 755, 10.1016/j.future.2011.04.017
Sonklin, 2017, New decrease-and-conquer strategies for the dynamic genetic algorithm for server consolidation
Ferdaus, 2014, Virtual machine consolidation in cloud data centers using ACO metaheuristic
Al-Moalmi, 2019, Optimal virtual machine placement based on grey wolf optimization, Electronics, 8, 283, 10.3390/electronics8030283
Li, 2016, Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing, Computing, 98, 303, 10.1007/s00607-015-0467-4
Yavari, 2019, Temperature and energy-aware consolidation algorithms in cloud computing, J. Cloud Comput., 8, 1, 10.1186/s13677-019-0136-9
Fatima, 2018, Virtual machine placement via bin packing in cloud data centers, Electronics, 7, 389, 10.3390/electronics7120389
Moges, 2019, Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework, J. Cloud Comput., 8, 2, 10.1186/s13677-019-0126-y
Kakadia, 2013, Network-aware virtual machine consolidation for large data centers, Proceedings of the Third International Workshop on Network-Aware Data Management, 10.1145/2534695.2534702
Zhang, 2019, Energy-aware virtual machine allocation for cloud with resource reservation, J. Syst. Softw., 147, 147, 10.1016/j.jss.2018.09.084
Rajabzadeh, 2020, New comprehensive model based on virtual clusters and absorbing Markov chains for energy-efficient virtual machine management in cloud computing, J. Supercomput., 1
Jangiti, 2020, EMC2: energy-efficient and multi-resource-fairness virtual machine consolidation in cloud data centres, Sustain. Comput. Inform. Syst., 27, 100414
Farahnakian, 2015, Utilization prediction aware VM consolidation approach for green cloud computing
Rai, 2017, Effect of VM selection heuristics on energy consumption and SLAs during VM migrations in cloud data centers, 189
Li, 2017, Bayesian network-based virtual machines consolidation method, Future Gener. Comput. Syst., 69, 75, 10.1016/j.future.2016.12.008
Mazumdar, 2017, Power efficient server consolidation for cloud data center, Future Gener. Comput. Syst., 70, 4, 10.1016/j.future.2016.12.022
Xiao, 2019, Multi-objective vm consolidation based on thresholds and ant colony system in cloud computing, IEEE Access, 7, 53441, 10.1109/ACCESS.2019.2912722
Deng, 2014, Reliability‐aware server consolidation for balancing energy‐lifetime tradeoff in virtualized cloud datacenters, Int. J. Commun. Syst., 27, 623, 10.1002/dac.2687
Sheikh, 2012, An overview and classification of thermal-aware scheduling techniques for multi-core processing systems, Sustain. Comput. Inform. Syst., 2, 151
Sheikh, 2015, An evolutionary technique for performance-energy-temperature optimized scheduling of parallel tasks on multi-core processors, IEEE Trans. Parallel Distrib. Syst., 27, 668, 10.1109/TPDS.2015.2421352
Khalid, 2019, An evolutionary approach to optimize data center profit in smart grid environment
Gu, 2015, Joint optimization of VM placement and request distribution for electricity cost cut in geo-distributed data centers
Sedaghat, 2016, Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior, Future Gener. Comput. Syst., 56, 51, 10.1016/j.future.2015.09.023
Theja, 2016, Evolutionary computing based on QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers, Cybern. Inf. Technol., 16, 97
Chang, 2018, Optimizing energy consumption for a performance-aware cloud data center in the public sector, Sustain. Comput. Inform. Syst., 20, 34
Khalil, 2020, Energy cost minimization for sustainable cloud computing using option pricing, Sustain. Cities Soc., 63, 102440, 10.1016/j.scs.2020.102440
Terra-Neves, 2019, Virtual machine consolidation using constraint-based multi-objective optimization, J. Heuristics, 25, 339, 10.1007/s10732-018-9400-2
Sheikh, 2012, Energy-and performance-aware scheduling of tasks on parallel and distributed systems, ACM J. Emerg. Technol. Comput. Syst. (JETC), 8, 1, 10.1145/2367736.2367743
Rahmani, 2020, Burstiness-aware virtual machine placement in cloud computing systems, J. Supercomput., 76, 362, 10.1007/s11227-019-03037-8
Gholipour, 2020, A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers, Simul. Model. Pract. Theory, 102127, 10.1016/j.simpat.2020.102127
Pradhan, 2020, A novel load balancing technique for cloud computing platform based on PSO, J. King Saud Univ.-Comput. Inform. Sci.
Kapil, 2013, Live virtual machine migration techniques: survey and research challenges
Varasteh, 2015, Server consolidation techniques in virtualized data centers: a survey, IEEE Syst. J., 11, 772, 10.1109/JSYST.2015.2458273
Khan, 2019, Energy-aware dynamic resource management in elastic cloud datacenters, Simul. Model. Pract. Theory, 92, 82, 10.1016/j.simpat.2018.12.001
Mann, 2015, Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms, Acm Comput. Surv. (CSUR), 48, 1, 10.1145/2797211
Ahmad, 2015, Virtual machine migration in cloud data centers: a review, taxonomy, and open research issues, J. Supercomput., 71, 2473, 10.1007/s11227-015-1400-5
Yao, 2014, Guaranteeing fault-tolerant requirement load balancing scheme based on VM migration, Comput. J., 57, 225, 10.1093/comjnl/bxt012
Zolfaghari, 2020, Virtual machine consolidation in cloud computing systems: challenges and future trends, Wirel. Pers. Commun., 115, 2289, 10.1007/s11277-020-07682-8
Hu, 2013, HMDC: live virtual machine migration based on hybrid memory copy and delta compression, Appl. Math., 7, 639
Shukla, 2019, A multiphase pre-copy strategy for the virtual machine migration in cloud, 437
Abe, 2016, Urgent virtual machine eviction with enlightened post-copy, ACM Sigplan Not., 51, 51, 10.1145/3007611.2892252
Nayak, 2018, A research paper of existing live VM migration and a hybrid VM migration approach in cloud computing
Yin, 2014, Live virtual machine migration with optimized three-stage memory copy, 69
Shribman, 2012, Pre-copy and post-copy vm live migration for memory intensive applications
Zhu, 2013, ITC-LM: a smart iteration-termination criterion based live virtual machine migration
Aikema, 2012, Green cloud VM migration: Power use analysis
Zolfaghari, 2013, Converting UML description of software architecture to QNM and performance evaluation, Int. J. Soft Comput. Eng. (IJSCE), 3, 281
Zolfaghari, 2013, Converting UML description of software architecture to stochastic process algebra and performance evaluation, Int. J. Adv. Comput. Technol. (IJACT), 2, 92
Song, 2020, Testing and evaluation system for cloud computing information security products, Procedia Comput. Sci., 166, 84, 10.1016/j.procs.2020.02.023
Jarraya, 2015, Verification of firewall reconfiguration for virtual machines migrations in the cloud, Comput. Netw., 93, 480, 10.1016/j.comnet.2015.10.008
Ficco, 2015, Modeling security requirements for cloud‐based system development, Concurr. Comput. Pract. Exp., 27, 2107, 10.1002/cpe.3402
Sandıkkaya, 2016, Design and formal verification of a cloud compliant secure logging mechanism, IET Inf. Secur., 10, 203, 10.1049/iet-ifs.2014.0625
Abid, 2016, Formal design of dynamic reconfiguration protocol for cloud applications, Sci. Comput. Program., 117, 1, 10.1016/j.scico.2015.12.001
Amoretti, 2015, A formalized framework for mobile cloud computing, Serv. Oriented Comput. Appl., 9, 229, 10.1007/s11761-014-0169-3
Abdelfattah, 2020, RAMWS: Reliable approach using middleware and WebSockets in mobile cloud computing, Ain Shams Eng. J., 10.1016/j.asej.2020.04.002
De, 2016, Modeling decoupled mobile cloud computing using Mobile UNITY, Concurr. Comput. Pract. Exp., 28, 2811, 10.1002/cpe.3300
Salaün, 2013, An experience report on the verification of autonomic protocols in the cloud, Innov. Syst. Softw. Eng., 9, 105, 10.1007/s11334-013-0204-0
Deng, 2015, An integrated framework of formal methods for interaction behaviors among industrial equipments, Microprocess. Microsyst., 39, 1296, 10.1016/j.micpro.2015.07.015
Rezaee, 2014, Formal process algebraic modeling, verification, and analysis of an abstract Fuzzy Inference Cloud Service, J. Supercomput., 67, 345, 10.1007/s11227-013-1005-9
Ruiz, 2016, Formal performance evaluation of the Map/reduce framework within cloud computing, J. Supercomput., 72, 3136, 10.1007/s11227-015-1553-2
Keshanchi, 2017, An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing, J. Syst. Softw., 124, 1, 10.1016/j.jss.2016.07.006
Cao, 2011, Formal verification of temporal properties for reduced overhead in grid scientific workflows, J. Comput. Sci. Technol., 26, 1017, 10.1007/s11390-011-1198-4
Wang, 2020, Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computing, Chin. J. Aeronaut.
Komosny, 2017, Testing Internet applications and services using PlanetLab, Comput. Stand. Interfaces, 53, 33, 10.1016/j.csi.2017.02.006