Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions

Energy efficiency is one of the important issues in green cloud data centers (DCs). In this context, a virtual machine (VM) placement is one of the important techniques which can be used to achieve energy efficiency in such environments. Bio-inspired optimization algorithms are widely used in the literature to solve the VM placement (VMP) problem and different types of them are benefited to achieve energy efficiency while meeting Quality of Service (QoS) and user-specified constraints such as deadlines and cost. This paper presents a comprehensive survey and taxonomy of the bio-inspired VMP schemes. For this purpose, we first provide the essential concepts regarding the VMP and describe various objectives and factors which can be considered in this process. Then, we provide a taxonomy of VMP schemes regarding their applied optimization algorithms and compare their employed factors in the VMP process as well as simulator environments and the metrics which have been utilized in the verification of the investigated VMP frameworks. Finally, the concluding remarks and future researches directions are provided.

[1]  Xiuqi Li,et al.  Virtual machine consolidated placement based on multi-objective biogeography-based optimization , 2016, Future Gener. Comput. Syst..

[2]  Mostafa Ghobaei-Arani,et al.  An efficient approach for improving virtual machine placement in cloud computing environment , 2017, J. Exp. Theor. Artif. Intell..

[3]  Mohammad Sadegh Aslanpour,et al.  Resource provisioning for cloud applications: a 3-D, provident and flexible approach , 2017, The Journal of Supercomputing.

[4]  S. D. Madhu Kumar,et al.  Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm , 2017 .

[5]  Wei Li,et al.  Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm , 2012, ICONIP.

[6]  Ye Tao,et al.  Multi-objective Ant Colony Optimization Algorithm Based on Load Balance , 2016, ICCCS.

[7]  S. Vijay Bhanu,et al.  A multi-objective krill herd algorithm for virtual machine placement in cloud computing , 2018, The Journal of Supercomputing.

[8]  Jie Wu,et al.  A Multi-objective Biogeography-Based Optimization for Virtual Machine Placement , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[9]  Samir Tata,et al.  Network-Aware Stochastic Virtual Machine Placement in Geo-Distributed Data Centers - (Short Paper) , 2017, OTM Conferences.

[10]  Nadjia Kara,et al.  Multi-objective ACO virtual machine placement in cloud computing environments , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).

[11]  Hongli Zhang,et al.  Discrete PSO-based workload optimization in virtual machine placement , 2018, Personal and Ubiquitous Computing.

[12]  Qing Zhao,et al.  Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing , 2018, Sci. Program..

[13]  Amir Masoud Rahmani,et al.  Dynamic VMs placement for energy efficiency by PSO in cloud computing , 2016, J. Exp. Theor. Artif. Intell..

[14]  MasdariMohammad,et al.  Towards workflow scheduling in cloud computing , 2016 .

[15]  Zibin Zheng,et al.  Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers , 2013, 2013 International Conference on Parallel and Distributed Systems.

[16]  Gang Wu,et al.  A New Approach to Multi-objective Virtual Machine Placement in Virtualized Data Center , 2013, 2013 IEEE Eighth International Conference on Networking, Architecture and Storage.

[17]  Amol C. Adamuthe,et al.  Multiobjective Virtual Machine Placement in Cloud Environment , 2013, 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies.

[18]  Alireza Souri,et al.  Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review , 2019, Int. J. Commun. Syst..

[19]  Ching-Hsien Hsu,et al.  Provision of Data-Intensive Services Through Energy- and QoS-Aware Virtual Machine Placement in National Cloud Data Centers , 2016, IEEE Transactions on Emerging Topics in Computing.

[20]  Mohammad Masdari,et al.  Towards workflow scheduling in cloud computing: A comprehensive analysis , 2016, J. Netw. Comput. Appl..

[21]  Bibhudatta Sahoo,et al.  Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers , 2017 .

[22]  Maolin Tang,et al.  A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers , 2014, Neural Processing Letters.

[23]  Daniele Tarchi,et al.  A reliable, secure, and energy efficient smart grid node allocation algorithm for heterogeneous network scenarios , 2018, Int. J. Commun. Syst..

[24]  Hong Chen.  A Grouping Genetic Algorithm for Virtual Machine Placement in Cloud Computing , 2016, CollaborateCom.

[25]  Suat Özdemir,et al.  Multi-objective virtual machine placement optimization for cloud computing , 2017, 2017 International Symposium on Networks, Computers and Communications (ISNCC).

[26]  Daniel C. Lee,et al.  A biogeography-based optimization algorithm for energy efficient virtual machine placement , 2014, 2014 IEEE Symposium on Swarm Intelligence.

[27]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[28]  Maolin Tang,et al.  A simulated annealing algorithm for energy efficient virtual machine placement , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[29]  Fabio Lopez-Pires,et al.  A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets , 2020, ArXiv.

[30]  Siyuan Liu,et al.  Virtual machine placement based on degradation factor ant colony algorithm , 2018, 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[31]  Yu-Chu Tian,et al.  A decrease-and-conquer genetic algorithm for energy efficient virtual machine placement in data centers , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[32]  Samir Tata,et al.  Traffic-aware virtual machine placement in geographically distributed Clouds , 2014, 2014 International Conference on Control, Decision and Information Technologies (CoDIT).

[33]  Chao Liu,et al.  A new evolutionary multi-objective algorithm to virtual machine placement in virtualized data center , 2014, 2014 IEEE 5th International Conference on Software Engineering and Service Science.

[34]  Norman W. Paton,et al.  Optimizing virtual machine placement for energy and SLA in clouds using utility functions , 2016, Journal of Cloud Computing.

[35]  José Antonio Lozano,et al.  Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies , 2015, Journal of Grid Computing.

[36]  Lei Wu,et al.  Ant colony optimization of virtual machine placement for data latency minimization in cloud systems , 2015, 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP).

[37]  Peng Zhang,et al.  Energy-Saving Virtual Machine Placement in Cloud Data Centers , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[38]  Mohammad Masdari,et al.  A Survey of PSO-Based Scheduling Algorithms in Cloud Computing , 2016, Journal of Network and Systems Management.

[39]  Huaxi Gu,et al.  Energy Efficient Virtual Machine Placement With an Improved Ant Colony Optimization Over Data Center Networks , 2019, IEEE Access.

[40]  Hadi Tabatabaee Malazi,et al.  Energy Efficieny in Virtual Machines Allocation for Cloud Data Centers Using the Imperialist Competitive Algorithm , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

[41]  Liang Liu,et al.  A multi-objective ant colony system algorithm for virtual machine placement in cloud computing , 2013, J. Comput. Syst. Sci..

[42]  D. Kesavaraja,et al.  QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization , 2017, J. Parallel Distributed Comput..

[43]  Arun Kumar Sangaiah,et al.  An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment , 2018, Cluster Computing.

[44]  Muhammad Shafiq,et al.  An Improved Particle Swarm Optimization for Energy-Efficiency Virtual Machine Placement , 2017, 2017 International Conference on Cloud Computing Research and Innovation (ICCCRI).

[45]  Kenli Li,et al.  Optimal Virtual Machine Placement Based on Grey Wolf Optimization , 2019, Electronics.

[46]  Medhat A. Tawfeek,et al.  Virtual Machine Placement Based on Ant Colony Optimization for Minimizing Resource Wastage , 2014, AMLTA.

[47]  Liang Hong,et al.  GACA-VMP: Virtual Machine Placement Scheduling in Cloud Computing Based on Genetic Ant Colony Algorithm Approach , 2015, 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom).

[48]  El-Ghazali Talbi,et al.  A pareto-based genetic algorithm for optimized assignment of VM requests on a cloud brokering environment , 2013, 2013 IEEE Congress on Evolutionary Computation.

[49]  Xuejie Zhang,et al.  An approach for cloud resource scheduling based on Parallel Genetic Algorithm , 2011, 2011 3rd International Conference on Computer Research and Development.

[50]  Benjamín Barán,et al.  Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[51]  Zhi-hui Zhan,et al.  Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach , 2014, GECCO.

[52]  Jie Lu,et al.  A multi-objective optimization model for virtual machine mapping in cloud data centres , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[53]  Piyuan Lin,et al.  Energy Efficient VM Placement Heuristic Algorithms Comparison for Cloud with Multidimensional Resources , 2012, ICICA.

[54]  Yu-Chu Tian,et al.  Profile-Based Ant Colony Optimization for Energy-Efficient Virtual Machine Placement , 2017, ICONIP.

[55]  Gadadhar Sahoo,et al.  Crow search based virtual machine placement strategy in cloud data centers with live migration , 2017, Comput. Electr. Eng..

[56]  Haibo Zhang,et al.  Energy-Aware on-chip virtual machine placement for cloud-supported cyber-physical systems , 2017, Microprocess. Microsystems.

[57]  Ruo Bao.  Performance Evaluation for Traditional Virtual Machine Placement Algorithms in the Cloud , 2016, IOV.

[58]  Liam Murphy,et al.  GeNePi: A Multi-Objective Machine Reassignment Algorithm for Data Centres , 2014, Hybrid Metaheuristics.

[59]  Habib Youssef,et al.  Multi-Objective Virtual Machine Placement Algorithm Based on Particle Swarm Optimization , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[60]  Hua Wang,et al.  An Energy-Aware Ant Colony Algorithm for Network-Aware Virtual Machine Placement in Cloud Computing , 2016, 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS).

[61]  Benjamín Barán,et al.  Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty , 2018, Future Gener. Comput. Syst..

[62]  Yao-Hui Chang,et al.  Virtual machine placement strategy based on discrete firefly algorithm in cloud environments , 2015, 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP).

[63]  Hemraj Saini,et al.  Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm , 2019, Int. J. Inf. Secur. Priv..

[64]  N. Nagaveni,et al.  Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence , 2012, Future Gener. Comput. Syst..

[65]  Guilherme Arthur Geronimo,et al.  Order@Cloud: A VM organisation framework based on multi-objectives placement ranking , 2016, NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium.

[66]  Benjamín Barán,et al.  Virtual machine placement. A multi-objective approach , 2013, 2013 XXXIX Latin American Computing Conference (CLEI).

[67]  Xiao-Dong Fu,et al.  A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform , 2014, TheScientificWorldJournal.

[68]  Mauricio G. C. Resende,et al.  A Biased Random-key Genetic Algorithm for Placement of Virtual Machines across Geo-Separated Data Centers , 2015, GECCO.

[69]  Mohammad Mehdi Ebadzadeh,et al.  A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment , 2019, Cluster Computing.

[70]  Fei Luo,et al.  DFA-VMP: An efficient and secure virtual machine placement strategy under cloud environment , 2016, Peer-to-Peer Networking and Applications.

[71]  Saeed Sharifian,et al.  A modified knowledge-based ant colony algorithm for virtual machine placement and simultaneous routing of NFV in distributed cloud architecture , 2019, The Journal of Supercomputing.

[72]  Haifeng Guo,et al.  A Power-Aware Virtual Machine Mapper Using Firefly Optimization , 2015, 2015 Third International Conference on Advanced Cloud and Big Data.

[73]  Albert Y. Zomaya,et al.  Energy-Efficient Data Center Networks Planning with Virtual Machine Placement and Traffic Configuration , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[74]  Mostafa Ghobaei-Arani,et al.  A learning‐based approach for virtual machine placement in cloud data centers , 2018, Int. J. Commun. Syst..

[75]  Chen-Khong Tham,et al.  Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing , 2011, 2011 IEEE International Conference on Advanced Information Networking and Applications.

[76]  Maolin Tang,et al.  A Penalty-Based Genetic Algorithm for the Migration Cost-Aware Virtual Machine Placement Problem in Cloud Data Centers , 2015, ICONIP.

[77]  Jun Zhang,et al.  An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing , 2018, IEEE Transactions on Evolutionary Computation.

[78]  Jianmin Jiang,et al.  A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory , 2015, J. Syst. Softw..

[79]  Saeed Sharifian,et al.  Dynamic prediction scheduling for virtual machine placement via ant colony optimization , 2015, 2015 Signal Processing and Intelligent Systems Conference (SPIS).

[80]  Zaki Brahmi,et al.  VM placement algorithm based on recruitment process within ant colonies , 2016, 2016 International Conference on Digital Economy (ICDEc).

[81]  Gadadhar Sahoo,et al.  A resource aware VM placement strategy in cloud data centers based on crow search algorithm , 2017, 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS).

[82]  Sadok Bouamama,et al.  Solving bin Packing Problem with a Hybrid Genetic Algorithm for VM Placement in Cloud , 2015, KES.

[83]  Lei Zhu,et al.  Towards energy efficient cloud: an optimized ant colony model for virtual machine placement , 2016, Journal of Communications and Information Networks.

[84]  Hua Wang,et al.  A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Traffic Intense Data Centers , 2018, IEEE Access.

[85]  Mohammad Masdari,et al.  An overview of virtual machine placement schemes in cloud computing , 2016, J. Netw. Comput. Appl..

[86]  Mohammad Masdari,et al.  A survey and taxonomy of DoS attacks in cloud computing , 2016, Secur. Commun. Networks.

[87]  David Atienza,et al.  Exploiting CPU-load and data correlations in multi-objective VM placement for geo-distributed data centers , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[88]  M SaitSadiq,et al.  Cuckoo search based resource optimization of datacenters , 2016 .

[89]  Benjamín Barán,et al.  Many-objective virtual machine placement for dynamic environments , 2015, 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC).

[90]  Nadeem Javaid,et al.  An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers , 2019, Electronics.

[91]  Benjamín Barán,et al.  Many-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2017, Research Advances in Cloud Computing.