A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers (DCs) has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine (VM) assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines (PM). In current paper a new approach is proposed that using a combination of the sine cosine algorithm (SCA) and ant lion optimizer (ALO) as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Second objective is reducing the resources wastage by using optimal VM assignment on PMs in cloud DCs. Reducing SLA levels was another purpose of this research. By using the method, the number of increase of migration of VMs to PMs is prevented. In this paper, several performance metrics such as resource wastage, power consumption, overall memory utilization, overall CPU utilization, overall storage space, and overall bandwidth, a number of active PMs, a number of shutdowns, a number of migrations, and SLA are used. Ultimately, the results obtained from the proposed algorithm were compared with those of the algorithms used in this regard, including First Fit (FF), VMPACS and MGGA.

[1]  Ankit Jain,et al.  Power and resource-aware virtual machine placement for IaaS cloud , 2018, Sustain. Comput. Informatics Syst..

[2]  Minrui Fei,et al.  An Ant Colony System for energy-efficient dynamic Virtual Machine Placement in data centers , 2019, Expert Syst. Appl..

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

[4]  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..

[5]  Mohammad Masdari,et al.  The Placement of Virtual Machines Under Optimal Conditions in Cloud Datacenter , 2019, Inf. Technol. Control..

[6]  Mohammad Masdari,et al.  CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs , 2019, The Journal of Supercomputing.

[7]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

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

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

[10]  Mohammad Masdari,et al.  Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues , 2019, Journal of Grid Computing.

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

[12]  Yang Li,et al.  Chemical reaction optimization for virtual machine placement in cloud computing , 2018, Applied Intelligence.

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

[14]  Amir Masoud Rahmani,et al.  An iterative mathematical decision model for cloud migration: A cost and security risk approach , 2018, Softw. Pract. Exp..

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

[16]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[17]  Mostafa Ghobaei-Arani,et al.  An efficient power-aware VM allocation mechanism in cloud data centers: a micro genetic-based approach , 2020, Cluster Computing.

[18]  Mirsaeid Hosseini Shirvani,et al.  Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm , 2020, J. Exp. Theor. Artif. Intell..

[19]  Amir Masoud Rahmani,et al.  A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: Taxonomy and challenges , 2020, J. King Saud Univ. Comput. Inf. Sci..

[20]  Tarachand Amgoth,et al.  Resource-aware virtual machine placement algorithm for IaaS cloud , 2017, The Journal of Supercomputing.

[21]  Arun Kumar Sangaiah,et al.  Pareto-optimal cost optimization for large scale cloud systems using joint allocation of resources , 2019 .

[22]  Mohammad Masdari,et al.  Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities , 2019, The Journal of Supercomputing.

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

[24]  HwaMin Lee,et al.  Energy efficient VM scheduling for big data processing in cloud computing environments , 2019, Journal of Ambient Intelligence and Humanized Computing.

[25]  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..

[26]  Mohammad Hossein Rezvani,et al.  Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach , 2020, Cluster Computing.

[27]  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).

[28]  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).

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

[30]  C. R. Rene Robin,et al.  RETRACTED ARTICLE: Power conserving resource allocation scheme with improved QoS to promote green cloud computing , 2020, Journal of Ambient Intelligence and Humanized Computing.

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

[32]  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).

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

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

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

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

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

[38]  Abdulaziz S. Alashaikh,et al.  Incorporating Ceteris Paribus Preferences in Multiobjective Virtual Machine Placement , 2019, IEEE Access.

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

[40]  Rajkumar Buyya,et al.  Network-aware virtual machine placement and migration in cloud data centers , 2015 .

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

[42]  Mohammad Masdari,et al.  A survey and classification of the workload forecasting methods in cloud computing , 2019, Cluster Computing.

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

[44]  Mohsen Rabbani,et al.  Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters , 2020, Sustain. Comput. Informatics Syst..

[45]  Sadiq M. Sait,et al.  Cuckoo search based resource optimization of datacenters , 2015, Applied Intelligence.

[46]  Zhuzhong Qian,et al.  Balancing Resource Utilization for Continuous Virtual Machine Requests in Clouds , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[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]  Abhishek Gupta,et al.  RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud , 2020, Journal of Ambient Intelligence and Humanized Computing.

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

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

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

[52]  Gang Sun,et al.  A new technique for efficient live migration of multiple virtual machines , 2016, Future Gener. Comput. Syst..

[53]  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).

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

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

[56]  R. Geetha,et al.  RETRACTED ARTICLE: An advanced artificial intelligence technique for resource allocation by investigating and scheduling parallel-distributed request/response handling , 2020, Journal of Ambient Intelligence and Humanized Computing.

[57]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[58]  S. Peer Mohamed Ziyath,et al.  RETRACTED ARTICLE: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services , 2020, Journal of Ambient Intelligence and Humanized Computing.

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

[60]  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).

[61]  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).

[62]  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.

[63]  Khalid Moussaid,et al.  FACO: a hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computing , 2019, Journal of Ambient Intelligence and Humanized Computing.

[64]  Amir Hossein Gandomi,et al.  Chaotic Krill Herd algorithm , 2014, Inf. Sci..

[65]  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.

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

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

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

[69]  Mohammad Masdari,et al.  Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions , 2019, Cluster Computing.

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

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

[72]  Mirsaeid Hosseini Shirvani,et al.  Server Consolidation Schemes in Cloud Computing Environment: A Review , 2016, European Journal of Engineering and Technology Research.

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