Reasoning Based Virtual Machine Mapping Toward Physical Machine

Cloud computing is an arising paradigm to run and hosts a number of applications and services. These computing services are accommodated by a set of virtual machines. These virtual machines are an abstraction of real servers or physical machines. A physical machine can hosts a number of virtual machines, depending on its capacity. Virtual machine placement has a direct effect on the quality of the services for both end-users and cloud service providers. In this study, we address the problem of virtual machine placement and migration for minimization of resources and power consumption. We formulate this problem as a multi-objective optimization and propose a resource-aware reasoning based scheme with state-of-the-art solutions. The simulations results with real-world traces show that the integrated schemes use a fewer number of physical servers to accommodate more virtual machines.

[1]  Yanli Yin,et al.  Energy-Efficient Many-Objective Virtual Machine Placement Optimization in a Cloud Computing Environment , 2017, IEEE Access.

[2]  Juan Luo,et al.  Reliable Virtual Machine Placement Based on Multi-Objective Optimization With Traffic-Aware Algorithm in Industrial Cloud , 2018, IEEE Access.

[3]  Wonjun Lee,et al.  Joint flow and virtual machine placement in hybrid cloud data centers , 2017, J. Netw. Comput. Appl..

[4]  Mário M. Freire,et al.  Approaches for optimizing virtual machine placement and migration in cloud environments: A survey , 2018, J. Parallel Distributed Comput..

[5]  Thinh Nguyen,et al.  A Dynamic Virtual Machine Placement and Migration Scheme for Data Centers , 2021, IEEE Transactions on Services Computing.

[6]  Muhammad Faheem,et al.  Performance prediction and adaptation for database management system workload using Case-Based Reasoning approach , 2018, Inf. Syst..

[7]  Ching-Hsien Hsu,et al.  Virtual machine placement with (m, n)-fault tolerance in cloud data center , 2017, Cluster Computing.

[8]  Alexander L. Stolyar,et al.  Shadow-Routing Based Dynamic Algorithms for Virtual Machine Placement in a Network Cloud , 2013, IEEE Transactions on Cloud Computing.

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

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

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

[12]  Anitha Ponraj,et al.  Optimistic virtual machine placement in cloud data centers using queuing approach , 2019, Future Gener. Comput. Syst..

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

[14]  Xuesong Zhou,et al.  Solving the time-dependent multi-trip vehicle routing problem with time windows and an improved travel speed model by a hybrid solution algorithm , 2018, Cluster Computing.

[15]  Hui Zhao,et al.  Power-Aware and Performance-Guaranteed Virtual Machine Placement in the Cloud , 2018, IEEE Transactions on Parallel and Distributed Systems.

[16]  T. V. Lakshman,et al.  Online Allocation of Virtual Machines in a Distributed Cloud , 2017, IEEE/ACM Transactions on Networking.

[17]  Enda Barrett,et al.  An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions , 2019, Simul. Model. Pract. Theory.

[18]  Huaglory Tianfield,et al.  Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters , 2018, IEEE Access.

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

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

[21]  Zoltán Ádám Mann,et al.  Which is the best algorithm for virtual machine placement optimization? , 2017, Concurr. Comput. Pract. Exp..