Virtual Machine Allocation in Cloud Computing Environment

2013 ◽  
Vol 3 (2) ◽  
pp. 47-60 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Seyed M. Buhari ◽  
Sahalu B. Junaidu
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Infrastructure ◽  
Private Cloud ◽  
Mapping Algorithm ◽  
Machine Allocation ◽  
Virtual Machine Allocation

Virtual machine allocation problem is one of the challenges in cloud computing environments, especially for the private cloud design. In this environment, each virtual machine is mapped unto the physical host in accordance with the available resource on the host machine. Specifically, quantifying the performance of scheduling and allocation policy on a Cloud infrastructure for different application and service models under varying performance metrics and system requirement is an extremely challenging and difficult problem to resolve. In this paper, the authors present a Virtual Computing Laboratory framework model using the concept of private cloud by extending the open source IaaS solution Eucalyptus. A rule based mapping algorithm for Virtual Machines (VMs) which is formulated based on the principles of set theoretic is also presented. The algorithmic design is projected towards being able to automatically adapt the mapping between VMs and physical hosts’ resources. The paper, similarly presents a theoretical study and derivations of some performance evaluation metrics for the chosen mapping policies, these includes determining the context switching, waiting time, turnaround time, and response time for the proposed mapping algorithm.

scholarly journals A dynamic programming approach to manage virtual machines allocation in cloud computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 128
Author(s):  
Abdellah Ouammou ◽  
Mohamed Hanini ◽  
Abdelghani Ben Tahar ◽  
Said El Kafhali
Keyword(s):  
Cloud Computing ◽  
Dynamic Programming ◽  
Resource Management ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Stochastic Modelling ◽  
Programming Approach ◽  
Conflicting Objectives ◽  
Machine Allocation ◽  
Virtual Machine Allocation

As a result of the dynamic nature of Virtual Machine allocation in cloud computing, it is not easy to manage system resources or choose the best configuration based solely on human experience.  In this work, we used stochastic modelling instead of comprehensive experiments to evaluate the best resource management of the system. In such complex systems, choosing the best decision is a challenge, for this reason we have designed a heuristic algorithm, specifically, dynamic programming as a resource management and programming tool that finds a way that attempts to satisfy the conflicting objectives of high performance and low power consumption. As a scenario for using this algorithm, we addressed the problem of virtual machine allocation, a subset of physical machines is designated as "reserve", and the reserves are actives when the number of jobs in the system is sufficiently high. The question is how to decide when to activate the reserves. The simulation results demonstrated the benefit of using our framework to identify the policy for consolidation or for a low energy consumption and in order to have a good quality of service in the system

scholarly journals Improved Virtual Machine Allocation Strategy using Particle Swarm Optimization Algorithm

2018 ◽  
Vol 7 (2.7) ◽  
pp. 813
Author(s):  
B Thirumala Rao ◽  
K Nandavardhini ◽  
K Navya ◽  
G Krishna Venkata Sunil
Keyword(s):  
Virtual Machine ◽  
Virtual Machines ◽  
Particle Swarm Optimization Algorithm ◽  
Critical Issue ◽  
Allocation Strategy ◽  
Swarm Optimization ◽  
Machine Allocation ◽  
Virtual Machine Allocation ◽  
Cloud Condition ◽  
Cross System

Virtual machine position (VMP) is a critical issue in choosing most appropriate arrangement of physical machines (PMs) for an arrangement of virtual machines (VMs) in distributed computing condition. These days information concentrated applications for handling huge information are being facilitated in the cloud. Since the cloud condition gives virtualized assets to calculation, and information concentrated applications require correspondence between the registering hubs, the situation of Virtual Machines (VMs) and area of information influence the general calculation time. The essential target is to decrease cross system activity and transmission capacity use, by setting required number of VMs and information in Physical Machines (PMs) which are physically nearer. This paper exhibits and assesses by a meta-heuristic calculation in view of Parallel Computing and Optimization (PCO) which select an arrangement of adjoining PMs for setting information and VMs . In the wake of choosing the PMs, the information are duplicated to the capacity gadgets of the PMs and the required number of VMs are begun on the PMs based on their VM allotment limits. Recreation comes about demonstrate that this determination diminishes the whole of separations amongst VMs and henceforth lessens the activity fruition time.

scholarly journals Security Strategy for Virtual Machine Allocation in Cloud Computing

2019 ◽  
Vol 147 ◽  
pp. 140-144 ◽  
Author(s):  
Hefei Jia ◽  
Xu Liu ◽  
Xiaoqiang Di ◽  
Hui Qi ◽  
Ligang Cong ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Security Strategy ◽  
Machine Allocation ◽  
Virtual Machine Allocation

scholarly journals Optimal Allocation of Virtual Machines in Cloud Computing

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 756
Author(s):  
Ming-Hua Lin ◽  
Jung-Fa Tsai ◽  
Yi-Chung Hu ◽  
Tzu-Hsuan Su
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Original Problem ◽  
Optimal Allocation ◽  
Virtual Machines ◽  
Mixed Integer ◽  
Quadratic Assignment ◽  
Optimization Approach ◽  
Communication Costs ◽  
Machine Allocation

Virtualization is one of the core technologies used in cloud computing to provide services on demand for end users over the Internet. Most current research allocates virtual machines to physical machines based on CPU utilization. However, for many applications that require communication between services running on different servers, communication costs influence the overall performance. Therefore, this study focuses on the optimal allocation of virtual machines across multiple geographically dispersed data centers, with the objective of minimizing communication costs. The original problem can be constructed as a quadratic assignment problem that is a classical NP-hard combinatorial optimization problem. This study adopts an efficient deterministic optimization approach to reformulate the original problem as a mixed-integer linear program that may be solved to obtain a globally optimal solution. Since the required bandwidth matrix and communication cost matrix are symmetric, the mathematical model of virtual machine placement can be simplified. Several numerical examples drawn from the literature are solved to demonstrate the computational efficiency of the proposed method for determining the optimal virtual machine allocation in cloud computing.

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