Fault-tolerant feedback virtual machine deployment based on user-personalized requirements

A key requirement of the cloud platform is the reasonable deployment of its large-scale virtual machine infrastructure. The mapping relation between the virtual node and the physical node determines the specific resource distribution strategy and reliability of the virtual machine deployment. Resource distribution strategy has an important effect on performance, energy consumption, and guarantee of the quality of service of the computer, and serves an important role in the deployment of the virtual machine. To solve the problem of meeting the fault-tolerance requirement and guarantee high reliability of the application system based on the full use of the cloud resource under the prerequisite of various demands, the deployment framework of the feedback virtual machine in cloud platform facing the individual user’s demands of fault-tolerance level and the corresponding deployment algorithm of the virtual machine are proposed in this paper. Resource distribution strategy can deploy the virtual machine in the physical nodes where the resource is mutually complementary according to the users’ different requirements on virtual resources. The deployment framework of the virtual machine in this paper can provide a reliable computer configuration according to the specific fault-tolerance requirements of the user while considering the usage rate of the physical resources of the cloud platform. The experimental result shows that the method proposed in this paper can provide flexible and reliable select permission of fault-tolerance level to the user in the virtual machine deployment process, provide a pertinent individual fault-tolerant deployment method of the virtual machine to the user, and guarantee to meet the user service in a large probability to some extent.

[1]  Ching-Hsien Hsu,et al.  Locality and loading aware virtual machine mapping techniques for optimizing communications in MapReduce applications , 2015, Future Gener. Comput. Syst..

[2]  Qiang Li,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .

[3]  Chen Junjie,et al.  The Research on Personalized VM Deployment Mechanism in Cloud , 2012 .

[4]  Xiaomin Zhu,et al.  FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized Clouds , 2015, IEEE Transactions on Computers.

[5]  Ying Li,et al.  Energy efficient scheduling with probability and task migration considerations for soft real-time systems , 2014, 2014 IEEE Computers, Communications and IT Applications Conference.

[6]  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.

[7]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[8]  Li Qiang,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .

[9]  Zibin Zheng,et al.  Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization , 2017, IEEE Transactions on Services Computing.

[10]  Peng Hong,et al.  Virtual Machine Deployment based on the Needs of Individual Users , 2013 .

[11]  Rajkumar Buyya,et al.  Using Proactive Fault-Tolerance Approach to Enhance Cloud Service Reliability , 2018, IEEE Transactions on Cloud Computing.

[12]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[13]  Anxiao Jiang,et al.  Systematic error-correcting codes for rank modulation , 2012, ISIT.

[14]  Nasreddine Lagraa,et al.  FNB: Fast Non-Blocking Coordinated Checkpointing Protocol for Distributed Systems , 2014, Theory of Computing Systems.

[15]  Ke Xu,et al.  Utility Maximization Model of Virtual Machine Scheduling in Cloud Environment: Utility Maximization Model of Virtual Machine Scheduling in Cloud Environment , 2014 .

[16]  Shi-Hai Liu,et al.  An Adaptive Bandwidth Allocation Algorithm for Virtual Machine Migration Based on Service Features: An Adaptive Bandwidth Allocation Algorithm for Virtual Machine Migration Based on Service Features , 2014 .

[17]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[18]  Fumio Machida,et al.  Redundant virtual machine placement for fault-tolerant consolidated server clusters , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[19]  Priyanka Sharma,et al.  Survey of virtual machine placement in federated clouds , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[20]  Ansuman Banerjee,et al.  Fault Tolerance as a Service , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

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

[22]  Raymond H. Putra,et al.  Dependable virtual machine allocation , 2013, 2013 Proceedings IEEE INFOCOM.

[23]  Liu Shi An Adaptive Bandwidth Allocation Algorithm for Virtual Machine Migration Based on Service Features , 2013 .

[24]  Min Xie,et al.  A study of N-version programming and its impact on software availability , 2014, Int. J. Syst. Sci..

[25]  Weimin Zheng,et al.  Automatic software deployment using user-level virtualization for cloud-computing , 2013, Future Gener. Comput. Syst..

[26]  Shi Xue Utility Maximization Model of Virtual Machine Scheduling in Cloud Environment , 2013 .

[27]  Vincenzo Piuri,et al.  Chapter 1 – Fault Tolerance and Resilience in Cloud Computing Environments , 2014 .