Service Oriented Cloud VM Placement Strategy for Internet of Things

The back-end intelligent system of Internet of Things (IoT) needs the powerful computing ability to support its service, and therefore, IoT is often constructed on the cloud platform. However, the resources for cloud computing are limited, and different types of services will have different demands for the resources. This paper proposed a service-oriented virtual machine (VM) placement strategy in cloud data center and divided the roles of VM into Web role, worker role, and storage according to the function types. On the basis of service orientation and in consideration of communication overhead between VMs in the data center, the genetic algorithm was used to conduct the optimal configuration for different types of VMs under the situation of limited resources to achieve the minimum communication overhead and total power consumption. The proposed cloud VM placement strategy is also suitable for the intelligent computing platform of IoT back end.

[1]  Ahmed E. Kamal,et al.  Power Minimization in Fat-Tree SDN Datacenter Operation , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Li-Der Chou,et al.  Service-Oriented Virtual Machine Placement Optimization for Green Data Center , 2015, Mob. Networks Appl..

[4]  Massoud Pedram,et al.  Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[5]  Yan Zhang,et al.  On Architecture Design, Congestion Notification, TCP Incast and Power Consumption in Data Centers , 2013, IEEE Communications Surveys & Tutorials.

[6]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

[7]  Ching-Chi Lin,et al.  Energy-efficient Virtual Machine Provision Algorithms for Cloud Systems , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[8]  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.

[9]  Anirudha Sahoo,et al.  On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[10]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[11]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[12]  Gautam Kar,et al.  Application Performance Management in Virtualized Server Environments , 2006, 2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006.

[13]  Anja Strunk.  Costs of Virtual Machine Live Migration: A Survey , 2012, 2012 IEEE Eighth World Congress on Services.

[14]  Yellu Sreenivasulu,et al.  FAST TRANSPARENT MIGRATION FOR VIRTUAL MACHINES , 2014 .

[15]  Hai Jin,et al.  Live Virtual Machine Migration via Asynchronous Replication and State Synchronization , 2011, IEEE Transactions on Parallel and Distributed Systems.

[16]  Peng Zhang,et al.  Virtual Machine Placement for Improving Energy Efficiency and Network Performance in IaaS Cloud , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[17]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[18]  Jianhua Gu,et al.  An Optimized Control Strategy for Load Balancing Based on Live Migration of Virtual Machine , 2011, 2011 Sixth Annual Chinagrid Conference.

[19]  Jian Tang,et al.  Survivable Virtual Infrastructure Mapping in Virtualized Data Centers , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[20]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[21]  Minghua Chen,et al.  Joint VM placement and routing for data center traffic engineering , 2012, 2012 Proceedings IEEE INFOCOM.

[22]  Gary R. Allred.  System/370 integrated emulation under OS and DOS , 1971, AFIPS '71 (Spring).

[23]  Sandeep K. S. Gupta,et al.  Trends and effects of energy proportionality on server provisioning in data centers , 2010, 2010 International Conference on High Performance Computing.

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

[25]  Pascal Bouvry,et al.  Green Flexible Opportunistic Computing with Virtualization , 2011, 2011 IEEE 11th International Conference on Computer and Information Technology.

[26]  Amin Vahdat,et al.  A scalable, commodity data center network architecture , 2008, SIGCOMM '08.

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

[28]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[29]  Ilsun You,et al.  Application-Aware Virtual Machine Placement in Data Centers , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[30]  Dong Xu,et al.  A Time-Series Based Precopy Approach for Live Migration of Virtual Machines , 2011, 2011 IEEE 17th International Conference on Parallel and Distributed Systems.

[31]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[32]  César A. F. De Rose,et al.  Maximum Migration Time Guarantees in Dynamic Server Consolidation for Virtualized Data Centers , 2011, Euro-Par.

[33]  Charles E. Leiserson,et al.  Fat-trees: Universal networks for hardware-efficient supercomputing , 1985, IEEE Transactions on Computers.

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

[35]  Kang-Won Lee,et al.  Application-aware virtual machine migration in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[36]  Naixue Xiong,et al.  VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers , 2013, Comput. Networks.

[37]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.