Web Server Performance Evaluation in a Virtualisation Environment

Operational and investment costs are reduced by resource sharing in virtual machine (VM) environments, which also results in an overhead for hosted services. VM machine performance is important because of resource contention. If an application takes a long time to execute because of its CPU or network, it is considered to be a failure because if many VMs are running over a single hardware platform, there will be competition for shared resources, e.g., the CPU, network bandwidth, and memory. Therefore, this study focuses on measuring the performance of a web server under a virtual environment and comparing those results with that from a dedicated machine. We found that the difference between the two sets of results is largely negligible. However, in some areas, one approach performed better than the other.

[1]  John M. Acken,et al.  Cloud Workload Characterization , 2013 .

[2]  Tinghuai Ma,et al.  Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm , 2014 .

[3]  Ludmila Cherkasova,et al.  Measuring CPU Overhead for I/O Processing in the Xen Virtual Machine Monitor , 2005, USENIX ATC, General Track.

[4]  Vasudeva Varma,et al.  Network-aware virtual machine consolidation for large data centers , 2013, NDM '13.

[5]  Zhongcheng Li,et al.  Improving consolidation of virtual machine based on virtual switching overhead estimation , 2016, J. Netw. Comput. Appl..

[6]  Mosleh M. Abu-Alhaj,et al.  Cloud Data Auditing Techniques with a Focus on Privacy and Security , 2017, IEEE Security & Privacy.

[7]  Jian Li,et al.  Migration-Based Elastic Consolidation Scheduling in Cloud Data Center , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[8]  César A. F. De Rose,et al.  Server consolidation with migration control for virtualized data centers , 2011, Future Gener. Comput. Syst..

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

[10]  Saied M. Abd El-atty,et al.  Storage allocation scheme for virtual instances of cloud computing , 2017, Neural Computing and Applications.

[11]  Shoubin Dong,et al.  An energy-aware heuristic framework for virtual machine consolidation in Cloud computing , 2014, The Journal of Supercomputing.

[12]  Gang Yin,et al.  Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers , 2010, 2010 IEEE International Conference on Services Computing.

[13]  Willy Zwaenepoel,et al.  Diagnosing performance overheads in the xen virtual machine environment , 2005, VEE '05.

[14]  Prashant J. Shenoy,et al.  Sharing-aware algorithms for virtual machine colocation , 2011, SPAA '11.

[15]  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 .

[16]  Rajkumar Buyya,et al.  Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers , 2010, MGC '10.

[17]  Umesh Deshpande,et al.  Inter-rack live migration of multiple virtual machines , 2012, VTDC '12.

[18]  Keyvan RahimiZadeh,et al.  Performance modeling and analysis of virtualized multi-tier applications under dynamic workloads , 2015, J. Netw. Comput. Appl..

[19]  Geoffrey Fox,et al.  Evaluating GPU Passthrough in Xen for High Performance Cloud Computing , 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.