Performance Optimization of Hypervisor’s Network Bridge by Reducing Latency in Virtual Layers

  • Ponnamanda China Venkanna VarmaEmail author
  • V. Valli Kumari
  • S. Viswanadha Raju
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)


Multi tenant enabled cloud computing is the de facto standard to rent computing resources in data centers. The basic enabling technology for cloud computing is hardware virtualization (Hypervisor), software defined networking (SDN) and software defined storage (SDS). SDN and SDS provision virtual network and storage elements to attach to a virtual machine (VM) in a computer network. Every byte processed in the VM has to travel in the network, hence storage throughput is proportional to network throughput. There is a high demand to optimize the network throughput to improve storage and overall system throughput in big data environments. Provisioning VMs on top of a hypervisor is a better model for high resource utilization. We observed that, as more VMs share the same virtual resources, there is a negative impact on the compute, network, and storage throughput of the system because the CPU is busy in context switching (Popescu et al. [1]). We studied KVM (Hirt, KVM—The kernel-based virtual machine [2]) hypervisor’s network bridge and measured throughput of the system using benchmarks such as Iometer (Iometer, [3]), (Netperf, [4]) against varying number of VMs. We observed a bottleneck in the network and storage due to increased round trip time (RTT) of the data packets caused by both virtual network layers and CPU context switches ( [5]). We have enhanced virtual network bridge to optimize RTT of data packets by reducing wait time in the network bridge and measured 8, 12% throughput improvement for network and storage respectively. This enhanced network bridge can be used in production with explicit configurations.


Hypervisor TCP round trip time Netperf Iometer Context switching Software defined storage Software defined network 


  1. 1.
    Popescu, D.A., Zilberman, N., Moore, A.W.:
  2. 2.
    Hirt, T: KVM—The kernel-based virtual machineGoogle Scholar
  3. 3.
  4. 4.
  5. 5.
  6. 6.
    China Venkanna Varma, P., Venkata Kalyan Chakravarthy, K., Valli Kumari, V., Viswanadha Raju, S.: Analysis of a network IO bottleneck in big data environments based on docker containersGoogle Scholar
  7. 7.
    Nuutti V.: Anatomy of a linux bridgeGoogle Scholar
  8. 8.
    Wu, Z.Z., Chen, H.C.: Design and implementation of TCP/IP offload engine system over gigabit ethernetGoogle Scholar
  9. 9.
  10. 10.
    Gupta, A.: A research study on packet sniffing tool TCPDUMP. Suresh Gyan Vihar, University, India:Google Scholar
  11. 11.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ponnamanda China Venkanna Varma
    • 1
    Email author
  • V. Valli Kumari
    • 2
  • S. Viswanadha Raju
    • 3
  1. 1.OpsRamp Inc.Kondapur, HyderabadIndia
  2. 2.AP State Council of Higher EducationTadepalliIndia
  3. 3.JNTUH College of EngineeringJagitialIndia

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