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Latency-aware virtual desktops optimization in distributed clouds

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Abstract

Distributed clouds offer a choice of data center locations for providers to host their applications. In this paper, we consider distributed clouds that host virtual desktops which are then accessed by users through remote desktop protocols. Virtual desktops have different levels of latency-sensitivity, primarily determined by the actual applications running and affected by the end users’ locations. In the scenario of mobile users, even switching between 3G and WiFi networks affects the latency-sensitivity. We design VMShadow, a system to automatically optimize the location and performance of latency-sensitive VMs in the cloud. VMShadow performs black-box fingerprinting of a VM’s network traffic to infer the latency-sensitivity and employs both ILP and greedy heuristic based algorithms to move highly latency-sensitive VMs to cloud sites that are closer to their end users. VMShadow employs a WAN-based live migration and a new network connection migration protocol to ensure that the VM migration and subsequent changes to the VM’s network address are transparent to end-users. We implement a prototype of VMShadow in a nested hypervisor and demonstrate its effectiveness for optimizing the performance of VM-based desktops in the cloud. Our experiments on a private as well as the public EC2 cloud show that VMShadow is able to discriminate between latency-sensitive and insensitive desktop VMs and judiciously moves only those that will benefit the most from the migration. For desktop VMs with video activity, VMShadow improves VNC’s refresh rate by 90% by migrating virtual desktop to the closer location. Transcontinental remote desktop migrations only take about 4 min and our connection migration proxy imposes 13 μs overhead per packet.

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Notes

  1. Example features include throughput, connection duration or inter-packet latency.

  2. Live migration of a VM takes place in rounds, where the whole disk and memory state is migrated in the first round. Since the VM is executing in this period, it dirties a fraction of the disk and memory, and in the next round, we must move \((S_{\text {disk}} + S_{\text {mem}}) \cdot r ,\) where r is the dirtied fraction. The next round will need an additional \((S_{\text {disk}} + S_{\text {mem}})\cdot r^2\). Thus we obtain an expression: \((S_{\text {disk}} + S_{\text {mem}})\cdot (1 + r + r^2 + \cdots )\). This expression can be further refined using different disk and memory dirtying rates for the VM.

  3. Our focus is not on comparing the performance differences of different remote desktop protocol.

  4. We choose Chrome browser due to the fact that Netflix is not supported in the default Firefox browser.

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Acknowledgements

We would like to thank all our reviewers for their comments and suggestions. This research was supported by NSF Grants CNS-1117221, CNS-1345300 and OCI-1032765.

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Authors and Affiliations

  1. Worcester Polytechnic Institute, Worcester, USA

    Tian Guo

  2. Umass Amherst, Amherst, USA

    Prashant Shenoy

  3. UC Riverside, Riverside, USA

    K. K. Ramakrishnan

  4. AT&T Labs, Bedminster, USA

    Vijay Gopalakrishnan

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  1. Tian Guo
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  2. Prashant Shenoy
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  3. K. K. Ramakrishnan
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  4. Vijay Gopalakrishnan
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Corresponding author

Correspondence to Tian Guo.

Additional information

Communicated by R. Rejaie.

A preliminary version of this work appeared at the ACM MMSys 2014 Conference [1].

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Guo, T., Shenoy, P., Ramakrishnan, K.K. et al. Latency-aware virtual desktops optimization in distributed clouds. Multimedia Systems 24, 73–94 (2018). https://doi.org/10.1007/s00530-017-0536-y

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