1. Resource Allocation in Virtual Desktop Clouds: VMLab-GENI Experiment Rohit Patali, Prasad Calyam, Mukundan Sridharan, Alex Berryman The Ohio State University, Columbus Abstract User communities are rapidly transitioning their "traditional desktops" that have dedicated hardware and software installations into "virtual desktop clouds" (VDCs) that are accessible via thin-clients. To allocate and manage VDC resources for Internet-scale desktop delivery, existing works focus mainly on managing server-side resources based on utility functions of CPU and memory loads, and do not consider network health and thin-client user experience. We present an analytical model viz., "Utility-Directed Resource Allocation Model (U-RAM)" to solve the resource allocation problem within VDCs. Our solution leverages utility functions of system, network and human components obtained using a novel virtual desktop performance benchmarking toolkit viz., "VDBench" that we developed. We evaluate our solution on GENI with varying user load and network health conditions. Evaluation results demonstrate that our solution maximizes VDC scalability i.e., 'VDs per core density', and 'user connections quantity', while delivering satisfactory thin-client user experience. Research Objectives Develop “system-aware”, “network-aware”, “human-aware” frameworks and tools to deploy virtual desktop clouds Couple client-and-server resource adaptation with measurements of network health and user experience to: - Minimize cloud resource over-commitment - Avoid guesswork in configuring thin client protocols - Deliver optimum user experience of virtual applications VDCs Today – Guesswork and Overprovisioning VDC Resource Allocation Scheme Current and Proposed Publications U-RAM Illustration Planned GENI Demo Cloud Scalability Performance Comparision The Research efforts have resulted in the following publications: Conference / Journal Papers Alex Berryman, Prasad Calyam, Albert Lai, Matthew Honigford, "VDBench: A Benchmarking Toolkit for Thin- client based Virtual Desktop Environments", IEEE Conference on Cloud Computing Technology and Science (CloudCom), 2010. Under Work: “Utility-Directed Resource Allocation in Virtual Desktop Clouds” The Research Effort hopes to result in the following theses/dissertations: Master’s Thesis: Resource Placement and Provisioning in Virtual Desktop Cloud – Rohit Patali Use of Glab/GENI Infrastructure Extend VMLab to a virtual desktop cloud with 3 data centers using the NSF GENI Testbed Facility Resource nodes in ProtoGENI/PlanetLab e.g., ~30 MHz CPU and ~15 GB RAM to install VMware ESX and support ~15 VD users VMLab data center will be identical to the ProtoGENI/PlanetLab setup Rate limit all VD load at data centers to 10 Mbps network bandwidth using a network emulator OnTimeMeasure Node Beacons will be installed at all the thin-clients and data centers; Root Beacon will be installed at VMLab Gush tool will be used from demo site to: - instruct VMLab web-portal to send load control commands to the smart thin-clients in ProtoGENI PlanetLab - Control OnTimeMeasure measurement service Future Work Provision “sandbox” and “desktop” VMs within a slice - For GENI experimenters, Classroom Labs, Internet users, etc. - VMs will host trial and open-source software for users Users generate synchronous and asynchronous loads Profile “monitor” and “user” VMs under various load conditions and investigate decision schemes for resource allocation - Monitor” VMs are instrumented to perform experiment runs - User loads trigger performance data logging in monitor VMs Experiments A utility function indicates how much of application performance can be increased with larger resource allocation. Beyond a certain point, application utility saturates and any additional resource allocation fails to further increase application performance. Fixed RAM (F-RAM) tends to allocate resources that result in Q excess U-RAM profiles users based on VDBench measurements and allocates resources that results in either Q min /Q set /Q max Satisfies SLA along timeliness and coding efficiency quality dimensions and ensures optimum user experience based on resources available. 1 st DFG/GENI Doctoral Consortium, San Juan, PR March 13 th -15 th, 2011 Home User Mobile User VDC Service Provider Inadequate CPU, memory and bandwidth (Impact e.g., Slow interaction response times) Calls from unhappy customers High operation $$ Problem: Resource allocation without awareness of system, network and user experience characteristics Inadequate CPU, memory and bandwidth (Impact e.g., IPTV with impairments and slow playback) Excess CPU, memory and bandwidth (Impact e.g., Good interaction response times and smooth IPTV playback) Research Scientist CPU Memory Bandwidth I. New VD requests handling with freely available resources II. New VD requests handling with all available resources allocated III. New VD request rejected when SLA violation situation occurs Legend: