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Architecture for Resource Allocation Services Supporting Interactive Remote Desktop Sessions in Utility Grids Vanish Talwar, HP Labs Bikash Agarwalla,

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Presentation on theme: "Architecture for Resource Allocation Services Supporting Interactive Remote Desktop Sessions in Utility Grids Vanish Talwar, HP Labs Bikash Agarwalla,"— Presentation transcript:

1 Architecture for Resource Allocation Services Supporting Interactive Remote Desktop Sessions in Utility Grids Vanish Talwar, HP Labs Bikash Agarwalla, Georgia Tech Sujoy Basu, HP Labs Raj Kumar, HP Labs Klara Nahrstedt, UIUC

2 2 MGC 2004 Utility Grids Consolidated data centers Blade Servers host all applications Data is accessed and stored at consolidated storage systems Multiple application domains Traditional batch jobs Interactive Remote Desktop Sessions eg. Microsoft Terminal Servers, Citrix, VNC Data Center Thin Clients Data Center UTILITY GRID Interactive Remote Desktop Sessions Batch Jobs Blade Server Storage Servers Data Center Blade Server Blade Server

3 3 MGC 2004 Utility Grids (contd.) Key Characteristics On-demand allocation of blade servers Sharing of blade servers among multiple users Heterogeneity of resources Scale of resources, users, applications Advantages Ease of manageability Reduced costs Applications of interest Batch: Technical applications Interactive sessions: CAD/CAM, financial, office applications

4 4 MGC 2004 The Problem Design a Resource Allocation Service for Utility Grids so as to satisfy QoS performance needs of interactive remote desktop sessions allow for sharing of resources across both batch and interactive sessions while maintaining a high overall throughput of the system minimizing the wait time for the user requests

5 5 MGC 2004 Outline Introduction Proposed Solution Key Research Contributions Proposed Framework Solution to Key Research Questions Simulation Related Work Conclusions and Future Work

6 6 MGC 2004 Key Research Contributions (Summary) Architectural Guidelines for a resource allocation service supporting interactive remote desktop sessions Simulation Studies for mixed workloads in Utility Grids

7 7 MGC 2004 RESOURCE MANAGEMENT SERVER COMPUTE NODE COMPUTE NODE COMPUTE NODE SITE User’s Request and Interaction with the System 1.Top level requests for remote desktop sessions 2. Allocate a compute node for the requested remote desktop session 3. Dispatch request Remote desktop session 4. Middle level requests for starting applications within a remote desktop session Application 5. Application specific workload consisting of the users’ interaction with a particular executing application

8 8 MGC 2004 Proposed Framework STANDARDINTERFACESTANDARDINTERFACE REPOSITORY Input Queue Pending Queue Generate Remote Desktop Session Performance Model Site Admission Control Resource Assignment 1.Resource Model 2.Application Performance Models 3. Real Time Utilization data from compute nodes RESOURCE MANAGEMENT SERVER

9 9 MGC 2004 Performance Models Application Performance Model captures the resource consumption requirement per application Profile vector for an application Ai represented by Ai = {Ci, Ni, Si, L Ni, L Si } Ci – CPU utilization requirement Ni – Network bandwidth requirement (between allocated blade server and thin client) Si – Storage bandwidth requirement (between allocated blade server and remote storage servers) L Ni – Network latency requirement (between allocated blade server and thin client) L Si – Storage latency requirement (between allocated blade server and remote storage servers)

10 10 MGC 2004 Performance Models (contd.) Remote desktop session performance model Generated dynamically for the set of applications requested within a remote desktop session Function of individual resource requirements of the requested applications Multiple possible execution models A1 A3 A2 A1 A3 A2 t1 t3 t2 A1 A3 A2 t1 t3 t2 t1 t3 t2 Simultaneous – Resource requirement taken as sum of individual application requirements Sequential – Resource requirement taken as max of individual application requirements Mixed – Resource requirement taken in between the 2 extremes of simultaneous and sequential execution

11 11 MGC 2004 Site Admission Control Problem Determine the set of blade servers that can admit the requested remote desktop session Input Profile for the requested remote desktop session Current utilization of the available blade servers Policies Admission Criterion Does the available resource utilization on the blade satisfy the requested resource utilization values for the remote desktop session without violating existing allocations?

12 12 MGC 2004 Resource Assignment System Multi Variable Best Fit Algorithm Tight Bin Packing to avoid fragmentation so that time to wait in pending queue reduces Weighted Best fit algorithm along dimensions of CPU, network, storage utilization 1. Determine available CPU cycles, N/W BW, Storage BW 2. Determine delta values between available resources and the desired resources for the requested remote desktop session 3. Determine, Wc = f(Cdelta, Compute Intensiveness) WN = f(Ndelta, Average expected display data size) WS = f(Sdelta, Data intensiveness) WNL = f(NLdelta, Interactiveness) WSL = f(NSdelta, Data intensiveness) 4. Weffective = Wc + WN + WS + WNL + WSL

13 13 MGC 2004 Session Admission Control Exists at the compute node for every executing remote desktop session Problem: Determine if the remote desktop session can admit the requested application Input Resources allocated to the remote desktop session Profiles for the currently executing applications Profile for the requested application Admission Criterion Does the available resource utilization for the remote desktop session satisfy the requested resource utilization values for the application without violating existing allocations?

14 14 MGC 2004 Outline Introduction Proposed Solution Key Research Contributions Proposed Framework Solution to Key Research Questions Simulation Related Work Conclusions and Future Work

15 15 MGC 2004 Simulation Setup Request classification Heavy and Light Interactive Sessions Heavy and Light Batch Jobs Request description Day Time Experiment Heavy Interactive Session, Light Batch Job Night Time Experiment Heavy Batch Job, Light Interactive Session

16 16 Results 100 batch jobs only 200 batch jobs only 500 interactive sessions Throughput (mins) 728730716 Max Waiting Time (mins) 000 No Resource Sharing among mixed workloads (100 dedicated nodes) Day Time experiment Heavy Interactive Session, Light Batch job 100 batch jobs, 500 interactive sessions 200 batch jobs, 500 interactive sessions Throughput (mins) 728 (batch jobs) 722 (interactive sessions) 730 (batch jobs) 724 (interactive sessions) Max Waiting Time (mins) 611 Complete Resource Sharing among mixed workloads (100 shared nodes)

17 17 Results (contd.) 30 interactive sessions 200 interactive sessions 500 batch jobs Throughput (mins) 65779622 No Resource Sharing among mixed workloads (100 dedicated nodes) Night Time experiment Light Interactive Session, Heavy Batch job 30 interactive sessions, 500 batch jobs 200 interactive sessions, 500 batch jobs Throughput (mins) 65 (interactive sessions) 660 (batch jobs) 779 (interactive sessions) 688 (batch jobs) Complete Resource Sharing among mixed workloads (100 shared nodes)

18 18 MGC 2004 Outline Introduction Proposed Solution Key Research Contributions Proposed Framework Solution to Key Research Questions Simulation Related Work Conclusions and Future Work

19 19 MGC 2004 Related Work Resource Allocation for different application domains Batch jobs 3-tier applications Thin Client Systems Citrix HP CCI Clearcube

20 20 MGC 2004 Summary Presented a resource allocation architecture supports interactive remote desktop sessions considers QoS requirements of applications and remote desktop sessions Simulation studies Mixed batch jobs and interactive sessions Studied effect on throughput and wait time

21 21 MGC 2004 Future Work More simulation studies Different resource sharing strategies Evaluating tradeoffs among various weight assignments Evaluation of overall system utilization under various workloads and different resource sharing strategies Architectural enhancements Admission Control strategies Resource Assignment strategies

22 22 MGC 2004 Questions? Thank You ! Bikash Agarwalla bikash@cc.gatech.edu http://www.cc.gatech.edu/~bikash

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