The User In Experimental Computer Systems Research Peter A. Dinda Gokhan Memik, Robert Dick Bin Lin, Arindam Mallik, Ashish Gupta, Sam Rossoff Department of Electrical Engineering and Computer Science Northwestern University

2 Experimental Computer Systems Researchers Should… Incorporate user studies into the evaluation of systems Incorporate direct user feedback into the design of systems

3 Experimental Computer Systems Researchers Should… Incorporate user studies into the evaluation of systems –No such thing as the typical user –Really measure user satisfaction Incorporate direct user feedback into the design of systems –No such thing as the typical user –Measure and leverage user variation

4 Outline Prescription Experiences with user studies and direct user feedback –User comfort with resource borrowing –User-driven scheduling of interactive VMs –User satisfaction with CPU frequency –User-driven frequency scaling –User-driven control of distributed virtualized envs. –Prospects for speculative remote display Principles for client/server context General advice

5 Experiences in Detail Concepts : ExpCS FCRC Specific Projects –User comfort with resource borrowing HPDC 2004, NWU-CS –User-driven scheduling of interactive VMs Grid 2004, SC 2005, VTDC 2006, NWU-EECS –User satisfaction with CPU frequency CAL 2006, SIGMETRICS 2007, NWU-EECS –User-driven frequency scaling (/process-driven voltage scaling) CAL 2006, SIGMETRICS 2007, NWU-EECS –User-driven control of distributed virtualized envs. Portion of Bin Lin’s thesis, see also ICAC 2007 –Prospects for speculative remote display NWU-EECS-06-08

6 User Comfort With Resource Borrowing Systems that use “spare” resources on desktops for other computation Condor on desktops, etc. How much can they borrow before discomforting user? –Inverse: How much must desktop replacement system give?

7 User Comfort With Resource Borrowing Developed system for controlled resource borrowing given a profile –CPU contention, disk BW contention, physical memory pages –User presses “irritation button” to stop User study –38 participants –Four apps Word, Powerpoint, Web browsing, Game –Ramp, Step, Placebo profiles –Double blinded

8 Example Result Massive Variation in User Response

9 User-driven Scheduling of Interactive VMs Virtual machine-based desktop replacement model –VM runs on backend server –User connects with remote display –VM is scheduled according to periodic real- time model Allows straightforward mixing of batch and interactive VMs + isolation properties What should interactive VM’s schedule be?

10 User-driven Scheduling of Interactive VMs VSched scheduler on server User interface on client Non-centering joystick allows user to set schedule $10 interface in study Cheaper interfaces possible Onscreen display indicates price of current schedule Also indicates when schedule cannot be admitted

11 User-driven Scheduling of Interactive VMs User study –18 participants –4 applications Word, Powerpoint, Internet browsing, Game –Survey response + measurement Deception scheme to control bias in survey response Results –Almost all could find a setting that was comfortable –Almost all could find a setting that was comfortable and believed to be of lowest cost –Lowest cost highly variable, as expected given previous results –<1 minute convergence typical Interface captures individual user tradeoffs –Fewer cycles for tolerant users –More cycles for others

12 User Satisfaction With CPU frequency Modern processors can lower frequency to reduce power consumption –Software control: DVFS - conservative How satisfied are users of different applications at different clock frequencies? User Study –8 users –3 frequencies + Windows DVFS –3 apps Presentation, Animation, Game –Rate comfort on 1 to 10 scale –Double-blinded

13 Example Results Dramatic variation in user satisfaction for fixed frequencies –And for DVFS Presentation Game

14 User-driven Frequency Scaling Developed system to dynamically customize frequency to user –User presses “irritation button” as input –2 very simple learning algorithms User study –20 participants –Three apps Powerpoint, Animation, Game –Comparison with Windows DVFS –Double blinded

15 Example Results (Measured System Power) Users % gain over Windows DVFS Users % gain over Windows DVFS Powerpoint Game

16 Outline Prescription Experiences with user studies and direct user feedback –User comfort with resource borrowing –User-driven scheduling of interactive VMs –User satisfaction with CPU frequency –User-driven frequency scaling –User-driven control of distributed virtualized envs. –Prospects for speculative remote display Principles for client/server context General advice

17 Principles for the Client/Server Context User variation –Considerable variation in user satisfaction with any given operating point –No such thing as a typical user User-specified performance –Have user tell system software how satisfied he is –No decoupling of user response from user and OS-level measurements Think global feedback Thin, simple user-system interface –One bit is a lot of information compared to zero Learning to decrease interaction rate –Model the individual user

18 Outline Prescription Experiences with user studies and direct user feedback –User comfort with resource borrowing –User-driven scheduling of interactive VMs –User satisfaction with CPU frequency –User-driven frequency scaling –User-driven control of distributed virtualized envs. –Prospects for speculative remote display Principles for client/server context General advice

19 General Advice for Evaluating Systems with User Studies Consult an HCI or psychology expert –User studies are different but not impossible –At least consult the literature Engage your IRB early –These are “social science”-based studies –Easier the second time around Accept small study size –Parameter sweeps, hundreds of traces impossible –Internet volunteerism not especially effective –Use non-user studies to augment if possible –Robust statistics

20 General Advice for Evaluating Systems with User Studies Accept that random sample unlikely –Selection bias estimation, if possible –Report all your data, not just summaries Histogram instead of curve fit Measure the noise floor / placebo effect –Vital to determine how much of user satisfaction is actually under your control Double-blind to greatest extent possible –Investigator bias and subject bias

21 General Advice for Evaluating Systems with User Studies Correlate system-level measurements with user responses to validate the latter –Consider deception when this is impossible Eliminate user-visible extraneous information during any study –What the user knows can hurt you Example: disk light

22 General Advice for Incorporating Direct User Feedback Out-of-band devices work best –Avoid cognitive context switch Use as little input as possible –One bit is much more information than zero –Utility of input may not be clear to user Output as little information as possible Minimize input rate through learning Bridge explicit feedback to implicit feedback when possible

23 Experimental Computer Systems Researchers Should… Incorporate user studies into the evaluation of systems –No such thing as the typical user –Really measure user satisfaction Incorporate direct user feedback into the design of systems –No such thing as the typical user –Measure and leverage user variation

24 For More Information Peter Dinda – Prescience Lab –

25 User-driven Control of Distributed Virtual Environments Area of current exploration (part of Lin’s thesis) Idea: Can we frame these problems as games that naïve or expert users/admins can solve? Initial results interesting, but still too early too tell –Scaling –Dimensionality –Categorical dimensions –…

26 Typical Design Models Optimize User Satisfaction Subject to Constraints Systems software’s decisions have dramatic effect on user experience But how does systems software know how well it is doing? Systems Software Application(s) Satisfaction with System/App Combination Individual User Interface Considerations Resource Management and Scheduling Considerations Core API

27 Typical Design Models Optimize User Satisfaction Subject to Constraints One option: let the application tell it! But how does the application know? Systems Software Application(s) Satisfaction with System/App Combination Individual User Interface Considerations Resource Management and Scheduling Considerations Core APIPolicy API

28 Typical Design Models Optimize User Satisfaction Subject to Constraints One option: let the application tell it! Assume typical user and apply general rules derived from him/her –And figure out how to translate to the policy API Systems Software Application(s) Satisfaction with System/App Combination Typical User Interface Considerations Resource Management and Scheduling Considerations Core APIPolicy API <500 ms latency and <100 ms jitter

29 Typical Design Models Optimize User Satisfaction Subject to Constraints One option: let the application tell it! Or formalize tradeoffs –And figure out how to translate to the policy API Systems Software Application(s) Satisfaction with System/App Combination Typical User Interface Considerations Resource Management and Scheduling Considerations Core APIPolicy API Utility Function Latency Satisfaction

30 Typical Design Models Optimize User Satisfaction Subject to Constraints Another option: generalize over applications and infer user experience Systems Software Application(s) Satisfaction with System/App Combination Typical User Resource Management and Scheduling Considerations Core API Latency Satisfaction Interface Considerations Inferred Latency Good/Bad?

31 Typical Design Models Optimize User Satisfaction Subject to Constraints Another option: Get the utility function right from the individual user –Assuming he/she knows it… Systems Software Application(s) Satisfaction with System/App Combination Individual User Resource Management and Scheduling Considerations Core API Interface Considerations Systems Software Application(s) Resource Management and Scheduling Considerations Policy Interface “What’s a utility function?” “What is your utility function?” or “Which of these profiles are you most like?”

32 Typical Design Models Optimize User Satisfaction Subject to Constraints Another option: Expose the system software to the user in its glory details –Works great for us! Systems Software Application(s) Satisfaction with System/App Combination Individual User Resource Management and Scheduling Considerations Core API Interface Considerations Systems Software Application(s) Resource Management and Scheduling Considerations Policy Interface “What the…”

33 Typical Evaluation Approaches Workloads –User workload model/generator How to account for user variation? How to evaluate as closed system? How to validate? –User traces Context dependent How to evaluate as closed system?

34 Typical Evaluation Approaches Metrics –Can system meet performance objectives given through policy interface? What should the objectives be? –Can system optimize over some combination of utility functions? What should the utility functions be?

35 New Model for Characterization and Evaluation User studies to characterize user response –Examine the range of user satisfaction for some perceivable quantity or combination of quantities –Capture the variation, not only the mean –Variation = opportunity User studies for evaluating systems –Directly measure user satisfaction with your system

36 New Model: Direct User Feedback Optimize User Satisfaction Subject to Constraints User conveys satisfaction (or dissatisfaction) through a simple user interface Systems Software Application(s) Satisfaction with System/App Combination Individual User Resource Management and Scheduling Considerations Core API Interface Considerations Systems Software Application(s) Resource Management and Scheduling Considerations Satisfaction Feedback

37 New Model: Direct User Feedback Optimize User Satisfaction Subject to Constraints User has some direct control over systems- level decision making through a simple interface Systems Software Application(s) Satisfaction with System/App Combination Individual User Resource Management and Scheduling Considerations Core API Interface Considerations Systems Software Application(s) Resource Management and Scheduling Considerations Some Control Over Decision Making

38 User-driven Control of Distributed Virtual Environments Virtuoso project (see virtuoso.cs.northwestern.edu) –User “rents” collection of virtual machines Virtuoso front-end looks like computer vendor –Providers stand up resources on which VMs can run or communicate –Virtuoso provides adaptation mechanisms VM migration Overlay topology and routing (VNet) CPU reservations (VSched) Network reservations (optical with VReserve) Transparent network services (VTL) –Virtuoso provides inference mechanisms Application traffic and topology (VTTIF) Network bandwidth and latency (Wren)

39 User-driven Control of Distributed Virtual Environments Optimization problem: Given the inferred demands and supply, choose a configuration made possible by the adaptation mechanisms that maximizes a measure of application performance within constraints –Formalizations –NP-Hard problem in general –Approximation bound is not great either –Heuristic solutions Can the user or a system administrator solve these problems given the right interface? –Can a naïve human do it?