Understanding Computer Usage Evolution
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1 Understanding Computer Usage Evolution David C. Anastasiu Department of Computer Science & Engineering University of Minnesota
2 Behavior evolves!
3 Behavior evolves!
4 Context Given various (summary) statistics related to how users use their PCs: Activity information: running applications, resource utilization, launch times, etc. System status/configuration: Goal: network type, CPU type and states, temperature, etc. model and characterize PC usage evolution. Why?
5 Outline Context of the work Modeling and characterizing the evolution of computer usage Orion: Cross-user usage segmentation Results on Intel s usage data Next steps Recap
6 Computing usage evolution What is usage? Web Productivity Media Games Idle Usage
7 time Computing usage evolution What is a usage evolution? Web Productivity Media Games Idle Usage evolution
8 Usage evolution What is characterization? Web Productivity Media Games Idle Different Users Key: common usage patterns
9 time time Characterize usage evolution We follow a segmentation based approach: Partition a user s usage sequence into disjoint consecutive sets of observations (segments) such that the usage in each segment remains fairly consistent. P1 P2 P3 P4 Usage evolution Proto evolution
10 Characterize usage evolution We follow a segmentation based approach: Partition a user s usage sequence into disjoint consecutive sets of observations (segments) such that the usage in each segment remains fairly consistent. Let be a sequence of usage vectors. A segmentation into m segments optimizes a function of the form: The proto vector captures the consistent usage during What if protos were shared among users?
11 Orion: Cross-user usage segmentation Input: Sequences of usage vectors of a set of users. A predefined number of protos. Output: A segmentation of the sequences of all users such that the error associated with modeling each segment by one of the protos is minimized.
12 Orion: Algorithmic details Iterative algorithm, whose iterations consists of two phases: Given the current set of protos, it identifies the segmentation that minimizes the total error. Given the segmentation, it identifies the protos that minimize the total error.
13 Orion: Algorithmic details (3) Initialization: The initial protos are determined by performing a K-means clustering of all usage vectors across all users. Robustness: Minimum length constraints on each segment. A penalty associated with the creation of each additional segment within a user s sequence. A segment is allowed to be created if it leads to a userspecified reduction in the approximation error.
14 Orion: Model assumptions The different users exhibit a rather small number of prototypical usage behaviors that are captured by the protos. The usage behavior of users remains consistent over a certain period. The usage behavior of users can change from one prototypical behavior to another. proto#:duration
15 DATA
16 Intel data Users systems provide Intel servers with: Daily summary application usage statistics Execution start and end time CPU time Number of page faults Geo-location (at the country level) System type CPU type OS first start date 7.52 B initial records, aggregated to 2.13 B weekly Much noise, e.g B records with 0 utilization
17 Data filtering App filtering: Removed unknown, system, and internet apps Removed records with < 60s/week utilization Removed apps with < 2K records User filtering: Kept users with > 5/week utilizations in > 20 weeks # users # apps 762 # records 11.05M
18 We only present results for analyzing the dataset using 15 protos. RESULTS
19 Prototypical behaviors (protos) Work/productivity related behaviors #usage vectors P2 (32K) Media creation P3 (31K) & office P4 (106K) Business communication P9 (83K) Writer P10 (105) Office
20 Prototypical behaviors (protos) Asian media & social related behaviors P7 (22K) Asian media downloads P8 (31K) Asian messenger
21 Prototypical behaviors (protos) Media & social related behaviors P11 (72K) itunes P0 (37K) Communicate & watch P1 (83K) File transfers P5 (48K) Media downloads P6 (105K) Media player P14 (71K) Facebook Messenger P12 (115K) Skype
22 Prototypical behaviors (protos) Gaming P13 (35K) Gaming`
23 Proto evolution
24 Proto transitions Office Business Communication
25 Proto evolution S Start 0 Communicate & watch movies 1 File transfers 2 Media creation 3 /Office 4 Business communication 5 Media downloads 6 Media player 7 Asian media downloads 8 Asian messenger 9 Writer 10 Office 11 itunes 12 Skype 13 Gaming 14 Facebook Messenger E End
26 Proto evolution P4 (106K) Business communication P10 (105K) Office
27 Proto evolution P0 (37K) Communicate & watch P6 (105K) Media player Tend to be interior protos
28 Side information correlation System Type NA Netbook Ultraportable Premium Multimedia Everyday Consumer All-in-One CPU Type All Office P10 (105K) Office Penryn-Pent-Cel gen2-pent-cel gen1-pent-cel Other Core2Duo Atom gen3-i7 gen2-i7 gen2-i5 gen2-i3 gen1-i7 gen1-i5 gen1-i All Office
29 Side information correlation
30 Side information correlation Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa All Facebook Messenger P14 (71K) Facebook Messenger System Type NA Netbook Ultraportable Premium Multimedia Everyday Consumer All-in-One All Facebook Messenger
31 Future directions Model sub-application classes: Explore approaches based on dimensionality reduction. This can be done within the context of Orion s cross-user segmentation Lower-dimensional protos should still be interpretable. Generalize the segment s properties assumptions: Instead of assuming that the usage in each segment is constant, what if we assume that the usage can be predicted based on previous within-segment behavior?
32 Recap Behavior evolves! Orion provides a way to analyze population behavior evolution Identifies common patterns of behavior (protos) Translates user behavior into sequences of protos Orion is versatile, applicable to diverse multivariate time-series domains
33 Orion source Q & A Royalty-free Images from Wikimedia.org and morguefile.com.
34 BACKUP SLIDES
35 Orion: Algorithmic details (2) Segmentation identification: Uses a dynamic-programming algorithm to find the optimal segmentation. Complexity: O(#users x μ 2 x #protos). Optimal proto identification: The mean of the usage vectors spanned by the proto.
36 Data filtering 7.52 B initial records, aggregated to 2.13 B weekly Most records within 100 week time span Most users have records for at least 50 weeks Much noise, e.g B records with 0 utilization Focused analysis on subset of users/applications
37 Proto evolution P2 (32K) Media creation P8 (31K) Asian messenger Protos with low (blue box) and high (red box) fan-out
38 Side information correlation Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa CPU Type All Asian messenger P8 (31K, 204) Asian messenger Penryn-Pent-Cel gen2-pent-cel gen1-pent-cel Other Core2Duo Atom gen3-i7 gen2-i7 gen2-i5 gen2-i3 gen1-i7 gen1-i5 gen1-i All Asian messenger
39 Side information correlation System Type NA Netbook Ultraportable Premium Multimedia Everyday Consumer All-in-One All Media Creation P2 (32K, 211) Media creation
40 Proto evolution P1 (83K, 238) File transfers P9 (83K, 239) Writer P11 (72K, 243) itunes P6 (105K, 85) Media player P12 (115K, 195) Skype Protos with high fan-in
41 LESSONS LEARNED
42 Lessons learned (1) We had to eliminate all web-browsing related applications in order to get meaningful protos With browsers in, the protos and their transitions were dominated by users switching between different browsers. A large chunk of user activity is lost. Need visibility into what the users are doing with their browsers to properly model/analyze this aspect of user behavior.
43 Lessons learned (2) Granularity of usage: Application vs. application class Each application was mapped by Intel to one of 11 application classes. Our early attempts to represent a user s usage in terms of application classes did not produce very encouraging results. We need to see how the Orion approach performs with that representation. Application-level representation fails to model usage of application subclasses for which there are not dominating applications. Users use a large number of applications to perform essentially the same task. Need to identify these scenarios and create sub-application classes to group them by. A middle ground between the individual applications and the 11 top-level classes.
44 Lessons learned (3) Data cleaning We ended up spending a large amount of time mapping across different versions of the same application: Locale specific executable names Executable names with embedded version numbers There is a need to map the different executables that are running in the context of a single application into a unique application ID: background processes, daemons, auxiliary programs, installers, servers, clients, etc. This is also related to the granularity issue discussed earlier.
45 Proto transitions P6 (105K, 85) Media player P12 (115K, 195) Skype
46 Proto transitions P7 (22K, 384) Asian media downloads P8 (31K, 204) Asian messenger
47 Proto transitions P0 (37K, 356) Communicate & watch P5 (48K, 242) Media downloads
48 Side information correlation Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa S=>P P1 (83K, 238) File transfers P12 (115K, 195) Skype Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa All File Transfers P12=>P Skype File Transfers
49 Side information correlation Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa CPU Type Media downloads itunes P5 (48K, 242) Media downloads P11 (72K, 243) itunes Penryn-Pent-Cel gen2-pent-cel gen1-pent-cel Other Core2Duo Atom gen3-i7 gen2-i7 gen2-i5 gen2-i3 gen1-i7 gen1-i5 gen1-i Media downloads itunes
50 Side information correlation P7: Asian media downloads Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa All Asian media downloads
51 Side information correlation System Type P4: Business communication NA Netbook Ultraportable Premium Multimedia Everyday Consumer All-in-One All Business Communication
52 Side information correlation Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa P13: Gaming P12: Skype CPU Type Penryn-Pent-Cel gen2-pent-cel gen1-pent-cel Other Core2Duo Atom gen3-i7 gen2-i7 gen2-i5 gen2-i3 gen1-i7 gen1-i5 gen1-i3 Gaming Skype Gaming Skype
53 Side information correlation P6: Media player P14: Facebook Messenger Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa Media player Facebook Messenger
54 Side information correlation Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa Geolocation File transfers Communicate & watch P1: File transfers P0: Communicate/ watch P5: Media downloads US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa File transfers Media downloads
55 Side information correlation Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa P12: Skype P1: File transfers Geolocation US Turkey Thai Russia Latin Intra India IE EU Canada Brazil Arabic Africa All File Transfers Skype File Transfers
56 Prototypical behaviors (protos) P0 (37K, 356) Communicate & watch P1 (83K, 238) File transfers P2 (32K, 211) Media creation P3 (31K, 231) & office P4 (106K, 364) Business communication P6 (105K, 85) Media player P5 (48K, 242) Media downloads P9 (83K, 239) Writer P7 (22K, 384) Asian media downloads P12 (115K, 195) Skype
57 Prototypical behaviors (protos) P8 (31K, 204) Asian messenger P10 (105K, 249) Office P13 (35K, 557) Gaming P11 (72K, 243) itunes P14 (71K, 296) Facebook Messenger
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