GENIUS: Generator of Interactive User Media Sessions

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1 GENIUS: Generator of Interactive User Media Sessions Claudiney Ramos Cristiano Costa Ítalo Cunha Jussara M. Almeida Department of Computer Science Federal University of Minas Gerais Brazil

2 Motivation Realistic evaluation of media distribution techniques Real workloads Hard to obtain Results may be too specific Synthetic workloads More general and flexible Must build realistic streaming media workload generator

3 Streaming Media User Behavior Sequential access to whole file assumption of most previous evaluations Interactive user behavior observed in many educational workload characterizations for example, users of educational video services pause a playback to take notes or jump backwards to review a specific portion of the video Existing media workload generators limited aspects of user interactivity

4 Goals New streaming media user behavior model General and flexible Captures user interactivity and heterogeneity GENIUS: a new streaming media workload generator Parameterized from real workload characterizations WWW 04 - Analyzing client interactivity in streaming media

5 User Behavior Model Interactivity User session: a sequence of interactive requests to the same object

6 User Behavior Model Interactivity User session: a sequence of interactive requests to the same object

7 User Behavior Model Interactivity User session: a sequence of interactive requests to the same object

8 User Behavior Model Interactivity User session: a sequence of interactive requests to the same object

9 User Behavior Model Interactivity User session: a sequence of interactive requests to the same object

10 User Behavior Model Interactivity User session: a sequence of interactive requests to the same object Session components Session start position Number of interactive requests ON and OFF Times Type of interactive actions (pause, jump, fast forwarding, etc)

11 User Behavior Model Heterogeneity Object classes: defined based on characteristics that impact typical user behavior (content type, object size, bitrate) Components within each class Object Popularity User session arrival process User sessions

12 User Behavior Model Assumptions Statistical independence between components Exception: dependency between consecutive user actions within same session Simple and reasonably accurate (preliminary validation) Constant Bit Rate objects

13 GENIUS Overview Implement new user behavior model Inputs: Workload duration Workload intensity Rate at which new user sessions are generated Total number of objects Workload configuration file Define a number of object classes in target workload Define fraction of objects and sessions per class Define statistical distributions of model components within each object class

14 GENIUS Templates Object classes are independent of each other: Reproduce behavior found in specific real workload (e.g., small video files) Each class can be separately reused

15 GENIUS Templates Evaluate impact of different model components A single component or class can be modified independently

16 GENIUS Templates More heterogeneous mixed workloads Classes derived from different real workloads can be combined into a single workload configuration file

17 GENIUS Parameterization Three basic templates of configuration files Educational Video Content Entertainment Video Content Entertainment Audio Content Parameters derived from previous characterization of several real workloads

18 Main Characteristics of Basic Templates Educational video content (eteach) Duration 5-55 minutes, bitrate kbps Sessions start at arbitrary positions in the file High degree of user interactivity Entertainment video content (TV/UOL) Duration < 5 minutes, bitrate < 250kbps Other characteristics are similar to eteach Entertainment audio content (Radio/UOL) Duration under 5 minutes, bitrate < 50kbps Low interactivity

19 GENIUS Output Output: Synthetic media workload Session description file # object_id: 28 time: nreqs: PAUSE JUMP_FORWARD STOP # object_id: 18 time: nreqs: STOP Object description file # File_id Time Size Bitrate MB MB

20 GENIUS Evaluation Preliminary validation Compare statistical characteristics of synthetic workloads and corresponding real workloads Distribution of ON time Distribution of file segment popularity (indirect measure) User interactive patterns (indirect measure) Compare impact of synthetic and real workloads on different streaming media distribution techniques Scalable Bandwidth Skimming streaming protocol [EaVZ00] Randomized I/O disk placement layout [SaMR00]

21 ON Time eteach Videos in the minute range

22 Segment Popularity Radio/UOL Indirect metric that results from the collective behavior of several model

synthetic workload captures the highly interactive

23 User Interactive Patterns Real Synthetic Start/end positions of each request to a given object The synthetic workload captures the highly interactive client behavior observed in the corresponding real workload

24 Case studies: Bandwidth skimming protocol Evaluation with real objects with different popularity rank on different periods of stable arrival rate (eteach, TV/UOL, Radio/UOL) Peak server bandwidth: similar accuracy (paper)

25 Case studies: RIO Disk Placement Server with 4 disks, block size equal to 256 Kbytes Peak server bandwidth: similar accuracy (paper)

26 Conclusions and Future Work Conclusions GENIUS, a new media workload generator: Models user interactivity and workload heterogeneity It s flexible and general Captures several aspects of real workloads with impact on media distribution techniques Future Work Characterization of new workloads Extensions to include possible correlations between model components and VBR objects Further validation

Analyzing Client Interactivity in Streaming Media

Analyzing Client Interactivity in Streaming Media Cristiano Costa, Italo Cunha, Alex Borges, Claudiney Ramos, Marcus Rocha Jussara Almeida, Berthier Ribeiro-Neto Computer Science Department Akwan Information