1. 1 Automatic Request Categorization in Internet Services Abhishek B. Sharma (USC) Collaborators: Ranjita Bhagwan (MSR, India) Monojit Choudhury (MSR, India) Leana Golubchik (USC) Ramesh Govindan (USC) Geoffrey M. Voelker (UCSD)

2. 2 Web Workload Characterization Static Dynamic Web + Application ServerDatabase Request categories based on resource usage characteristics. Better performance modeling accuracy (Stewart et al. 2007).

3. 3 Workload with multiple categories: What do we need to know? 1.Request Categories 2. Arrival rates/processes 3. Per-category resource usage at each service tier

4. 4 Workload Characterization: Instrumentation-based techniques Request Categories Arrival rates/processes Per-category resource usage at each service tier PinPoint Magpie Requires middleware / OS level instrumentation Sophisticated monitoring and logging infrastructure

5. 5 Workload Characterization: Inference-based techniques Easier to obtain “aggregate” measurements : –aggregate resource utilization –information from server logs Assume: request categories correspond to “high level” transactions (login, checkout, write a review) Techniques based on: Least-Squares Regression, Convex Optimization, … Per-category resource consumption at each tier Response times given the system load

6. 6 “Hot” Question ? Is there an inference technique that does not assume knowledge of the request categories? If yes, then –do not need invasive instrumentation –can infer the request categories and the workload mix. –can infer the service times (resource consumptions) for each request category

7. 7 Our Approach Two key elements. A linear model for aggregate resource consumption. Use Blind Source Separation methods to infer the request categories and their resource consumption characteristics.

8. 8 A Linear Model for Resource Usage Linear Model for aggregate resource consumption: System of equations for n resources, m categories and T measurements, AX + C = R n = 2, m = 2, T = 2 Aggregate resource usage# requests of category 1 Resource consumed by one request + = (n x m)(m x T)(n x 1)(n x T) “Noise”

9. 9 System of equations: AX + C = R Known: R from our measurements If we knew X –Solve for A and C using OLS (Zhang et al. ICAC’07), LAR (Stewart et al. EuroSys’07) But A, C and X are the unknowns! If the linear model holds, and n ≥ m, we can estimate A and X, given R –Equivalent to matrix factorization: AX = R –Also known as Blind Source Separation Equivalent to Matrix Factorization!

10. 10 Equivalent to Matrix Factorization! If the linear model holds, and n ≥ m, we can estimate A and X, given R –Equivalent to matrix factorization: AX = R –Also known as Blind Source Separation What about the “noise” terms C ? –Filter it out ! (We take this approach) –Can be accounted for. added complexity

11. 11 Assumptions Arrivals for different categories are mutually uncorrelated. But uncorrelated constraint is not enough! –Infinitely many solutions for A. Independent Component Analysis (ICA): –Statistically independent components –Maximizes kurtosis, negentropy –Works only when “source signals” are non-Gaussian Other techniques: –Principal Component Analysis (PCA) –Canonical Correlation Analysis (CCA)

12. 12 Methodology We use ICA. –FastICA package (Thank you, Hyvarinen !) PCA used to pre-process R. Evaluation: E-commerce workload –An online store: “Browsing” and “Shopping” sessions –Tools: Microsoft PetShop 4.0 –Visual Studio Test Suite (for Client Emulation) Testbed: –Microsoft IIS 6.0 (web server) –Microsoft SQL Server 2005 (database)

13. 13 Results: Request Categories & their resource usage We compare ICA estimates against OLS regression estimates because we do not have the ground truth ! I II Do these categories correspond to something?

14. 14 Results: Request Arrivals Avg. Percentage Error: Browsing 11%, Shopping 17%

15. 15 Is the Linear Model valid? At best, approximate fit Conjecture: Not valid when –Heavily load Does not capture? –Interaction effects across requests. –Autocorrelation in resource consumption. –More severe in heavily loaded system Stewart et al. (Eurosys’07), Mi et al. (Performance’07)

16. 16 What about the ICA assumptions? 1.ICA assumes non-Gaussianity and Independence. 2.All BSS methods assumes mutually uncorrelated “sources” non-Gaussianity: –Per-second aggregate arrivals are non-Gaussian –Sampling interval should not be too large (~ 10 min) Independence/mutually uncorrelated: –May not always hold. –In practice, FastICA finds as independent components as possible.

17. 17 Future Work 1.Determining m, the number of categories ? 2.Effect of noise –FastICA is known to be robust against noise. –“Noisy” ICA 3.Which request belongs to which category? –Goldszmidt et al. (EC’01) 4.How do results from CCA compare against ICA? –“Autocorrelated” constraint might be less restrictive than “independence” constraint in the real world. 5.Evaluation using benchmarks—TPC-W, RuBiS, real system traces. 6.Use queueing network models to tie our workload characterization work with end user quality of service.

18. 18 Thank you