Characterizing Web Workloads For Quality of Service Prediction

• US 20130007259A1
• Filed: 07/01/2011
• Published: 01/03/2013
• Est. Priority Date: 07/01/2011
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Casale, Giuliano, Pacheco-Sanchez, Sergio
• Granted Patent: US 8,560,618 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 2201/875 Monitoring of systems including the internet
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/147 for predicting network behaviour

Abstract

Implementations of the present disclosure provide systems and methods directed to receiving, at a computing device, trace data provided in a trace log file corresponding to a server, parameterizing, using the computing device, a first Markovian Arrival Process (MAP) model based on the trace data, parameterizing, using the computing device, a second MAP model based on the trace data, defining a queuing model that models a performance of the server and that includes the first MAP model and the second MAP model, and storing the queuing model in computer-readable memory.

Claims

1. A computer-implemented method for generating a queuing model, comprising:

receiving, at a computing device, trace data provided in a trace log file corresponding to a server;

parameterizing, using the computing device, a first Markovian Arrival Process (MAP) model based on the trace data;

parameterizing, using the computing device, a second MAP model based on the trace data;

defining a queuing model that models a performance of the server and that includes the first MAP model and the second MAP model; and

storing the queuing model in computer-readable memory.

2. The method of claim 1, wherein the trace data comprises data corresponding to a plurality of requests arriving at the server.

3. The method of claim 2, wherein each request of the plurality of requests is one of a static request and a dynamic request.

4. The method of claim 1, wherein parameterizing the first MAP model is based on request inter-arrival times.

5. The method of claim 4, wherein the request inter-arrival times are computed based on timestamps provided in the trace data.

6. The method of claim 4, wherein a request inter-arrival time is provided as a time elapsed between successive requests received at the server.

7. The method of claim 4, further comprising removing sequences of zero inter-arrival times from the inter-arrival times that have been requested to provide an adjusted set of inter-arrival times.

8. The method of claim 7, wherein parameterizing the first MAP model is based on the adjusted set of inter-arrival times.

9. The method of claim 1, wherein parameterizing the first MAP model is achieved using a maximum likelihood fitting method.

10. The method of claim 1, wherein parameterizing the second MAP model is based on a plurality of service times, the service times being determined from the trace data.

11. The method of claim 10, further comprising determining the service times based on whether each of the plurality of requests is one of a static request and a dynamic request.

12. The method of claim 10, wherein a service time is provided as a time that the server spends servicing a request.

13. The method of claim 10, further comprising aggregating the service times.

14. The method of claim 1, further comprising splitting the trace data into blocks.

15. The method of claim 1, further comprising calculating one or more performance metrics using the queuing model.

16. The method of claim 15, wherein the one or more performance metrics comprise one or more of a tail distribution of the plurality of requests, a mean queue length, and a mean response time of system requests.

17. The method of claim 15, further comprising:

receiving user input at the computing device; and

providing the user input as input to the queuing model, wherein the one or more performance metrics are calculated based on the user input.

18. The method of claim 1, wherein the server comprises a web server.

19. A computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

receiving trace data provided in a trace log file corresponding to a server;

parameterizing a first Markovian Arrival Process (MAP) model based on the trace data;

parameterizing a second MAP model based on the trace data;

defining a queuing model that models a performance of the server and that includes the first MAP model and the second MAP model; and

storing the queuing model in computer-readable memory.

20. A system, comprising:

one or more processors; and

a computer-readable storage medium coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising;

receiving trace data provided in a trace log file corresponding to a server;

parameterizing a first Markovian Arrival Process (MAP) model based on the trace data;

parameterizing a second MAP model based on the trace data;

defining a queuing model that models a performance of the server and that includes the first MAP model and the second MAP model; and

storing the queuing model in computer-readable memory.