Web Services Resilience Evaluation using LDS Load dependent Server Models

In the field of ICT, the term resilience is commonly used, understanding to mean the ability of a system to deliver acceptable service in the presence of faults. We interpret resilience assessment to mean assessment of ICT systems in evolving environments and conditions and in the presence of faults or failures of any kind. Resilience Measurement and benchmarking is an open problem, mainly referred to security-related problems. In this paper we propose to model Web Services (WS)as Load Dependent Servers (LDS), i.e. systems whose response time to a given request depends on the load at the time the request is received. Adoption of LDS-based models enable us to have a simple way to represent the System state. We propose to use this simple representation to quantify the system resilience comparing models built using off-line measurement and models built using on-line measurement.

[1]  Sang Hyuk Son,et al.  Feedback Control Architecture and Design Methodology for Service Delay Guarantees in Web Servers , 2006, IEEE Transactions on Parallel and Distributed Systems.

[2]  Edward D. Lazowska,et al.  Incorporating load dependent servers in approximate mean value analysis , 1984, SIGMETRICS '84.

[3]  Jing Xu,et al.  Performance modeling and prediction of enterprise JavaBeans with layered queuing network templates , 2006, ACM SIGSOFT Softw. Eng. Notes.

[4]  K. Shin,et al.  Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach , 2002, IEEE Trans. Parallel Distributed Syst..

[5]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[6]  Shikharesh Majumdar,et al.  The Stochastic Rendezvous Network Model for Performance of Synchronous Client-Server-like Distributed Software , 1995, IEEE Trans. Computers.

[7]  Guy Pujolle,et al.  Analysis of a queueing network model with class dependent window flow control , 1992, [Proceedings] IEEE INFOCOM '92: The Conference on Computer Communications.

[8]  Magnus Karlsson,et al.  Dynamic Black-Box Performance Model Estimation for Self-Tuning Regulators , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[9]  Markus Voelter,et al.  State of the Art , 1997, Pediatric Research.

[10]  Brendan Murphy,et al.  Progress on Defining Standardized Classes for Comparing the Dependability of Computer Systems , 2002 .

[11]  Yixin Diao,et al.  Feedback Control of Computing Systems , 2004 .

[12]  Paola Inverardi,et al.  Model-based performance prediction in software development: a survey , 2004, IEEE Transactions on Software Engineering.

[13]  Dorina C. Petriu,et al.  The Future of Software Performance Engineering , 2007, Future of Software Engineering (FOSE '07).

[14]  C. Murray Woodside.  The Relationship of Performance Models to Data , 2008, SIPEW.

[15]  Vipul Mathur,et al.  A computational complexity-aware model for performance analysis of software servers , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[16]  C. Murray Woodside,et al.  A Calibration Framework for Capturing and Calibrating Software Performance Models , 2008, EPEW.

[17]  Henrique Madeira.  Assessing, Measuring, and Benchmarking Dependability and Resilience , 2007, LADC.

[18]  Ramón Puigjaner,et al.  Using Load Dependent Servers to Reduce the Complexity of Large Client-Server Simulation Models , 2001, Performance Engineering.