Configuration of distributed message converter systems

Finding a configuration of a distributed system satisfying performance goals is a complex search problem that involves many design parameters, like hardware selection, job distribution and process configuration. Performance models are a powerful tool to analyze potential system configurations, however, their evaluation is expensive, such that only a limited number of possible configurations can be evaluated. In this paper we present a systematic method to find a satisfactory configuration with feasible effort, based on a two-step approach. First, performing a queuing network analysis a hardware configuration is determined and then a software configuration is incrementally optimized by simulating Layered Queuing Network models. We applied this method to the design of performant EDI converter systems in the financial domain, where increasing message volumes need to be handled due to the growing importance of B2B interaction.

[1]  Edward D. Lazowska,et al.  Quantitative System Performance , 1985, Int. CMG Conference.

[2]  C. Murray Woodside,et al.  Performance analysis of distributed server systems , 2000 .

[3]  Larry Kerschberg,et al.  A software architectural design method for large-scale distributed information systems , 1996, Distributed Syst. Eng..

[4]  C. Murray Woodside,et al.  Layered analytic performance modelling of a distributed database system , 1997, Proceedings of 17th International Conference on Distributed Computing Systems.

[5]  Shikharesh Majumdar,et al.  Software Bootlenecking in Client-Server Systems and Rendezvous Networks , 1995, IEEE Trans. Software Eng..

[6]  Daniele Vigo,et al.  Bin Packing Approximation Algorithms: Combinatorial Analysis , 1999, Handbook of Combinatorial Optimization.

[7]  Edward G. Coffman,et al.  Approximation algorithms for bin packing: a survey , 1996 .

[8]  Yossi Azar,et al.  On-line bin-stretching , 1998, Theor. Comput. Sci..

[9]  C. Murray Woodside,et al.  An "Active Server" model for the performance of parallel programs written using rendezvous , 1986, J. Syst. Softw..

[10]  Virgílio A. F. Almeida,et al.  Capacity Planning and Performance Modeling: From Mainframes to Client-Server Systems , 1994 .

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

[12]  János Csirik An on-line algorithm for variable-sized bin packing , 2004, Acta Informatica.

[13]  Karl Aberer,et al.  Configuration of distributed message converter systems using performance modeling , 2001, Conference Proceedings of the 2001 IEEE International Performance, Computing, and Communications Conference (Cat. No.01CH37210).

[14]  Karl Aberer,et al.  Online Scheduling in Distributed Message Converter Systems , 2001 .

[15]  Michael L. Fontenot Software Congestion, Mobile Servers, and the Hyperbolic Model , 1989, IEEE Trans. Software Eng..

[16]  Karl Aberer,et al.  Efficient Processing of Voluminous EDI Documents , 2000, ECIS.

[17]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[18]  C. Murray Woodside Throughput Calculation for Basic Stochastic Rendezvous Networks , 1989, Perform. Evaluation.

[19]  Jerome A. Rolia,et al.  A Toolset for Performance Engineering and Software Design of Client-Server Systems , 1995, Perform. Evaluation.

[20]  Dorina C. Petriu,et al.  Applying performance modelling to a telecommunication system , 1998, WOSP '98.

[21]  Sarah Williams,et al.  Computer applications , 1988 .

[22]  Larry Kerschberg,et al.  A performance oriented design methodology for large-scale distributed data intensive information systems , 1995, Proceedings of First IEEE International Conference on Engineering of Complex Computer Systems. ICECCS'95.

[23]  Edward D. Lazowska,et al.  Quantitative system performance - computer system analysis using queueing network models , 1983, Int. CMG Conference.

[24]  Frank D. Murgolo An Efficient Approximation Scheme for Variable-Sized Bin Packing , 1987, SIAM J. Comput..

[25]  Klaus H. Ecker,et al.  Scheduling Computer and Manufacturing Processes , 2001 .

[26]  Edward G. Coffman,et al.  Approximation Algorithms for Extensible Bin Packing , 2001, SODA '01.

[27]  Hesham El-Sayed,et al.  Automation support for software performance engineering , 2001, SIGMETRICS '01.

[28]  P. Pardalos,et al.  Handbook of Combinatorial Optimization , 1998 .

[29]  Ramesh Subramonian,et al.  LogP: towards a realistic model of parallel computation , 1993, PPOPP '93.

[30]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[31]  Vikram S. Adve,et al.  Analyzing the behavior and performance of parallel programs , 1993 .

[32]  Stephen F. Lundstrom,et al.  Predicting Performance of Parallel Computations , 1990, IEEE Trans. Parallel Distributed Syst..

[33]  D. Atkin OR scheduling algorithms. , 2000, Anesthesiology.

[34]  YONG YAN,et al.  An Effective and Practical Performance Prediction Model for Parallel Computing on Nondedicated Heterogeneous NOW , 1996, J. Parallel Distributed Comput..

[35]  E. L. Lawler,et al.  Branch-and-Bound Methods: A Survey , 1966, Oper. Res..

[36]  R. F. Brown,et al.  PERFORMANCE EVALUATION , 2019, ISO 22301:2019 and business continuity management – Understand how to plan, implement and enhance a business continuity management system (BCMS).

[37]  Jerome A. Rolia,et al.  The Method of Layers , 1995, IEEE Trans. Software Eng..

[38]  Dorina C. Petriu Approximate Mean Value Analysis of Client-Server Systems with Multi-class Requests , 1994, SIGMETRICS.

[39]  Xiaodong Zhang,et al.  Erratum: "An Effective and Practical Performance Prediction Model for Parallel Computing on Nondedicated Heterogeneous NOW" , 1997, J. Parallel Distributed Comput..