Performance analysis for multimedia communication systems with a multilayer queuing network model

Software performance evaluation in multimedia communication systems is typically formulated into a multi-layered client-server queuing network (MLCSQN) problem. However, the existing analytical methods to MLCSQN model cannot provide satisfactory solution in terms of accuracy, convergence and consideration of interlocking effects. To this end, this paper proposes a heuristic solving method for MLCSQN model to boost the performance prediction of distributed multimedia software systems. The core concept of this method is referred to as the basic model, which can be further decomposed into two sub-models: client sub-model and server sub-model. The client sub-model calculates think time for server sub-model, and the server sub-model calculates waiting time for client sub-model. Using a breadth-first traversal from leaf nodes to the root node and vice versa, the basic model is then adapted to MLCSQN, with net sub-models iteratively resolved. Similarly, the interlocking problem is effectively addressed with the help of the basic model. This analytical solver enjoys advantages of fast convergence, independence on specific average value analysis (MVA) methods and eliminating interlocking effects. Numerical experimental results on accuracy and computation efficiency verify its superiority over anchors.

[1]  Melanie Keller Computer Networks And Systems Queuing Theory And Performance Evaluation , 2016 .

[2]  Giuliano Casale An efficient algorithm for the exact analysis of multiclass queueing networks with large population sizes , 2006, SIGMETRICS '06/Performance '06.

[3]  Sridhar Ramesh,et al.  A Multilayer Client-Server Queueing Network Model with Synchronous and Asynchronous Messages , 2000, IEEE Trans. Software Eng..

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

[5]  Stephen S. Lavenberg,et al.  Mean-Value Analysis of Closed Multichain Queuing Networks , 1980, JACM.

[6]  Liang Zhou,et al.  QoE-Driven Delay Announcement for Cloud Mobile Media , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Liang Zhou,et al.  On Data-Driven Delay Estimation for Media Cloud , 2016, IEEE Transactions on Multimedia.

[8]  Xin Yu,et al.  Performance analysis of opportunistic scheduling in wireless multimedia and data networks using stochastic network calculus , 2013, Multimedia Tools and Applications.

[9]  Olivia Das,et al.  Web Application Performance Modeling Using Layered Queueing Networks , 2011, PASM@ICPE.

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

[11]  Steffen Becker,et al.  The Palladio component model for model-driven performance prediction , 2009, J. Syst. Softw..

[12]  Edward D. Lazowska,et al.  Designing an Architecture for Delivering Mobile Information Services to the Rural Developing World , 2006, Seventh IEEE Workshop on Mobile Computing Systems & Applications (WMCSA'06 Supplement).

[13]  Jerome A. Rolia,et al.  A Synthetic Workload Generation Technique for Stress Testing Session-Based Systems , 2006, IEEE Transactions on Software Engineering.

[14]  R. D. van deMei,et al.  Stability and throughput for two-layered queueing networks , 2010 .

[15]  C. Murray Woodside,et al.  Performance modeling from software components , 2004, WOSP '04.

[16]  C. Murray Woodside,et al.  Enhanced Modeling and Solution of Layered Queueing Networks , 2009, IEEE Transactions on Software Engineering.

[17]  C. Murray Woodside,et al.  Solving layered queueing networks of large client-server systems with symmetric replication , 2005, WOSP '05.