1. Introduction

1. DVD audio rend audio dec. Get Full Packet Put Full Packet read dmux dec. sharp enh. main: scalabale buffer  mixer  digit scaler enc. task fqi-1 fqi data transfer digitizer: non-scalable pip: scalable mixer : non-scalable dec scalable task hw scaler enc. writer enc. hierarchical task connection to HW IO disk : non-scalable eqi-1 eqi   Put Empty Packet Get Empty Packet Component Processing function(ci)  arbitrary interleavings channel consistent traces schedule consistent traces priority consistent traces Courtesy of E.F.M. Steffens TN … idle time idle time idle time tinit fq1 fq2 fqN-1 C2 CN … C1 eq1 eq2 eqN-1 Analysis of a Time-driven Chain of Dependent Components M.A. Weffers-Albu 1 , J.J. Lukkien 1 , P.D.V. v.d. Stok 1,2 3. Modeling chain execution 1. Introduction The goal of our work is the prediction and optimization of performance attributes to provide guaranteed and optimized Quality of Service (QoS) for real-time streaming applications. Impose predicates until obtain trace and schedule which specify the system behavior. The approach we took was to provide a characterization of streaming applications execution to determine performance attributes and provide insight into best design practices for optimising these attributes.Performance attributes:Number of Context Switches (NCS), Response Time of tasks and chain (RT), Resources utilization (RU) for CPU and memory. Unique trace ρ, eager scheduleeager QoS Requirement – CN executes always strictly at rate TN. First step solution - rate of production higher than rate of consumption for packets in fqN-1: PRkN-1. TN, k  N The systems we analyseare streaming applications using the TriMedia Streaming Software Architecture (TSSA) and executing on a TriMedia device. Stable Phase Theorem - Provided that PRkN-1. TN , k  N, the pipeline system assumes a periodic behavior after a finite initial phase.The complete behavior is characterized the unique trace: ρ= tinit (inc(i) fqN-1? eqN? cN eqN-1! tL fqN ! d(i *TN)) ω. tinit – trace recording the initial phaseof the system execution. tstable– stable phase: (inc(i) fqN-1? eqN? cN eqN-1! tL fqN ! d(i *TN)) ω tL – subtrace recording the interleaved execution of C1..CN-1. 2. TriMedia Streaming Software Architecture • A TSSA media processing application - graph: • Nodes: - software components • Edges: - finite buffers (queues) that transport the data stream from one component to the next component in the graph. Time-driven component Data-driven components Typical program of component Ci : j=0; while (true) do {inc(j);  receive( fqi-1, p);  receive( eqi, q);  process_func_ci(p, q);  send( eqi-1, p);  send( fqi, q); delay( j*TN ); } • Corollaries • Given the computation times of components actions, ρ andeagercan be calculated at design time. • Hence NCS, task and chain RT, RU for CPU and memory can also be calculated. • Minimum Queue Capacity: 1 • CN drives execution in the same way as a minimum priority data-driven component in a chain composed of only data-driven components. • Minimum NCS at Stable Phase: P(C1)<…<P(CN-1) and Cap(fqi)=2, i < N-1. • Chain RT cannot be optimized 4. Conclusions Express behavior as traces: Tr(Ci): { (fqi-1?, eqi?, ci, eqi-1!, fqi!) }, Tr(CN): {j = 0 (inc(j) fqN-1? eqN? cN eqN-1! fqN! d(j *TN)).}. • Results • Characterization of streaming applications execution • Prediction and optimisation of performance quality attributes. Affiliation 1) Eindhoven University of Technology Department of Mathematics and Computer Science HG 6.57, P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands 2) Philips Research NatLab Prof. Holstlaan 4, 5656AA Eindhoven, The Netherlands About the Author Alina Weffers-Albu received her P.D.Eng. in Software Technology from the Department of Mathematics and Computer Science of the Eindhoven University of Technology (TU/e) in 2003. In July 2003 Alina started a Ph.D. project within the SAN group of the same department, in collaboration with Philips Research Laboratories Eindhoven.