QoS Control for Pipelines of Tasks Using Multiple Resources

We consider soft real-time applications organized as pipelines of tasks using resources of different type (communication, computation, and storage). The applications are assumed to be periodically triggered and the different tasks communicate by unidirectional buffers. The problem we cope with is how to effectively share the resources so that some specified Quality of Service (QoS) requirements are met. The QoS considered here is tightly related to the end-to-end temporal behavior of the application. To compensate for time-varying resource requirements, we advocate a distributed control approach whereby the scheduling parameters of each task are tuned depending on the temporal behavior of the application measured by appropriate sensors. The use of real-time scheduling strategies enables a mathematically safe control design in which the QoS requirements are translated into control goals, and formal proofs are provided on the ability of the controller to fulfil these goals. We also offer extensive simulations that validate the approach for multimedia applications.

[1]  Steve Goddard,et al.  Managing Latency and Buffer Requirements in Processing Graph Chains , 2001, Comput. J..

[2]  Tommaso Cucinotta,et al.  QoS Management Through Adaptive Reservations , 2005, Real-Time Systems.

[3]  Tommaso Cucinotta.  Access Control for Adaptive Reservations on Multi-User Systems , 2008, 2008 IEEE Real-Time and Embedded Technology and Applications Symposium.

[4]  Daniel P. Siewiorek,et al.  Practical solutions for QoS-based resource allocation problems , 1998, Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279).

[5]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[6]  Calton Pu,et al.  Control and modeling issues in computer operating systems: resource management for real-rate computer applications , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[7]  Jonathan Walpole,et al.  Real-rate scheduling , 2004, Proceedings. RTAS 2004. 10th IEEE Real-Time and Embedded Technology and Applications Symposium, 2004..

[8]  Paolo Valente,et al.  Hybrid: achieving deterministic fairness and high throughput in disk scheduling , 2004 .

[9]  Jonathan Walpole,et al.  Analysis of a reservation-based feedback scheduler , 2002, 23rd IEEE Real-Time Systems Symposium, 2002. RTSS 2002..

[10]  Tommaso Cucinotta,et al.  AQuoSA—adaptive quality of service architecture , 2009, Softw. Pract. Exp..

[11]  Douglas C. Schmidt,et al.  Integrated Adaptive QoS Management in Middleware: A Case Study , 2004, Proceedings. RTAS 2004. 10th IEEE Real-Time and Embedded Technology and Applications Symposium, 2004..

[12]  Giorgio C. Buttazzo,et al.  Integrating multimedia applications in hard real-time systems , 1998, Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279).

[13]  Alan Burns,et al.  Data Consistency in Hard Real-Time Systems , 1995, Informatica.

[14]  Giorgio C. Buttazzo,et al.  Adaptive bandwidth reservation for multimedia computing , 1999, Proceedings Sixth International Conference on Real-Time Computing Systems and Applications. RTCSA'99 (Cat. No.PR00306).

[15]  Edward A. Lee,et al.  Software Synthesis from Dataflow Graphs , 1996 .

[16]  Douglas C. Schmidt,et al.  Integration of QoS-Enabled Distributed Object Computing Middleware for Developing Next-Generation Distributed Application , 2001, LCTES/OM.

[17]  John P. Lehoczky,et al.  Practical Solutions for QoS-Based Resource Allocation , 1998, RTSS 1998.

[18]  Michael Roitzsch,et al.  Principles for the Prediction of Video Decoding Times Applied to MPEG-1/2 and MPEG-4 Part 2 Video , 2006, 2006 27th IEEE International Real-Time Systems Symposium (RTSS'06).

[19]  Sanjoy K. Baruah,et al.  Proportionate progress: a notion of fairness in resource allocation , 1993, STOC '93.

[20]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[21]  Chenyang Lu,et al.  Feedback utilization control in distributed real-time systems with end-to-end tasks , 2005, IEEE Transactions on Parallel and Distributed Systems.

[22]  Tommaso Cucinotta,et al.  Feedback Scheduling for Pipelines of Tasks , 2007, HSCC.

[23]  Elisa Bertino,et al.  PARALLEL AND DISTRIBUTED SYSTEMS , 2010 .

[24]  Tommaso Cucinotta,et al.  AQuoSA—adaptive quality of service architecture , 2009 .

[25]  Edward A. Lee,et al.  Advances in the dataflow computational model , 1999, Parallel Comput..

[26]  Richard Gerber,et al.  Guaranteeing Real-Time Requirements With Resource-Based Calibration of Periodic Processes , 1995, IEEE Trans. Software Eng..

[27]  G. Calafiore,et al.  Interval predictors for unknown dynamical systems: an assessment of reliability , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[28]  Antonio Bicchi,et al.  Quality of service control in soft real-time applications , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[29]  Richard Gerber,et al.  Parametric Design Synthesis of Distributed Embedded Systems , 2000, IEEE Trans. Computers.

[30]  Sang Hyuk Son,et al.  Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms* , 2001, Real-Time Systems.

[31]  Shuichi Oikawa,et al.  Resource kernels: a resource-centric approach to real-time and multimedia systems , 2001, Electronic Imaging.

[32]  Karl-Erik Årzén,et al.  Feedback–Feedforward Scheduling of Control Tasks , 2002, Real-Time Systems.

[33]  Hussein M. Abdel-Wahab,et al.  A proportional share resource allocation algorithm for real-time, time-shared systems , 1996, 17th IEEE Real-Time Systems Symposium.

[34]  Douglas C. Schmidt,et al.  Hierarchical control of multiple resources in distributed real-time and embedded systems , 2006, 18th Euromicro Conference on Real-Time Systems (ECRTS'06).

[35]  Chenyang Lu,et al.  ControlWare: a middleware architecture for feedback control of software performance , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[36]  John Regehr,et al.  Augmented CPU reservations: towards predictable execution on general-purpose operating systems , 2001, Proceedings Seventh IEEE Real-Time Technology and Applications Symposium.

[37]  John P. Lehoczky,et al.  Integrated resource management and scheduling with multi-resource constraints , 2004, 25th IEEE International Real-Time Systems Symposium.

[38]  Eric Eide,et al.  Dynamic CPU management for real-time, middleware-based systems , 2004, Proceedings. RTAS 2004. 10th IEEE Real-Time and Embedded Technology and Applications Symposium, 2004..

[39]  Calton Pu,et al.  A feedback-driven proportion allocator for real-rate scheduling , 1999, OSDI '99.

[40]  Erol Gelenbe,et al.  Self-aware networks and QoS , 2004, Proceedings of the IEEE.

[41]  Franco Blanchini,et al.  Set invariance in control , 1999, Autom..

[42]  Yukikazu Nakamoto,et al.  Adaptive Resource Allocation Control for Fair QoS Management , 2007, IEEE Transactions on Computers.