A Hierarchical Scheduling Model for Dynamic Soft-Realtime System

We present a new hierarchical approximation and scheduling approach for applications and tasks with multiple modes on a single processor. Our model allows for a temporal and spatial distribution of the feasibility problem for a variable set of tasks with non-deterministic and fluctuating costs at runtime. In case of overloads an optimal degradation strategy selects one of several application modes or even temporarily deactivates applications. Hence, transient and permanent bottlenecks can be overcome with an optimal system quality, which is dynamically decided. This paper gives the first comprehensive and complete overview of all aspects of our research, including a novel CBS concept to confine entire applications, an evaluation of our system by using a video-on-demand application, an outline for adding further resource dimension, and aspects of our protoype implementation based on RTSJ.

[1]  A. Gregg An integration. , 1953, Journal of the Mount Sinai Hospital, New York.

[2]  Lothar Thiele,et al.  Approximate schedulability analysis , 2002, 23rd IEEE Real-Time Systems Symposium, 2002. RTSS 2002..

[3]  Luca Abeni,et al.  Deadline scheduling in the Linux kernel , 2016, Softw. Pract. Exp..

[4]  Giuseppe Lipari,et al.  Elastic Scheduling for Flexible Workload Management , 2002, IEEE Trans. Computers.

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

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

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

[8]  Tommaso Cucinotta,et al.  On the Integration of Application Level and Resource Level QoS Control for Real-Time Applications , 2010, IEEE Transactions on Industrial Informatics.

[9]  Warren B. Powell,et al.  What you should know about approximate dynamic programming , 2009, Naval Research Logistics (NRL).

[10]  Andy J. Wellings,et al.  Getting more flexible scheduling in the RTSJ , 2006, Ninth IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC'06).

[11]  Jitender S. Deogun,et al.  Real-time scheduling of divisible loads in cluster computing environments , 2010, J. Parallel Distributed Comput..

[12]  James Gosling,et al.  The Real-Time Specification for Java , 2000, Computer.

[13]  Dennis Shasha,et al.  Skip-Over: algorithms and complexity for overloaded systems that allow skips , 1995, Proceedings 16th IEEE Real-Time Systems Symposium.

[14]  Riccardo Bettati,et al.  Imprecise computations , 1994, Proc. IEEE.

[15]  Andy J. Wellings,et al.  A framework for flexible scheduling in the RTSJ , 2010, TECS.

[16]  Sanjoy K. Baruah,et al.  Preemptively scheduling hard-real-time sporadic tasks on one processor , 1990, [1990] Proceedings 11th Real-Time Systems Symposium.

[17]  Giuseppe Lipari,et al.  Soft Real-Time Systems: Predictability vs. Efficiency , 2010 .

[18]  Martin Lukasiewycz,et al.  Embedded systems and software challenges in electric vehicles , 2012, 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE).

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

[20]  Jay K. Strosnider,et al.  The Deferrable Server Algorithm for Enhanced Aperiodic Responsiveness in Hard Real-Time Environments , 1987, IEEE Trans. Computers.